Full-Stack & AI Developer | Next.js, AI Agents
- $50k+
- Earned
- 12x
- Hired
- 5.0
- Rating
- 277
- Followers
Full-Stack & AI Developer | Next.js, AI Agents
Full-Stack & AI Developer | Next.js, AI Agents
- $25k+
- Earned
- 9x
- Hired
- 5.0
- Rating
- 61
- Followers
Full-Stack & AI Developer | Next.js, AI Agents
Founding Engineer & Team Lead | Full-Stack, AI & Web3
- $1k+
- Earned
- 1x
- Hired
- 5.0
- Rating
- 24
- Followers
Founding Engineer & Team Lead | Full-Stack, AI & Web3
Production-ready AI agents and SaaS built to scale reliably.
- $50k+
- Earned
- 4x
- Hired
- 132
- Followers
Production-ready AI agents and SaaS built to scale reliably.
AutoStudio Pro — AI-Powered Vehicle Photography SaaS
Transforming ordinary car photos into professional, dealership-ready images using AI.
The Challenge
Car dealerships rely on high-quality vehicle photos to attract buyers, but professional photography is expensive, time-consuming, and difficult to scale.
Inconsistent lighting, cluttered backgrounds, and poor framing often make vehicle listings look less appealing, especially for dealerships managing large inventories.
The goal was to create an automated solution that could produce consistent, professional-looking vehicle images without traditional studio photography.
Our Solution
We developed AutoStudio Pro, an AI-powered SaaS platform built with Next.js and Google Nano Banana Pro.
The platform enables dealerships to upload vehicle photos and transform them into polished marketing assets through an automated AI workflow.
Key features included:
• AI Background Replacement: Transform ordinary dealership photos into professional studio-style images.
• Image Enhancement: Improve vehicle presentation through automated framing, polishing, and visual consistency.
• Multi-Agent AI Workflow: Coordinate image-processing tasks to produce consistent results.
• Custom Branding: Apply dealership watermarks for branded vehicle listings.
• Simple Upload Experience: Enable users to generate professional images without editing expertise.
The Results
AutoStudio Pro simplified vehicle photography and content preparation for dealerships.
• Turned ordinary vehicle photos into studio-quality listing assets.
• Reduced the need for manual photo editing and professional studio shoots.
• Created more consistent, branded visuals across vehicle inventories.
• Streamlined image preparation through an automated SaaS workflow.
Tech Stack
Next.js · Google Nano Banana Pro · Generative AI · Image Processing · Multi-Agent AI Workflows
Key Takeaway
AutoStudio Pro demonstrates how AI can transform a time-consuming business process into a scalable, user-friendly product. By combining generative AI with a streamlined SaaS experience, we made professional vehicle imagery more accessible to dealerships.
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433
Blue 360 Media — E-Commerce Modernization with React & Salesforce
Transforming a legacy legal publishing storefront into a modern, reliable, and CRM-connected e-commerce platform.
The Challenge
Blue 360 Media provides specialized legal publications for law enforcement professionals. Its existing Laravel-based platform made it difficult for customers to find jurisdiction-specific products, while inefficient Salesforce integrations and complex checkout workflows created operational challenges.
The goal was to simplify product discovery, modernize the shopping experience, and improve the reliability of backend integrations.
Our Solution
We developed a modern e-commerce platform using React and AWS Amplify, focusing on usability, performance, and seamless integrations.
Key improvements included:
• Smarter Product Search: Improved catalog navigation with state-specific and jurisdiction-based filtering.
• Salesforce Integration: Implemented paginated API calls for more efficient product data retrieval and integrated shipping information.
• Streamlined Checkout: Built reliable cart, shipping, and promotional code workflows.
• Payment Integration: Integrated Authorize.Net (http://Authorize.Net) and Braintree for payment processing.
• Modern Architecture: Replaced legacy frontend workflows with a more flexible React-based experience.
The Results
The modernization delivered a smoother shopping experience and more efficient commerce operations.
• Simplified product discovery for specialized legal publications.
• Reduced unnecessary data retrieval through Salesforce API pagination.
• Improved checkout reliability and payment workflows.
• Strengthened integration between the storefront and Salesforce.
Tech Stack
React · AWS Amplify · Salesforce · Authorize.Net (http://Authorize.Net) · Braintree · REST APIs
Key Takeaway
Blue 360 Media's transformation went beyond a visual redesign. By modernizing its architecture and connecting critical commerce systems, we delivered a more dependable e-commerce experience tailored to the needs of legal and law enforcement customers.
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422
Multi-Tenant CMS and Website Platform for Multi-Brand Organizations
Description:
A production-ready multi-site CMS that lets one team run many branded websites from a single admin. It includes tenant isolation, a block-based page builder, automated WordPress migration, AI-assisted page building, and AWS infrastructure.
The challenge:
The client needed to manage many separate websites from one CMS. Each site had its own domain, theme, content and users. The platform had to do four things at once:
• keep tenants fully isolated from each other
• give editors reusable building blocks instead of one-off pages
• make moving existing WordPress sites in fast instead of a manual rebuild
• run reliably in production, not just work as a prototype
What I built:
1. Multi-tenant Payload CMS.
Tenant isolation, per-tenant domains and themes, and role-based access control, so every brand stays separate within one admin.
2. Block-based page builder.
Reusable content blocks, plus blog and form support, so editors build pages without a developer.
3. WordPress migration automation.
Existing sites are imported automatically, and content import and export runs through structured workflows.
4. AI-assisted page and component generation.
Claude turns screenshots and layouts into ready-to-use pages and components, which speeds up new-site setup.
5. Performance and SEO.
Static site generation with Next.js, semantic HTML, and Tailwind CSS for fast, search-friendly pages.
6. Production AWS infrastructure.
Defined in CloudFormation with RDS (PostgreSQL), S3, CloudFront, ACM, SES, Lambda and WAF, with separate staging and production environments.
7. Production-readiness work.
Improvements to admin UX, data workflows, environments and infrastructure security, plus Playwright in the tooling.
The outcome:
The product went from concept to a production-ready multi-site platform. One team can now launch and run many branded websites from one place. Each site is isolated, fast and SEO-friendly. Migrations and new builds take a fraction of the manual effort.
Key takeaway:
A white-label CMS only scales when flexible content tooling comes with real production engineering: isolation, migrations, infrastructure and security.
Good fit for: agencies, franchises, multi-brand organizations, and SaaS teams managing many websites from one platform.
Skills and tools used:
Next.js · Payload CMS · PostgreSQL · Tailwind CSS · AWS · CloudFormation · CloudFront · RDS · S3 · Lambda · SES · WAF · Playwright · Claude API · Multi-Tenant SaaS · Headless CMS · WordPress Migration · SEO
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From Instinct to Infrastructure: How Athliq Went From Trainer's Notebook to Production Performance Platform
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The Person Who Understood the Problem Before Anyone Else Did
Marcus had been a strength and conditioning coach for eleven years. He'd worked with professional football squads, Olympic track athletes, and NCAA programs. He knew exactly what was wrong with how performance data was managed, not because someone told him, but because he'd lived inside the problem every day.
Every morning, he'd open four browser tabs, a spreadsheet, and a WhatsApp thread just to answer one question: Is this athlete safe to train today?
HRV from one app. Sleep data from another. Yesterday's load from a Google Sheet. Wellness check-ins in a form nobody filled out consistently. Injury notes in a physio's personal folder. The picture was always incomplete, not because the data didn't exist, but because no system was designed to assemble it.
He wasn't guessing the problem. He was the problem's daily victim.
So he built something.
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The First Version Had Real Value
What Marcus and a developer friend put together in six weeks was genuinely useful. A React frontend. Static JSON files simulating the data feeds he wished he had. A morning readiness dashboard showing each athlete's status: green, amber, red. ACWR calculations. A training plan view. A return-to-play protocol tracker.
It looked like a real platform. It felt like one. When he demoed it to his performance director, the response was immediate: "This is what we've been trying to build for two years."
The prototype surfaced something important. The problem wasn't that nobody had the data. The problem was nobody had designed the right lens to look at it through. Marcus had. His system organized information around the questions coaches actually ask, not the questions software vendors assume they ask.
The initial build worked. For one squad. In static conditions. With fake data.
And that was exactly the point where it started to matter, and exactly the point where its limitations became unavoidable.
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What Started Breaking
The prototype had no backend. Data was hardcoded. Every "insight" was pre-written. The readiness scores didn't calculate; they were authored. When a second sport scientist saw the demo and said, "Can we plug in our GPS data?" there was no honest answer that didn't involve a complete rebuild.
More specifically:
The data layer was decorative. The JSON files looked real but required manual authoring for every scenario. There was no pipeline, no ingestion, no validation. Any live deployment would mean the dashboard showed stale or fabricated numbers, which in a performance context is worse than showing nothing at all.
The AI insights were static strings. Every "AI-generated" recommendation was hardcoded text. In the prototype, this was fine. It demonstrated the concept. In production, it would mean the same insight appearing for every athlete regardless of their actual state, silently eroding clinician trust.
The system had no concept of time. Training load calculations, ACWR ratios, wellness trends, all of these are temporal by definition. The prototype rendered them as snapshots. A real system needed rolling windows, historical comparison, and the ability to detect change over time.
There was no role isolation. The role switcher in the UI was cosmetic. A physio and a performance director seeing the same underlying data with a CSS class change was not access control, it was theater.
Nothing would survive a real integration. Real wearables return messy, incomplete, delayed data. The system had no error handling, no retry logic, no fallback states. The first real data feed would have broken the UI in ways that were invisible until they were catastrophic.
The system was not wrong. It was early.
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Why They Didn't Just Fix It Themselves
Marcus understood sport science. His developer understood React. Neither of them had built a production data system before, and that gap is not a skills deficiency, it is a domain specialization.
What they needed was not someone to rewrite their frontend. The frontend was actually good. The visual hierarchy was sharp, the domain terminology was accurate, the workflows reflected how coaches genuinely think. That institutional knowledge was irreplaceable and not to be discarded.
What they needed was:
• A real-time data layer that could ingest from multiple sources reliably
• A calculation engine that could run ACWR, load zone distributions, and readiness scoring against live data
• A structured API contract between the front and back end
• Authentication and role-based data scoping that actually enforced access boundaries
• Observability, so when something silently broke, someone would know
The prototype had proven the concept. The job now was to make the concept dependable.
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What We Actually Did
We kept the frontend. Almost entirely. The visual design, the component architecture, the domain-accurate terminology, all of it stayed. We refactored the data layer, not the UI layer. The prototype's greatest strength was its UX fidelity to how coaches actually work, and we had no interest in rebuilding that from scratch.
We built a real data ingestion pipeline. Rather than static JSON, we designed a service layer that could ingest from GPS units, HRV monitors, and wellness form submissions. Each source had its own adapter with validation, normalization, and error handling. Partial data was acceptable; silently wrong data was not.
We replaced static calculations with a live computation engine. ACWR calculations now ran against a rolling 28-day window of actual load data. Readiness scoring pulled from real HRV baselines, not fixed numbers, and recalibrated as an athlete's personal baseline shifted over a training block.
We replaced pre-written AI insights with a rules-based inference layer. Every insight shown in the platform now corresponded to a condition that was evaluated against live data. If Lena Vasquez's ACWR crossed 1.3 and her HRV dropped more than 15% from her 7-day baseline, the injury risk flag was triggered, not because it was hardcoded, but because those conditions were true.
We implemented real role-based access control. A physio sees medical data, injury history, and RTP protocols. A sport scientist sees load analytics and benchmarks. A performance director sees the squad-level overview. A head coach sees readiness and today's session. The frontend already had role-switching built in, we gave it actual enforcement.
We added observability throughout. Every data ingestion event was logged. Every calculation that produced an out-of-range result generated an alert. If a data source stopped sending, the system surfaced a staleness warning rather than silently displaying old numbers as current.
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Tech Stack
React 19 + Vite, Tailwind CSS v3, Recharts, React Router v7, Node.js + Fastify, PostgreSQL + TimescaleDB, Redis, GPS/HRV/sleep data adapters (Catapult, Polar, Garmin), Google Forms/Typeform ingestion, Auth0, row-level security, Sentry, Datadog, Railway/Render, Vercel, Supabase Storage
What Changed
The morning workflow Marcus had been running across four tabs and a spreadsheet now ran in a single view that was populated automatically before he arrived at the training ground.
The difference wasn't just convenience. It was confidence.
When the dashboard flagged an athlete as high-risk, the coaching staff could interrogate why and trust the answer. When a return-to-play progression showed 80% completion, that number reflected actual criteria met against measured data, not a manually updated percentage.
The platform was no longer a demo tool. It was a clinical decision-support system.
For the squads using it, the shift was measurable: fewer reactive injury responses, more consistent load monitoring, and perhaps most importantly, a shared language between coaching staff, physios, and sport scientists built around the same data rather than competing interpretations of separate sources.
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The Part That Rarely Gets Said
Most platforms built in this space start from the software side. Someone builds a data collection tool, adds a dashboard, and then tries to reverse-engineer what coaches actually care about.
Athliq started from the other direction. A domain expert who understood the problem at a professional level built the frame first, and built it correctly. The pain points were real, the workflows were accurate, the terminology was precise.
The engineering work didn't fix a bad idea. It made a good idea survivable.
That distinction matters more than most technical case studies acknowledge. The hardest part of building a platform like this is not the infrastructure. It's knowing which questions to answer. That knowledge was already there.
Our job was to make sure the system could keep answering them reliably, at scale, over time.
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Athliq is now in active deployment across two professional squads and one university performance program. The morning readiness dashboard processes real-time data from four integrated sources and serves role-scoped views to performance directors, sport scientists, physiotherapists, and athletes.
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2.4K
Product Development Partner for Early Startups to Enterprise
- $5k+
- Earned
- 1x
- Hired
- 5.0
- Rating
- 62
- Followers
Product Development Partner for Early Startups to Enterprise
Backend, DevOps & AI Engineer | NestJS, TypeScript, AWS
A good donation flow needs dependable systems behind the screens. For Gabriel SGO, I built the NestJS/PostgreSQL backend for donor and school workflows, with PayPal donation processing, Redis/BullMQ background jobs, Mailgun email, and AWS delivery.
The public experience helps Ohio donors estimate a contribution, choose a school, and understand the tax-credit process. I documented the backend work and real site screens here: https://contra.com/p/Ls6L2o7Y-gabriel-sgo-or-donation-and-school-allocation-backend
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833
Senior Full Stack SaaS & AI Developer | React, Next.js, Node
- 90
- Followers
Senior Full Stack SaaS & AI Developer | React, Next.js, Node
Free SaaS Metrics Calculator — Private, Fast, No Signup
I built a browser-based SaaS Metrics Calculator that helps founders quickly calculate:
• MRR and ARR
• Net Revenue Retention
• Customer Acquisition Cost
• Customer Lifetime Value
• CAC payback period
• MRR growth and 12-month run rate
Everything runs locally in the browser—no account, API, or financial data storage. The tool also explains the formulas and assumptions behind every result.
Built with HTML, CSS and JavaScript, with automatic number formatting and a responsive interface.
Try it free:
https://aisaaswithmustafa.com/tools/saas-metrics-calculator.html
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174
Built an AI job hunter solo because applying to jobs by hand was eating my evenings.
It finds live vacancies, matches each one against my experience, and writes a CV tailored to that specific job. No more sending the same CV to 40 places.
I build AI features into web apps and SaaS products as a full-stack dev. If you're thinking about adding something like this to your product, my DMs are open.
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I think every senior engineer should have at least one live product in production — not a tutorial, not a clone. Something where real business and real money are involved.
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Built JobHunter — an AI job search and application agent.
JobHunter helps users discover relevant opportunities and uses Claude AI to match jobs against their experience and tailor their CV for each position.
The platform combines:
• AI-powered job matching
• Job-specific CV tailoring
• Live job discovery through the JSearch API
• A fast, responsive experience built with Next.js
Instead of sending the same generic CV everywhere, JobHunter helps candidates create a more relevant application for every opportunity.
Built with Next.js, Claude AI
#AI #AIDeveloper #NextJS #WebDevelopment #SaaS
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Product Design, Growth and Developer
Kodsinc CRM: AI-First Revenue Intelligence and Sales Management Platform
I designed Kodsinc CRM as an AI-first revenue operating system for modern B2B sales teams. Rather than treating AI as a chatbot added on top of a conventional CRM, the entire product is designed around intelligent agents that continuously understand customers, monitor opportunities, interpret buying signals, prioritize work, assist sales representatives, forecast revenue, automate repetitive activity, and connect every interaction back to measurable commercial outcomes.
Traditional CRMs are primarily systems of record. Sales representatives enter contacts, update stages, create tasks, and managers review reports after the activity has already happened. Kodsinc CRM is designed to operate differently. It functions simultaneously as a system of record, system of intelligence, and system of action.
The CRM records what has happened, AI analyzes what it means, and specialized agents help determine what should happen next.
This creates a connected operating environment covering the complete revenue lifecycle: lead generation, account intelligence, prospect engagement, sales conversations, deal management, forecasting, invoicing, attribution, website intelligence, SEO performance, AI-search visibility, management reporting, and operational administration.
AI Agent Operating Layer
At the centre of Kodsinc CRM is an agentic intelligence layer that continuously works across the CRM rather than waiting for users to manually request assistance.
The agents share access to approved CRM context including accounts, contacts, deals, emails, activities, website behaviour, buying signals, invoices, campaigns, marketing attribution, AI citations, historical performance, and user-defined business rules.
This allows multiple specialized AI agents to work together.
The Revenue Intelligence Agent continuously watches the overall business. It monitors pipeline creation, deal movement, forecast categories, quota attainment, revenue velocity, lead sources, invoice status, marketing contribution, and AI-influenced pipeline. It surfaces important changes to leadership instead of requiring them to search through multiple dashboards.
The Deal Agent monitors every opportunity individually. It understands the deal stage, stakeholders, communication history, website visits, competitor involvement, days in stage, engagement levels, next steps, and close date. It can identify stalled opportunities, missing decision makers, declining engagement, overdue actions, or unusual risk patterns and recommend the most appropriate next action.
For example, rather than simply showing that a deal is worth $48,000 and currently in Proposal, the agent can identify that the economic buyer has not been engaged recently and recommend arranging a CFO review before the expected close date.
The Account Intelligence Agent maintains a living view of every customer and prospect account. It combines firmographic information, people, open opportunities, invoices, engagement history, website visits, sales activity, technology information, acquisition channels, and relationship strength into one account profile. Sales representatives therefore enter a customer conversation already understanding what that company has been researching and how engaged it has become.
The Conversation Agent assists inside the Inbox. It understands previous messages, the associated account, active opportunity, recent activity, and next steps before producing a response suggestion. The user can review, edit, or send the response, preserving human control while dramatically reducing time spent drafting repetitive communication.
The Outreach Agent supports structured multi-step prospecting sequences across channels such as email, LinkedIn and calls. Rather than evaluating outreach only through sending volume, it watches opens, replies, meetings, step-level conversion and sequence performance. Over time it helps identify which message, channel, audience and sequence structure actually produces qualified conversations.
The Forecasting Agent analyzes the probability of revenue landing during the quarter. It combines closed revenue, Commit, Best Case and Pipeline opportunities with individual deal behaviour and recent movement. Instead of merely adding CRM probabilities together, it produces an AI-predicted landing range and explains the factors that are pushing the forecast upward or downward.
The Growth Intelligence Agent connects sales with digital demand generation. It understands how prospects discovered the company through traditional search, AI answer engines, campaigns, referrals, paid traffic and outbound activities. This means management can move beyond asking, “Where did this lead come from?” and begin asking, “Which source, page, AI citation, campaign or search journey actually influenced this revenue?”
The AI Visibility Agent monitors how the company appears inside ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot and other AI-driven discovery environments. It tracks citations, brand visibility, citation position, sentiment, factual inaccuracies, unanswered high-intent prompts and AI-influenced pipeline.
The Revenue Operations Agent acts as the operational layer across the CRM. It can surface incomplete data, overdue tasks, stale opportunities, missing fields, unassigned records, unusual pipeline behaviour and integration problems. This reduces the administrative burden normally placed on RevOps teams.
Finally, the Finance and Collections Agent connects sales outcomes with invoicing. Once an opportunity becomes revenue, its financial lifecycle remains visible inside the same system. Paid, due, overdue and awaiting invoices can therefore be connected back to the account and opportunity that generated them.
Together these agents transform Kodsinc CRM from a database users maintain into a platform that actively helps operate the revenue function.
Command Center
The Command Center acts as the executive cockpit of Kodsinc CRM.
It immediately presents closed-won revenue, open pipeline, quota attainment, revenue versus target, pipeline distribution, invoice status, AI-search visibility, recent activity and deals expected to close during the current quarter.
Instead of forcing managers to move through several reporting applications, the screen combines commercial performance, pipeline health, cash status and growth intelligence in one view.
The system is designed around exception management. Management does not need to inspect every opportunity manually. AI highlights meaningful changes such as a high-value deal losing momentum, an invoice becoming overdue, an important prospect reopening a proposal, an AI citation suddenly improving, or pipeline coverage falling below acceptable levels.
Every metric is also actionable. Pipeline numbers lead directly into opportunities, forecast values lead into revenue intelligence, invoice metrics lead into billing, and AI visibility metrics lead into the growth intelligence module.
The result is not simply a dashboard showing what happened. It is a control centre for deciding where the team needs to act next.
Intelligent Sales Pipeline
The Pipeline workspace manages every active opportunity visually across stages such as Discovery, Qualified, Demo, Proposal and Negotiation.
Each deal card communicates more than an opportunity name and monetary value. It includes account context, expected close date, time spent in the current stage, forecast category, health score and acquisition source.
This allows managers to understand both the financial value and underlying quality of the pipeline.
Deals can be moved between stages through drag-and-drop interaction. When an opportunity changes stage, associated probability, forecast contribution and pipeline totals can update automatically.
The deeper intelligence comes from AI monitoring the movement itself.
The system can identify:
opportunities spending unusually long periods in a stage;
high-value deals without recent contact;
close dates repeatedly being moved;
missing economic buyers;
falling engagement;
unusually strong intent signals;
opportunities whose stated stage does not match observed behaviour;
deals that should potentially move into Commit or out of the current forecast.
Instead of forcing managers to run weekly pipeline-cleaning exercises manually, Kodsinc CRM makes pipeline hygiene part of the operating system.
Deal Intelligence and Deal 360
Opening an opportunity moves the user into Deal 360.
This screen becomes the complete operating environment for that commercial opportunity.
The CRM displays the stage, amount, probability, forecast category, close date, opportunity type, acquisition source, first-touch source, last-touch influence, time in stage, health indicators, competitors and account owner.
More importantly, it combines traditionally disconnected events into a single chronological activity timeline.
Emails, calls, meetings, notes and stage changes can appear alongside website visits, proposal engagement and AI-search citations.
For example, a representative could see that a prospect:
first discovered the company through Google,
attended a webinar,
visited the pricing page three times,
opened a proposal,
appeared through a Perplexity citation,
and later replied to the sales representative.
That sequence gives the seller a far more complete understanding of buying behaviour than an ordinary CRM activity log.
The Deal Agent continuously analyzes this information and can recommend a next-best action.
A deal health score becomes explainable rather than decorative. The system can show which behaviours are strengthening or weakening the opportunity so representatives understand why the AI considers something risky.
Accounts and Contacts
The Accounts and Contacts workspace provides the master customer database.
Accounts include information such as company size, industry, geography, owner, annual recurring revenue, health, active opportunities, acquisition channel and recent activity.
Contacts contain relationship-level information including title, account, deal role, qualification score, source, engagement state and last interaction.
Contacts can be classified as Champions, Decision Makers, Influencers, Blockers or Users, giving sales teams visibility into the political structure of an opportunity.
AI scoring can prioritize people and accounts using explicit evidence rather than opaque numerical scoring.
For example, a high prospect score could be explained through factors such as seniority, company size, webinar attendance, repeated pricing-page visits or direct email engagement.
This allows sellers to understand why someone is important rather than simply being told that a lead has a score of 91.
Account 360
Account 360 combines every relevant commercial signal for a company.
Sales representatives can see annual revenue contribution, account health, open pipeline, lifetime value, company characteristics, technology stack, contacts, opportunities, invoices and recent engagement.
An Engagement view connects CRM activity with web behaviour.
Instead of website analytics remaining anonymous inside a marketing product, known-account behaviour can be attached to the customer record.
The account team might therefore see that a company has recently visited the pricing page fourteen times, viewed a forecasting product page nine times and visited a competitor comparison page six times.
These behaviours become valuable buying signals.
The Account Intelligence Agent can then summarize what is happening across the account and surface meaningful changes before a seller begins their next conversation.
Unified Inbox and Activity Management
The Inbox combines customer conversations and sales work in one place.
Email, LinkedIn-compatible messages and other supported communication channels can appear in a unified conversation view.
Users can see unread messages, tasks due today, overdue activities and conversations connected to specific accounts or opportunities.
Opening a conversation simultaneously displays the message thread and CRM context.
Instead of switching from email to CRM to account records to opportunity notes, the seller can immediately see who the person is, which company they belong to, what opportunity is active and what the next step should be.
The AI Conversation Agent can draft replies using that complete context.
Crucially, AI-generated responses are treated as recommendations rather than autonomous truth. Users can review and modify the generated content before sending it.
Task management is also integrated into the same workflow. Completing a task updates activity records and reduces outstanding workload immediately.
This creates a daily workspace in which sellers can operate without repeatedly moving between CRM, email, task management and prospecting applications.
AI-Assisted Outreach
The Outreach workspace manages multi-step sales sequences.
Teams can create structured cadences containing combinations of email, waiting periods, LinkedIn activity and calls.
Each sequence tracks enrolment, open rates, response rates, meetings booked and performance at individual steps.
This makes the system useful for both SDR execution and management optimization.
The Outreach Agent can analyze which steps produce engagement and which steps cause prospects to disappear from the sequence.
Templates can then be evaluated using actual downstream outcomes instead of only open rates.
The objective is not simply “send more messages.” The objective is to continuously improve which people are contacted, what is communicated, when communication occurs and which channel is most appropriate.
Over time, the platform can use this information to recommend sequence structures and messaging based on prospect segment, seniority, industry, previous engagement and buying signals.
Revenue Forecasting
The Forecast workspace turns pipeline data into a forward-looking revenue model.
Management can see Closed Won, Commit, Best Case, Pipeline and projected quarter-end revenue compared with quota.
A traditional CRM forecast often depends heavily on seller-selected probabilities.
Kodsinc CRM adds a behavioural intelligence layer.
The Forecasting Agent can consider opportunity movement, engagement patterns, close-date changes, stakeholder coverage, historical conversion, opportunity health and recent events before producing an expected landing range.
The output therefore includes both a prediction and confidence interval.
More importantly, the system explains why the prediction changed.
Management might see positive drivers such as three opportunities moving into Commit, alongside negative drivers such as a major deal slipping four days.
Forecasting therefore becomes a management conversation supported by evidence rather than a single unexplained number.
Individual sales representatives can also be expanded to inspect the opportunities contributing to their forecast, making the model auditable.
Billing, Invoicing and Deal-to-Cash Management
Kodsinc CRM continues tracking a customer after the opportunity closes.
The Invoices and Billing workspace displays paid revenue, amounts due, contracted balance, overdue invoices, invoice status, recent transactions and available payment methods.
Invoices remain linked to their original accounts and deals.
This provides a true deal-to-cash workflow.
A sales leader can therefore follow an opportunity from pipeline creation through close, invoice generation and eventual payment without losing context between CRM and finance systems.
New invoices can be generated directly from the CRM. Existing invoices can be previewed, reminders can be issued, and payment status changes can update financial metrics.
Overdue invoices can also become operational signals.
Instead of Finance discovering a problem separately, the account owner can immediately see that an important customer has an overdue balance and adjust the commercial conversation accordingly.
Web Analytics
The Web Analytics module brings acquisition behaviour directly into the revenue platform.
It tracks sessions, users, engagement time, bounce rate, conversion rates, traffic channels, landing pages, devices, geography and the complete marketing-to-revenue funnel.
A real-time visitor view provides current website activity, while historical visualizations explain longer-term trends.
The important difference is that web analytics are not isolated from CRM outcomes.
Traffic channels can be connected to leads, opportunities and closed-won revenue.
AI answer engines are specifically tracked as their own acquisition channel, allowing teams to compare traffic and conversion from services such as ChatGPT, Perplexity, Gemini and Copilot against traditional channels.
This helps answer a commercially important question:
Which digital discovery environments are actually creating customers?
SEO Intelligence
The SEO workspace monitors conventional search visibility.
It tracks organic traffic, impressions, CTR, average ranking position, domain strength, keyword movement, referring domains, backlinks, indexed pages and Core Web Vitals.
Keywords can be connected with landing pages and AI Overview presence.
Technical SEO issues are also surfaced directly in the CRM environment.
Instead of presenting technical problems without commercial context, AI can generate remediation guidance and help teams understand which issues may affect high-value pages or revenue-producing search journeys.
This connects the work of growth teams with the same commercial dataset used by Sales and RevOps.
AI Visibility: AEO, GEO and LLMO
AI Visibility is one of the defining capabilities of Kodsinc CRM.
As buyers increasingly ask AI systems for recommendations rather than relying entirely on traditional search engines, the platform monitors how the company appears inside those AI-generated answers.
The module covers three complementary disciplines.
AEO — Answer Engine Optimization measures visibility in answer-oriented search experiences such as featured snippets, People Also Ask results, structured answers and schema-driven results.
GEO — Generative Engine Optimization measures brand presence and citation position inside generative search environments such as Google AI Overviews, Perplexity, ChatGPT Search, Copilot and Gemini.
LLMO — Large Language Model Optimization measures how often a brand appears within LLM-generated answers, its share of model voice, citation frequency, sentiment and factual accuracy.
Kodsinc CRM can therefore track questions that prospective customers are asking AI systems and determine whether the company appears in the answer.
It can also compare visibility across different engines.
A GEO matrix can show which queries receive strong citations and which engines fail to mention the company.
The platform can identify high-intent prompt gaps where competitors appear but the company does not.
AI can then create a content brief designed to address the missing information.
The platform also detects factual errors.
For example, if an AI engine incorrectly describes pricing, lists an obsolete integration or attributes a competitor capability to the company, Kodsinc CRM creates an alert.
The AI Visibility Agent can draft a correction strategy, supporting content or structured-data recommendation for review.
This turns AI-search visibility into an operational business process rather than an experimental marketing metric.
Revenue Attribution
The Attribution workspace connects marketing investment with commercial results.
Users can analyze first-touch, last-touch, linear and time-decay attribution models.
Channels can be evaluated against spend, sessions, leads, pipeline generated, closed-won revenue, CAC and return on investment.
The platform also visualizes actual customer journeys.
A successful opportunity might follow a journey such as:
Organic Search → Pricing Page → Perplexity Citation → Demo → Closed Won.
This is extremely valuable because modern B2B buying journeys are rarely attributable to a single click.
Kodsinc CRM preserves that multi-touch context while still allowing management to understand which channels materially influence revenue.
AI answer engines therefore become measurable alongside outbound, paid advertising, events, referrals and conventional organic search.
Reporting and Revenue Analytics
The Reports workspace gives leadership and RevOps teams a configurable analysis environment.
Reports can combine Sales, Growth and Finance data because the underlying platform works from one connected revenue model.
Users can create reports around metrics such as AI-sourced pipeline, quarter performance, keyword movement, accounts receivable ageing, pipeline velocity or campaign ROI.
Metrics, dimensions, filters and visualization formats can be configured directly.
Reports can also be scheduled for recurring distribution.
Instead of rebuilding management packs manually every week, teams can create persistent reporting views that remain connected to live CRM data.
The AI layer can eventually extend this into narrative reporting by explaining material changes such as:
“Pipeline increased 11% this week, primarily due to three AI-sourced opportunities entering Proposal, while forecast risk increased because two enterprise opportunities moved their expected close dates into Q4.”
This changes reporting from static visualization into decision support.
Settings, Teams, Permissions and Integrations
The Settings area manages the operating environment behind the CRM.
Administrators can manage workspace configuration, users, roles, permissions, pipeline stages, custom fields, notifications, integrations, billing and API access.
Role-based access supports administrators, managers, account executives, SDRs and viewers.
The platform is designed to integrate with systems such as Gmail, Outlook, Slack, HubSpot, Stripe, Google Search Console, GA4, Ahrefs, Perplexity, OpenAI, Segment and Zapier.
These connections allow the CRM to become the intelligence layer across the broader revenue technology stack rather than replacing every specialized system.
The RevOps Agent can monitor these connections and identify issues such as missing data, failed synchronization or incomplete records.
Cross-Functional Automation
One of the strongest aspects of Kodsinc CRM is that features are not designed as isolated pages.
The application supports complete operational journeys.
An AI citation can generate referral traffic. That visitor can become a lead. The lead can be qualified, converted into an account and contact, and eventually become an opportunity. The opportunity can progress through the pipeline, contribute to the forecast, become closed revenue, generate an invoice and eventually appear in attribution reporting.
Likewise, an incorrect AI-generated claim can become a visibility alert. AI can draft a remediation strategy, create a task, assign it to the appropriate team, track the resulting SEO or AI visibility improvement and later connect recovered traffic with pipeline.
A sales opportunity can move into Negotiation, become Closed Won, automatically affect revenue reporting, produce an invoice and update forecast performance.
Quarterly management reviews can move seamlessly from executive KPIs into forecast inspection, individual seller performance, attribution analysis and board reporting.
This connected architecture is what makes Kodsinc CRM fundamentally different from a collection of dashboards.
Human-Controlled AI
Although AI is deeply embedded throughout the product, the system is designed around controlled automation.
Generated responses, recommendations, content corrections and actions remain visible to the user.
AI-generated material can be reviewed, accepted, modified or dismissed.
Important actions can be logged through an audit trail.
Role permissions, approval rules, sending limits and operational guardrails can govern what an agent is allowed to perform automatically.
The objective is not to remove people from the sales process.
The objective is to remove unnecessary administrative work so people can spend more time on judgment, relationships, negotiation and closing business.
AI-First CRM Architecture
The conceptual architecture can be understood as five connected layers.
At the bottom is the Revenue Data Layer, containing accounts, contacts, leads, deals, activities, communications, campaigns, invoices, website behaviour, search data, AI citations and revenue events.
Above it is the Intelligence Layer, where AI models analyze relationships, score opportunities, detect signals, summarize information, forecast outcomes and identify anomalies.
Above that sits the Agent Layer, containing specialized agents responsible for deals, accounts, outreach, conversations, forecasting, visibility, RevOps and revenue operations.
The fourth layer is the Action Layer, where agents can draft communication, recommend tasks, prepare reports, create briefs, update records, trigger workflows and surface notifications within defined permissions.
Finally, the Experience Layer exposes everything through the Command Center, Pipeline, Account 360, Inbox, Outreach, Forecast, Billing, Growth Intelligence, Reporting and Settings interfaces.
Because every layer operates on the same revenue graph, intelligence generated in one part of the product can immediately become useful somewhere else.
The Result
Kodsinc CRM is designed to move CRM software beyond data entry and pipeline administration.
It gives sales representatives a clearer understanding of every prospect and opportunity.
It gives managers continuous visibility into pipeline quality, seller performance and forecast risk.
It gives SDR teams intelligent outreach and prioritized conversations.
It gives RevOps a connected system for data quality, workflow management, automation and reporting.
It gives Growth teams visibility into SEO, AI discovery, attribution and digital behaviour.
It gives Finance a direct connection between deals, invoices and collected revenue.
And it gives leadership one environment for understanding where revenue came from, what is happening now, what is likely to happen next and where the organization should act.
The result is an AI-first CRM that does not merely store the history of the customer relationship, it continuously helps the revenue team decide and execute the next best action.
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GPUStore: A Modern GPU Shopping and Discovery App
I designed GPUStore to make buying graphics cards feel less like decoding a spec sheet and more like exploring a well-organized catalog. Instead of overwhelming users with dense tables and inflated resale listings, I structured the product around what PC builders actually ask: which generation is this, what did it originally cost, and what will it do for me?
The visual identity pairs a dark charcoal interface with electric lime green accents. The green guides interaction. It marks active filter tabs, the Shop Now and Add to Cart buttons, the cart badge, saved hearts, and the NVIDIA brand label. Dark cards and high-contrast product renders give each card a premium, showroom feel while keeping specs and prices easy to read.
The home screen opens with a personal greeting, a search bar with a real example query ("RTX 4090"), and quick category chips for All, NVIDIA, AMD, Accessories, and Builds. A bold RTX 50 Series banner promotes new releases, followed by a "Featured GPUs" section. Each card shows the architecture, release year, launch price, and a one-tap cart button.
The All GPUs screen turns browsing into a timeline. Segmented filters (All, GTX, RTX, By Year) sit above a 2016 to 2026 range slider, so users can explore generations and their original launch prices (MSRP). Seeing the GTX 1080 at $599 next to the RTX 4090 at $1,599 makes a decade of pricing history readable at a glance.
The product detail screen prioritizes clarity. An image carousel with wishlist and share actions leads into the architecture and release date, then a dedicated MSRP panel. A compact Key Specifications table follows (Ampere architecture, 8,704 CUDA cores, 10 GB GDDR6X, 320-bit memory bus, 320 W TDP). A sticky quantity stepper and Add to Cart button stay within thumb reach. A short note clarifies that launch price shows original MSRP and actual market prices may vary.
The profile screen gives users a personal hub with their saved items, orders, builds, and member-since year. Wishlist, Orders, and Settings tabs keep everything in one place, and saved cards show the date each item was added. Across the app, a five-tab bottom navigation (Home, Browse, Wishlist, Orders, Account) keeps the structure predictable.
Frontend
I built the client as a cross-platform mobile app using React Native with TypeScript. React Navigation handles the bottom tabs and the stack flows between listing, detail, and cart. TanStack Query manages server data, caching, and loading states, while Zustand holds lightweight local state such as cart count, wishlist toggles, and active filters. The year-range slider and category filters update query parameters, so results refresh without a full reload. Reusable components (GPU card, spec row, filter chip, quantity stepper) are driven by a shared design-token file for the dark theme and green accent, keeping the UI consistent across screens.
Backend
The backend is a Node.js REST API (Express or NestJS) backed by PostgreSQL through Prisma. The core schema covers users, GPUs, wishlists, carts, cart items, orders, order items, and builds. Each GPU record stores brand, series (GTX or RTX), architecture, release date, MSRP, CUDA cores, memory size and type, bus width, and TDP. MSRP is stored separately from the live selling price, which matches the app's "launch price vs. market price" approach, and all money values are stored as integer cents to avoid rounding errors. Endpoints support search, brand, series, and year-range filtering (GET /gpus?series=RTX&yearFrom=2016&yearTo=2026), product details, cart and wishlist actions, and order history. Authentication uses JWT with refresh tokens, images are served through a CDN, and Stripe handles checkout.
The result is a GPU shopping experience that feels premium, informative, and easy to navigate, built for gamers and creators.
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Signal: AI-Powered Sales Development and Outreach Platform
I designed Signal as a full-stack AI sales development platform that helps teams discover prospects, manage outreach sequences, prioritize conversations, and convert engagement into qualified meetings. Instead of spreading sales activities across disconnected tools, Signal brings prospect research, communication, automation, and performance reporting into one focused workspace.
The visual identity uses a pitch-black interface with subtle violet, teal, green, and amber accents. Flat panels, restrained borders, professional outline icons, and generous spacing give the product a confident, human-designed appearance. Color is used functionally to distinguish active sequences, positive responses, scheduled meetings, prospect scores, and automation statuses without overwhelming the interface.
The main dashboard provides an immediate overview of active prospects, emails sent, reply rates, and meetings booked. Outreach performance is displayed through clean charts, while campaign tables highlight prospect volumes, delivery activity, and response rates. A dedicated research queue shows the work currently being completed by the sales agent without relying on distracting animations or exaggerated AI visuals.
The Prospects screen operates as the central lead database. Users can search and filter contacts, review company information, examine buying signals, compare qualification scores, and track each prospect’s latest activity. Qualified prospects can be moved directly into outreach sequences, creating a connected workflow from discovery to engagement.
The Sequences workspace enables teams to build and monitor structured, multi-step campaigns across email and LinkedIn. Every sequence presents its delivery schedule, prospect volume, open rate, reply rate, meetings generated, and current operating status. The connected Inbox brings replies into one place, prioritizes high-intent conversations, and provides context-aware response suggestions that users can review, edit, or send.
The AI Agent workspace gives users control over automated prospecting. Teams can define their ideal customer profile using industry, company size, geography, seniority, department, and buying-signal criteria. Guardrails and written instructions determine how prospects are researched, qualified, enriched, and prepared for outreach, while a live activity log keeps every automated action visible and accountable.
The Analytics screen connects outreach activity to commercial outcomes. It tracks pipeline sourced, meetings booked, opportunities created, cost per meeting, funnel conversion, top-performing buying signals, and sequence-level performance. This allows sales leaders to understand not only how much activity is taking place, but which audiences, signals, and messages are producing meaningful results.
The backend architecture is designed around a secure REST or GraphQL API layer built with Node.js and TypeScript. PostgreSQL stores users, workspaces, prospects, companies, sequences, messages, tasks, campaign events, and analytics records. Redis supports caching, background queues, rate limiting, and scheduled outreach, while asynchronous workers handle enrichment, qualification, message generation, email delivery, follow-ups, and CRM synchronization.
AI capabilities are managed through a controlled orchestration layer that combines company data, prospect history, buying signals, sequence context, and user-defined instructions. Retrieval-based context can be used to ground generated messages in approved company information, while approval workflows, audit logs, prompt controls, and configurable sending limits provide operational oversight.
Authentication uses secure session or token-based access with role-based permissions for administrators, managers, and sales representatives. External integrations can connect Signal with email providers, LinkedIn-compatible workflows, calendars, enrichment services, and CRM platforms such as HubSpot or Salesforce. Webhooks capture delivery, open, reply, bounce, booking, and opportunity events in near real time.
Across the complete product, I prioritized clarity, practical automation, transparent AI assistance, and connected navigation between Dashboard, Prospects, Sequences, Inbox, AI Agent, Analytics, and Settings. The result is a modern sales development platform that feels focused, professional, scalable, and genuinely useful to the people responsible for building pipeline.
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Northstar CRM: Full-Stack Customer Relationship and Sales Management Platform
I designed and developed Northstar CRM to transform customer relationship management into a focused, visually intuitive, and connected workspace. Instead of overwhelming sales teams with dense tables and fragmented tools, I structured the platform around clear customer insights, actionable sales data, and streamlined daily workflows.
The visual identity combines a soft atmospheric background with glass-inspired surfaces, dimensional cards, and vibrant blue, teal, yellow, and charcoal accents. Carefully layered shadows, subtle gradients, and elevated components create a refined 3D appearance while maintaining clarity and professional usability. The entire interface follows a consistent 4:3 presentation ratio, making every screen suitable for portfolio displays and high-quality screenshots.
The overview dashboard gives users an immediate picture of business performance. Key metrics—including total revenue, pipeline value, completed tasks, priority deals, recent activities, and upcoming meetings—are presented through scannable cards, progress indicators, compact charts, and contextual status colours. This allows teams to identify important changes and required actions without navigating through multiple reports.
The customer management screen provides a searchable directory with company information, recent interactions, account value, relationship status, and assigned ownership. Selecting a customer dynamically updates the detailed profile panel, where users can review contact information, relationship health, next actions, and communication options such as messaging, calling, or scheduling a meeting.
The deal pipeline organizes opportunities into clear stages, including Discovery, Proposal, Negotiation, and Closing. Each opportunity card displays its customer, estimated value, expected closing date, owner, and current stage. The Kanban-style structure gives sales teams a visual understanding of pipeline movement while supporting faster prioritization and opportunity management.
The calendar and reporting screens connect operational planning with performance analysis. The calendar provides monthly scheduling, customer meetings, deadlines, focus time, and team commitments. The reporting workspace combines revenue trends, conversion rates, customer retention, pipeline sources, account health, and period-based performance comparisons through interactive charts and summary cards.
On the backend, I implemented a modular Node.js and Express REST API connected to a PostgreSQL database. The data model supports users, customers, companies, contacts, deals, pipeline stages, activities, tasks, meetings, notes, and reporting records. Structured relationships and indexed queries ensure that customer histories, pipeline summaries, and dashboard analytics remain fast and consistent as the dataset grows.
Authentication is handled through secure token-based sessions, password hashing, protected API routes, and role-based access control. Different permissions can be assigned to administrators, sales managers, and sales representatives, ensuring users only access or modify information relevant to their responsibilities. Input validation, centralized error handling, audit logging, and rate limiting strengthen reliability and application security.
Real-time updates allow changes to deals, tasks, customer activity, and meeting schedules to appear across connected screens without requiring a manual refresh. Background processes handle reminders, notifications, scheduled follow-ups, and reporting calculations, while caching improves the performance of frequently requested dashboard metrics.
Across the complete full-stack platform, I prioritized reusable frontend components, responsive layouts, secure API design, reliable data relationships, and consistent interaction patterns. The result is a modern CRM experience that feels premium and approachable while providing the technical foundation required for scalable customer management, sales execution, and business intelligence.
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