Freelance Systems Engineers

Freelance Systems EngineersFreelance Systems Engineers

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AI Agents | LLMs, Computer Vision & Full-Stack Dev

88: Followers

AI Agents | LLMs, Computer Vision & Full-Stack Dev

Cover image for 🚨 Turning CCTV Into a

🚨 Turning CCTV Into a Crime-Prevention AI System We helped build Aegis FaceGuard, an AI-powered security platform designed to prevent armed robberies before they happen—not after. In high-risk regions like Colombia, traditional CCTV only records. This system predicts, detects, and reacts — instantly. Here’s what it catches in real time: 🪖 Helmet detection 🔫 Gun detection 🎭 Mask detection 👤 Suspicious posture ⚠️ Threat movement patterns When danger appears, the system automatically: Triggers alarms Alerts police Notifies the owner Saves evidence — all within milliseconds This is security before the crime, not after. If you're building AI for public safety, retail protection, or real-world automation, I’d love to connect. 🔥 #AI #ComputerVision #SecurityTech

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Cover image for 🚀 Most AI outreach tools

🚀 Most AI outreach tools stop at writing messages. Warmo.ai (http://Warmo.ai) actually helps you find prospects, personalize outreach, and start meaningful conversations—all from one platform. Whether you're a freelancer, agency owner, recruiter, or founder, it's built to save hours of manual work and help you book more meetings. 🔥 Best part? The first users can use it completely FREE. If you've been waiting for an AI tool that actually helps you grow your business instead of just generating text, now's the time to try it. 👉 Check out Warmo.ai (http://Warmo.ai) and claim your free access while it's available. #AI #Sales #LeadGeneration #Outreach #Freelancer #Agency #Startup #BusinessGrowth #Automation #Contra

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Cover image for AI-Powered Code Editor for Modern Developers

AI-Powered Code Editor for Modern Developers

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Cover image for WorkMind AI: Unified AI Workplace Platform

WorkMind AI: Unified AI Workplace Platform

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Systems Engineer(1)

Islamabad, Pakistan

Enterprise Cloud · DevOps · AI/ML · Email · Marketing Expert

$10k+: Earned

13x: Hired

4.4: Rating

41: Followers

Enterprise Cloud · DevOps · AI/ML · Email · Marketing Expert

Cover image for Legacy System Scalable and Secure Modernization

Legacy System Scalable and Secure Modernization

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Cover image for Implementing DevOps Practices; Enterprise-Level Transformatios

Implementing DevOps Practices; Enterprise-Level Transformatios

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Cover image for Maximize Data Protection & Availability with Vetted Veeam Expert

Maximize Data Protection & Availability with Vetted Veeam Expert

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Cover image for Citrix DaaS Implementation & Optimization Services

Citrix DaaS Implementation & Optimization Services

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Systems Engineer(4)

Yasin Nurmohamed

proLeiden, Netherlands

Everything for your visibility & Growth

$1k+: Earned

2x: Hired

5.0: Rating

3: Followers

Everything for your visibility & Growth

Cover image for YOPE – Process Optimization & Capacity Planning

YOPE – Process Optimization & Capacity Planning

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Cover image for Squarespace Website Revamp

Squarespace Website Revamp

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Cover image for Portfolio — Sparkvivid

Portfolio — Sparkvivid

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Cover image for An award winning speech

An award winning speech

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Systems Engineer(3)

proDimapur, India

Full-Stack Engineer | AI & Security | 6+ Years of Experience

1x: Hired

5.0: Rating

40: Followers

Full-Stack Engineer | AI & Security | 6+ Years of Experience

Cover image for Speaker-Aware AI Voice Assistant Development

Speaker-Aware AI Voice Assistant Development

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Cover image for Filim: Full-Stack Streaming Platform

Filim: Full-Stack Streaming Platform

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Cover image for Cloudon: Backend & Telegram Bot

Cloudon: Backend & Telegram Bot

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Cover image for DOOM ’93 Level Explorer

DOOM ’93 Level Explorer

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Systems Engineer(7)

proKarachi, Pakistan

Production-ready AI agents and SaaS built to scale reliably.

$25k+: Earned

2x: Hired

80: Followers

Production-ready AI agents and SaaS built to scale reliably.

Cover image for Project Overview:
Conduit's Interoperable Orchestration Platform

Project Overview: Conduit's Interoperable Orchestration Platform (IOP) seamlessly connects robots, machines, software, and sensors into a unified command center, enabling real-time factory floor automation and intelligence. It accelerates deployment, reduces integration costs, and transforms operations into fast, resilient, high-output systems. The Challenge Factory automation is often limited by: 1. Fragmented systems: Robots, machines, and sensors operate in silos, complicating coordination. 2. High integration costs: Connecting diverse equipment requires custom, expensive solutions. 3. Lack of real-time insights: Without live data, decision-making is slow, reducing efficiency and output. The Solution Conduit's IOP streamlines factory operations by: 1. Unified orchestration: Connects any robot, machine, software, or sensor into a single command center. 2. Rapid deployment: Automation workflows can be implemented in days, not months. 3. Live factory intelligence: Provides real-time insights to optimize productivity, reduce downtime, and enhance resilience. 4. Landing Page Description Conduit's Interoperable Orchestration Platform (IOP) connects every robot, machine, software, and sensor into a single command center. Deploy automation in days, gain real-time factory insights, and transform your operations into a fast, resilient, high-output powerhouse. What Our Client Got: We delivered a fully functional Conduit IOP with: - A unified command center connecting all robots, machines, software, and sensors. - Rapid deployment tools for automation workflows in days. - Real-time dashboards and analytics for actionable factory insights. Results Achieved: - 70% faster automation deployment across the factory floor. - 60% reduction in costly system integrations. - 85% improvement in operational efficiency and uptime. Conduit IOP is now transforming factories into high-output, resilient, and intelligent operations.

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Cover image for 😍 This the best

😍 This the best I have done in health industry - A SaaS platform: https://treatmentnotes.com/ 🚀 Built an AI-powered behavioral health documentation platform designed for real clinical workflows. The system supports AI generated notes from live conversations, uploaded transcripts, and custom knowledge-base logic tailored to mental health and addiction treatment. It was designed for secure, scalable healthcare use with HIPAA ready architecture and EMR integration support. 💯 Complete Tech Stack: AI Summarization, Clinical Nuance Detection, FastAPI, Vite, React, AWS Bedrock, Anthropic Claude, PostgreSQL, pgvector, Vector Database, Retrieval-Augmented Generation (RAG), AI Agent Architecture, HIPAA Compliant Cloud, Speech-to-Text API, LLM Fine-tuning, Healthcare Interoperability, HL7/FHIR Integration, Serverless Backend, Semantic Search, Private LLM Deployment, Encryption at Rest, Python Backend Development. 🧠 What do you say Contra Community?

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Cover image for From Instinct to Infrastructure: How

From Instinct to Infrastructure: How Athliq Went From Trainer's Notebook to Production Performance Platform ────────────────────────────────────────── 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. ────────────────────────────────────────── 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. ────────────────────────────────────────── 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. ────────────────────────────────────────── 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. ────────────────────────────────────────── 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. ────────────────────────────────────────── 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. ────────────────────────────────────────── 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. ────────────────────────────────────────── 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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Cover image for KovaRisk: When the Interface Knew

KovaRisk: When the Interface Knew More Than the System ────────────────────────────────────────── The Expert in the Room Compliance officers don't struggle to understand risk. They struggle to act on it fast enough. The team behind KovaRisk understood this precisely. They had spent years inside financial institutions watching the same dysfunction repeat: alerts buried in spreadsheets, investigations tracked in email threads, audit trails reconstructed after the fact. They knew what the interface needed to feel like because they'd lived with the one that didn't. So they built it. Fast. Exactly as they'd imagined it. What emerged was sharp: a risk monitoring dashboard with filterable alert feeds, entity profiles with 12-month risk trajectories, a rule engine with toggle controls, and an audit log that felt immutable. The scenario switcher let compliance teams stress-test different alert load states. The side panel made investigations feel contained and intentional. It looked like a system that had survived production. It hadn't been asked to yet. ────────────────────────────────────────── What Existed Was a Strong Interface - Not a System Every alert in KovaRisk was generated at startup. Every risk score was computed by a random seed function. Every status change - Investigating, Resolved, Escalated - lived in component state. Every timeline event was appended to an in-memory array. Every rule toggle disappeared on refresh. The audit log recorded nothing. The export downloaded a snapshot of what React was holding at that moment. The entity risk history was a curve drawn from a formula, not a record. The logic was there. But it had nowhere to live. A compliance officer investigating a high-risk wire transfer would open the side panel, read the plain-English rule explanation, mark the alert as Investigating, add an internal note - and lose every one of those actions the moment they refreshed the browser. No colleague could see what they'd done. No regulator could verify it had happened. In financial compliance, that's not a UX problem. It's a liability. The prototype validated the workflow brilliantly. It exposed exactly how a compliance team would move through their day. But three things were missing: a source of truth, a coordination layer, and a trail that could be audited under pressure. ────────────────────────────────────────── They Didn't Need More Features - They Needed a System Behind the Interface The team came with a clear idea and a working prototype. What they needed was the architecture that made the prototype a product - the layer that turned interface actions into durable facts. Not a rebuild. A foundation. ────────────────────────────────────────── The Layer That Made It Dependable Data Models: Giving State a Home The first thing to reconstruct was where the data should actually live. KovaRisk's frontend implied a clear schema - alerts, entities, rules, audit events - but none of it persisted. The production system needed a PostgreSQL core with five primary entities: • Alert — with foreign keys to Entity, Rule, Transaction, and a JSONB timeline column for ordered event history • Entity — with risk tier, jurisdiction metadata, and a one-to-many relationship to RiskScore snapshots • Rule — with active/disabled state, trigger thresholds, false-positive tracking, and a versioning mechanism so changes to rules didn't retroactively alter historical alerts • AuditEvent — append-only, with actor ID, action type, target reference, and a server-generated timestamp that clients cannot modify • InternalNote — owned by an alert, with authorship and a soft-delete flag to preserve compliance integrity Every status change, note, escalation, and flag the UI handled ephemerally became a write to this schema. The Alert Generation Engine: Replacing the Seed Function In the prototype, 85 alerts appeared because a loop ran 85 times at startup. In production, alerts are the output of a Transaction Monitoring Service - a background process that runs continuously against incoming transaction streams. This service: • Evaluates each transaction against every active Rule definition • Computes a risk score using rule weights, entity risk tier, jurisdiction flags, and behavioral baselines • Creates an Alert record only when a threshold is breached • Emits an event to a notification queue for high-risk triggers The rule engine the UI let users toggle wasn't decorative. Each rule mapped to an evaluation function in the monitoring service. Disabling a rule didn't just grey out a card - it removed it from the active evaluation set. Re-enabling it didn't retroactively generate alerts it would have caught; it resumed from the point of activation. That distinction mattered for regulatory defensibility. ────────────────────────────────────────── Async Workflows: The Operations the UI Implied But Couldn't Sustain Several interactions in the prototype implied workflows that couldn't complete synchronously. Escalation - When an alert was escalated, the UI changed a status badge. In production, escalation triggers a queue job that: notifies the senior compliance officer via a configured channel, creates a case record linking the alert, and starts a response SLA timer. The UI reflects the outcome - it doesn't produce it. Scheduled Screening - The Sanctions Screening Match rule in the prototype was static. In production, it's a nightly job that re-screens all active entities against updated OFAC, EU, and UN sanctions lists - generating new alerts if a previously clean entity now appears. The results feed back into the alert pipeline. Report Export - The dashboard's Export Report button downloaded a text file of whatever React was holding in memory. In production, report generation is an async job: the user requests the report, the job runs server-side against the live database, and a download link is returned when ready. The content is a verifiable, timestamped record - not a UI snapshot. ────────────────────────────────────────── The Audit Log: From Feed to Fact The prototype's audit log was populated by a generateAuditLog function. It looked comprehensive and immutable. It was neither. Production audit events are written by the API layer on every state-modifying operation - before the response is returned to the client. The table is append-only. No update operations are permitted on AuditEvent records. Timestamps are server-generated in UTC and stored with full precision. Actor identity comes from the authenticated session, not from a string the client sends. The audit log the interface displayed was a simulation of accountability. The production version is the accountability. ────────────────────────────────────────── Tech Stack 1. Frontend: React + Vite, React Router v6, Recharts, React Context + local state, TanStack Query 2. Backend: Node.js + TypeScript, Fastify, Prisma, PostgreSQL, Redis, BullMQ, Passport.js + express-session 3. Infrastructure: AWS ECS / Railway / Render, S3, AWS Secrets Manager / Doppler, Sentry + Datadog, GitHub Actions 4. External Integrations: OFAC / ComplyAdvantage, Refinitiv World-Check, SendGrid / SMTP, Webhooks ────────────────────────────────────────── From Interface to System The prototype answered the right questions. It proved the workflow was sound, the information hierarchy was correct, and the alert investigation pattern worked the way compliance officers needed it to. What it couldn't answer was: what happens when two investigators open the same alert simultaneously? What happens when a rule change needs to take effect immediately across 200 pending alerts? What happens when a regulator asks for every action taken on a specific entity over the past 18 months? Those questions don't live in the interface. They live in the system. KovaRisk's interface was always strong. What it needed was the architecture to make it real - persistent, coordinated, auditable, and defensible under scrutiny. The logic existed from the beginning. We gave it somewhere to live.

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Systems Engineer(2)

El Mehdi El Wafi

Linux VPS SysAdmin AWS & Oracle Cloud Architect | Terraform

$10k+: Earned

1x: Hired

12: Followers

Linux VPS SysAdmin AWS & Oracle Cloud Architect | Terraform

Cover image for Enterprise VM Template for Proxmox VPS

Enterprise VM Template for Proxmox VPS

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Cover image for DevOps/Cloud & DataOps maintainer

$12K+ earned

DevOps/Cloud & DataOps maintainer

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Cover image for 92% Cost OFF of EC2 Airflow/DBT Workers using Lambda

92% Cost OFF of EC2 Airflow/DBT Workers using Lambda

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Cover image for 🛠️ DevOps Tips 🛠️ If

🛠️ DevOps Tips 🛠️ If your GitHub workflows or jobs are failing due to jobs concurrently accessing and/or modifying shared resources, You can control execution at the workflow or job levels using the concurrency. Jobs/Workflows in the same are prohibited from running at the same time, and you can cancel any jobs/workflow if you trigger another one. Groups let you define compartments in which you don't want concurrent workflows running. They can be organized however you like. I find that using them in separate environments is a great practice. Cancel-in-progress: specifies if the old running job/workflow needs to shut down. Read more in docs: https://docs.github.com/en/actions/how-tos/write-workflows/choose-when-workflows-run/control-workflow-concurrency

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Systems Engineer(2)

Khondoker Ali Asgor Pavel

Dhaka, Bangladesh

Experienced IT Specialist & WordPress Developer

$5k+: Earned

2x: Hired

5.0: Rating

18: Followers

Experienced IT Specialist & WordPress Developer

Cover image for WatchSaver LLC Server Management

WatchSaver LLC Server Management

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Cover image for SEO for customviallabels.com & vialpackaging.com

SEO for customviallabels.com & vialpackaging.com

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Cover image for 2r3ml Website SEO

2r3ml Website SEO

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Cover image for 2r3ml.com Website Development

2r3ml.com Website Development

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Systems Engineer(1)

Russell Williamson

Little Rock, USA

Small-Business Website Builder | RVW Systems

1x: Hired

5.0: Rating

12: Followers

Small-Business Website Builder | RVW Systems

Cover image for A roofing company was spending

A roofing company was spending $70,000 a year on a 4-person manual inventory and procurement operation. I replaced it with a zero-touch pipeline. No human data entry. No spreadsheet handoffs. No "did we order that?" Slack messages at 9pm. The system runs on Airtable, Python, and OpenAI. It took 3 weeks to build. It paid for itself in the first month. Here's what I've learned after 20+ years of operations work and building these systems across hospitality, e-commerce, SaaS, and construction: The expensive problem is almost never the one you think it is. It's not the software. It's the 6 spreadsheets nobody wants to touch. It's the workaround that became company policy. It's the process that only works because one person memorized it. I build systems that replace all of that: the CRM, the automation, the AI agents, the dashboards, the content pipelines. One architect, one connected system, everything built to work together from day one. Recent builds: Recovered 20 hours/week for a multi-venue hospitality operation Unified 5 e-commerce storefronts into one automated command center Built an AI system that replaced 8 improv actors per venue per night (yes, really) Turned a hiring round into a one-button AI content pipeline What's the one manual process in your business that you know is costing you the most time, but you haven't fixed yet? #BusinessAutomation #AI #Operations #SystemsArchitecture #Airtable #Python #WorkflowAutomation #NoCode #ECommerce +1

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Cover image for Building an AI-powered affiliate

Building an AI-powered affiliate publishing engine from the ground up. I am currently working on a full automation system for NodeRidge, designed to turn product and software research into structured, review-ready WordPress content with human approval built into the workflow. The goal is simple: remove the repetitive work from affiliate publishing while keeping quality control, visibility, and editorial review in place. This build connects: 🗄️Airtable as the central operations hub ⚡Make.com (http://Make.com) for workflow automation 🧠OpenAI for content generation 📝WordPress for draft publishing 📊Google Search Console for rank and performance tracking What I like most about this project is that it is not just "AI writing content." It is a real backend publishing engine with structured data, review gates, sync monitoring, and operational visibility. The system is already handling prompt runs, WordPress draft syncing, affiliate link registry logic, and QA tracking. My next focus is scaling the Airtable architecture to handle higher volume. This is the kind of build I enjoy most: connecting messy manual workflows into a clean system that can actually scale. What are the biggest operational bottlenecks you're currently dealing with in your own workflows?

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Cover image for Stop building science experiments and

Stop building science experiments and start building scalable, autonomous operational systems. I just published a detailed look under the hood at my most sophisticated AI integration yet: The Hierarchical Multi-Agent Business System. Most founders use ChatGPT to write emails. I build AI agent teams to source leads, design technical architectures, manage client-facing dashboards, and automate complex content pipelines—all with zero human input for everything except final approval. Take a look at the attached infographic to see how this recursive, self-optimizing architecture functions. Here is the operational impact of what I build: Departmental Specialization: I don't use one "smart" agent. I build a crew of highly specialized agents (Lead Gen, Content, DevOps) overseen by a Manager Agent to ensure quality and prevent hallucinations. The Human-in-the-Loop Safeguard: The entire workflow is designed to be fully autonomous until final quality control and strategic approval. You get the scale of AI with the certainty of a human final check. Tool Agnosticism: My builds are logic-first. We use the best tool for the task, whether that's CrewAI, LangGraph, Supabase, Cloudflare, OpenAI, or a custom API. If you are a founder or operator ready to turn your standard business workflows into high-performance, autonomous engines, check out the full case study. https://contra.com/s/2rFLgNAU-custom-ai-agent-for-your-business-workflow

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Cover image for Architected an autonomous AI pipeline

Architected an autonomous AI pipeline that ingests product links, conducts SEO research, and generates publish-ready affiliate reviews with zero manual writing.

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Systems Engineer(5)