Freelance AI Chatbot Developers in LahoreFreelance AI Chatbot Developers in Lahore
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Saif Ullah Mushtaq's avatar
Saif Ullah Mushtaq
proLahore, Pakistan
Certified Full-Stack Engineer, AI Expert & Tech Enthusiast!
5.0
Rating
14
Followers
Certified Full-Stack Engineer, AI Expert & Tech Enthusiast!
Cover image for Development of DeepSeek AI Chatbot SaaS Platform
Development of DeepSeek AI Chatbot SaaS Platform
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13
Cover image for Agentic AI – RAG-Based Chatbot Platform
Agentic AI – RAG-Based Chatbot Platform
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15
Cover image for Streamly SaaS Platform Development
Streamly SaaS Platform Development
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12
Cover image for Gymlytic - SaaS Gym Management Platform
Gymlytic - SaaS Gym Management Platform
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9
AI Chatbot Developer(2)
Saud Saleem's avatar
Saud Saleem
proLahore, Pakistan
Top Rated Plus Freelancer & Top 1% Talent
$5k+
Earned
2x
Hired
5.0
Rating
18
Followers
Top Rated Plus Freelancer & Top 1% Talent
Cover image for AI Function Calling Agent
AI Function Calling Agent
3
14
Cover image for Business Development AI Workflow
Business Development AI Workflow
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10
Cover image for ADHD-Friendly AI Automation Workflows
ADHD-Friendly AI Automation Workflows
3
40
Cover image for Anchor Down - Transport & Logistics Platform
Anchor Down - Transport & Logistics Platform
2
16
AI Chatbot Developer(1)
SYED ALI 's avatar
SYED ALI
Lahore, Pakistan
Claude, Codex AI SaaS | More Output, Less Tokens, Clean Code
9
Followers
Claude, Codex AI SaaS | More Output, Less Tokens, Clean Code
Cover image for AI Voice Agent for Car
AI Voice Agent for Car Dealerships (Manages Inbound phone calls) My role. AI Voice Agent Expert (Voice-to-Voice AI bot) Developer Project description. I demonstrate Dealer Voice, a voice-to-voice agent designed specifically for car dealerships. The system automates call handling, providing summaries and call recordings, and allows for agent management and voice selection. I walk you through creating an agent for a dealership, including setting up the phone number, operating hours, and context for the calls. I also show a live call example where I inquire about available cars, highlighting the system's efficiency. Please test the bot by calling the assigned dealership number to experience its capabilities firsthand. Skills and deliverables Bot Development Natural Language Processing AI Bot Artificial Intelligence Python
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221
Cover image for I worked on MoolAI, an
I worked on MoolAI, an enterprise AI agent platform built for back-office SaaS workflows. The platform helps companies move from AI pilots to production-ready agents with proper governance, guardrails, workflow control, and deployment management. I focused on creating a clean, scalable, and business-friendly AI SaaS experience rather than a basic chatbot product.
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157
Cover image for AndalibAI is a voice AI
AndalibAI is a voice AI product for businesses that want to handle customer calls through natural conversation. The product focuses on custom AI voice agents that can answer questions, support customers, book appointments, qualify leads, and work with business tools and workflows. From the product positioning, it is built around voice intelligence, helping teams interact with their systems, data, and customers through conversation instead of manual back-and-forth
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153
Cover image for The platform provides a holistic
The platform provides a holistic solution, offering improved asset management, user visibility, risk assessment, security maturity, and streamlined data management. Ultimately, the project aims to empower SMBs with the tools and insights needed to protect their assets from cyber threats effectively. http://app.assetcurve.io
1
158
AI Chatbot Developer(1)
Abdul Rehman Haris's avatar
Abdul Rehman Haris
proLahore, Pakistan
AI Automation Engineer | n8n, APIs & AI Agents
New to Contra
AI Automation Engineer | n8n, APIs & AI Agents
Cover image for WhatsApp AI Chatbot & Order
WhatsApp AI Chatbot & Order Automation System | n8n + Claude AI Agent An n8n-based WhatsApp chatbot that handles customer conversations and order automation from start to finish, delivered as part of a completed 80-hour engagement rated 5.0 out of 5. Client feedback: "Very strategic and great to work with! Knows his stuff and communicates clearly." The agent uses Claude to understand customer intent, hold context across a conversation, and take the correct action, capturing orders, answering frequently asked questions, and updating records, while every interaction is logged for review. Built with the same reliability standard used across all of my automation work: input validation, structured AI output instead of raw free text, and error handling that catches failures instead of letting messages disappear silently. Stack: n8n, Claude AI, WhatsApp, webhooks and API integration.
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115
Cover image for ARH Jarvis | Voice and
ARH Jarvis | Voice and Text Command Automation Assistant (n8n + Windows Agent) A personal automation assistant that takes voice or text commands from a web page or phone and carries them out on a real Windows computer, in English or Roman Urdu. Voice commands are transcribed through Groq, then converted into a structured command. Text commands go through a separate authenticated route: common commands are resolved with deterministic rules with no AI cost involved, while complex natural-language requests are planned by DeepSeek before execution. Both paths converge on the same Windows agent, connected through a permanent tunnel, with a confirmation step before any sensitive action runs. Current capabilities include opening and searching applications such as YouTube, Gmail, and WhatsApp Web, sending Gmail messages and WhatsApp messages, checking the PC's live status, writing files to approved folders, and monitoring the automation platform itself: it can detect the latest failed workflow execution, summarise the error, and save a report to the desktop. Every request is authenticated at each hop, and errors return as structured categories such as authentication_error, planner_error, or execution_error, rather than a plain crash. Actions such as arbitrary software installs, unrestricted system commands, or unrestricted file deletion are intentionally blocked by design, not because they were forgotten. This is a personal proof of concept still being hardened, with voice recognition consistency and fully autonomous multi-step flows as the current development focus. It uses the same architecture principles I bring to client systems: hybrid AI plus deterministic logic, authenticated multi-hop communication, and structured error handling. Stack: n8n (self-hosted on Contabo), Groq, DeepSeek, Windows local agent, ngrok, Gmail API.
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41
Cover image for AI Sports Stats Automation |
AI Sports Stats Automation | n8n Data Pipeline + Parent Dashboard An automated data pipeline built in n8n that pulls sports statistics on a schedule, cleans and structures the data, and pushes it into a live dashboard for parents to track, with no manual data entry required. The workflow handles scheduled triggers, API pagination, data transformation, and structured writes to the dashboard's data source, with retry logic so one failed request does not break the entire run. Demonstrates the data engineering side of automation work: scheduled triggers, pagination, data cleaning, and dashboard-ready output, not only chatbots and lead generation. Stack: n8n, REST APIs, Google Sheets and dashboard integration.
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74
Cover image for Lead Generation & Contact Research
Lead Generation & Contact Research Automation | n8n + Claude AI Agent Built a 90+ node workflow in n8n that takes a plain-language lead request from Slack and turns it into a verified, CRM-ready contact list, with no manual research involved. Claude AI parses the request into a structured ideal customer profile covering company size, industry, region, and role. The workflow then searches for matching companies and contacts, verifies each email through Hunter and MX record checks, and cross-checks speciality, location, and name relevance before anything is written to the sheet. Every record passes through multi-layer deduplication against the current batch, past results, and related company records, then is routed into Ready, Review, and Rejected tabs with a full audit trail and failure log. Result from a real run: the client asked for 50 qualified leads. The system delivered exactly 50 Ready contacts, held back 7 for manual review, and filtered out 143 low-confidence or duplicate records automatically. Stack: n8n (self-hosted), Claude AI, Serper, Hunter, Google Sheets, Slack.
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121
AI Chatbot Developer(1)
Muhammad Anas Khan's avatar
Muhammad Anas Khan
Lahore, Pakistan
I build AI voice agents & RAG that replace manual work
New to Contra
I build AI voice agents & RAG that replace manual work
Cover image for AI Teaching Copilot — Learner-Aware
AI Teaching Copilot — Learner-Aware Generative AI Tutor for RRampUp (Canada) An AI tutor that detects whether a student is struggling or excelling — and rewrites its own teaching style in real time to match. Most AI tutoring tools give every student the same explanation regardless of level — struggling learners get lost, advanced learners disengage, and instructors have no way to personalize at scale. I built a learner-aware generative AI copilot for RRampUp (Canada) that solves this by classifying each student's level in real time and switching teaching styles automatically. The system tracks accuracy, response time, and topic mastery per learner to detect which mode fits: Supportive Mode kicks in on low accuracy or repeated errors, delivering step-by-step scaffolding with simpler vocabulary and a worked example first. Advanced Mode kicks in on high accuracy or fast correct answers, delivering concise direct responses with follow-up challenge questions that assume fundamentals are already mastered. Every single answer is grounded in real course material through a RAG pipeline — content is chunked, embedded, and retrieved per question — so the copilot never hallucinates an explanation untethered from the actual curriculum. Consistency across both teaching tiers was achieved through iterative system-prompt and few-shot tuning, evaluated and refined rather than shipped once and left alone. Results: 85% response accuracy, 95% positive learner feedback, 100% RAG-grounded answers (zero hallucination), and a single engine serving both remedial and advanced learners without separate systems. Stack: RAG pipeline with vector store (ingestion & retrieval) · learner profiling engine (accuracy, response time, topic-mastery tracking) · system-prompt & few-shot engineering per learner tier · LLM-based grounded generation. If you're building a tutoring product, internal training tool, or any Q&A assistant where users have different skill levels, this personalization layer adapts without needing separate systems per user tier.
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Cover image for NovaMart — AI-Powered E-Commerce Automation
NovaMart — AI-Powered E-Commerce Automation Suite Four connected AI workflows that turn every customer action — order, support ticket, abandoned cart, low stock — into an automated response. Everything after checkout is still done by hand for most stores — and it doesn't scale. Abandoned carts go unrecovered with no follow-up, support tickets are read and routed manually, order confirmations are pushed out by a person, and stock-outs are only discovered after they've already cost sales. I built NovaMart, a Next.js 16 storefront backed by four orchestrated n8n workflows that automate the entire post-checkout lifecycle. The moment a customer places an order, abandons a cart, or files a support request, the system takes over: orders move through confirm → ship → deliver → review automatically with every step visible in Slack; GPT-4o-mini classifies incoming support requests and routes them without a human in the loop; idle carts trigger timed recovery emails with a discount nudge; and daily inventory checks flag low stock and auto-pause zero-stock items before they turn into lost sales. A single Supabase database acts as the one source of truth across the entire system, so there's no fragmented data between the storefront, automation layer, and support tools. Results: Zero manual triage in the support loop, recovered revenue from carts that would otherwise have been lost, one unified source of truth instead of scattered spreadsheets, and inventory management that's proactive instead of reactive. Stack: Next.js 16 (storefront) · Supabase (database, auth, real-time) · n8n (4 orchestrated workflows) · GPT-4o-mini (support classification) · Slack (team alerts) · Gmail API (customer communications). If your store still runs on manual order tracking, ad-hoc support replies, or spreadsheet inventory checks, this same 4-workflow system can be adapted to your storefront in days.
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Cover image for Outbound Agent — AI-Powered Multi-Tenant
Outbound Agent — AI-Powered Multi-Tenant Insurance Verification & Document Follow-Up Platform An AI voice agent that calls insurance and provider offices, verifies coverage, and faxes missing documents live on the call — with human escalation built in. Calling doesn't scale with headcount — chasing missing documents and verifying insurance across hundreds of patient orders traditionally means hiring more staff to sit on hold. I built a multi-tenant AI voice platform that automates this end-to-end: the agent calls the office, identifies the patient using only the minimum PHI required, explains exactly what's missing, and confirms whether the prior request was received. If the office needs the document resent, the agent can trigger it to go out by fax directly during the call, using the submission method already on file — no manual follow-up needed. If the call requires human judgment — escalation, a sensitive case, anything outside defined scope — it transfers to a live agent instead of guessing or hallucinating a response. Every call returns structured, queryable data back to the platform: call status, blocker/reason, next step, submission path, full transcript, and recording reference — all mapped to the correct patient order. The platform is fully multi-tenant, with role-based access, tenant-scoped audit logs, and PHI-safe handling: call artifacts are automatically purged from the telephony layer once securely stored downstream. Results: Consistent, structured call outcomes across every tenant on one schema, scalable outbound follow-up on flat headcount, and fully auditable, PHI-compliant operations throughout. Stack: Python & FastAPI (backend) · SignalWire (telephony, call orchestration, fax dispatch) · Deepgram (speech-to-text) · ElevenLabs (voice synthesis) · RAG pipeline (grounding responses in live patient/order data) · webhook-driven post-call pipeline for transcript analysis and PHI cleanup. If your team is stuck chasing missing documents or verifying insurance over the phone, this same agent can be rebuilt around your provider network and compliance requirements.
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Cover image for Sophia — Autonomous AI Voice
Sophia — Autonomous AI Voice Receptionist for Dental Practices An AI receptionist that answers 100% of calls, verifies insurance live, and books directly into your calendar — 24/7. Built for a US dental practice specializing in cosmetic and implant consultations, Sophia is an autonomous voice AI receptionist that eliminates the two biggest sources of lost revenue in dental front desks: missed calls during procedures and hours lost to manual insurance verification. Sophia handles the entire patient call end-to-end — detecting intent (cosmetic, implant, insurance, or emergency), asking qualifying questions with empathetic, medically-tuned dialogue, verifying insurance in real time, and booking directly into the practice's live calendar via Cal. Ambiguous or sensitive cases escalate to a human automatically. Results: 100% call answer rate, sub-2-second response time, zero manual insurance calls, and 24/7 booking coverage — with the entire pipeline built on a reusable core that can be reconfigured for any dental or med-spa practice in days, not months. Stack: Vapi (telephony/voice widget) · Deepgram Nova-3 Medical (speech) · Claude/GPT-4o (reasoning) · Cal V2 API (scheduling) · structured JSON output per call for CRM logging. If your practice loses leads to voicemail, or your front desk spends hours on insurance calls, this same pipeline can be rebuilt around your calendar and services in days.
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AI Chatbot Developer(1)
Muhammad Ahsan's avatar
Muhammad Ahsan
Lahore, Pakistan
Expert Full Stack & AI Engineer | AI Web & Mobil Apps
Expert Full Stack & AI Engineer | AI Web & Mobil Apps
Cover image for 𝐊𝐚𝐢𝐝𝐨 – 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐚𝐧𝐝 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥
𝐊𝐚𝐢𝐝𝐨 – 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐚𝐧𝐝 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 Kaido is an AI-powered stylist SaaS platform designed to deliver a personalized, subscription-based fashion experience. The platform enables users to interact with an AI stylist, manage their wardrobe and preferences, receive product recommendations, and generate virtual try-on images using advanced AI models. It also provides embeddable chat solutions for eCommerce stores and a sourcing tool for product analysis and resale insights. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 AI Stylist Chat Experience: Users and guests can interact with an AI stylist for personalized fashion advice, recommendations, and styling guidance. Virtual Try-On & Image Generation: AI-powered image generation for outfit previews and mockups using multiple AI providers. User Preferences & Wardrobe Management: Store user style preferences, wardrobe items, and history for tailored recommendations. Product Recommendation Engine: Intelligent suggestions based on user behavior, preferences, and AI analysis. Embeddable Chat Widget (Plugin): Configurable website widget for store owners to handle customer queries, product inquiries, and order-related questions. eCommerce Integration: JSON-based catalog support for product search, recommendations, and customer assistance. Reseller Sourcing Tool: AI-powered analysis of uploaded product images using vision models and market comparison data. Automated Content & Sync Jobs: Cron jobs for blog generation, catalog updates, and product synchronization. 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 𝐂𝐨𝐫𝐞 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝑩𝒂𝒄𝒌𝒆𝒏𝒅 𝑨𝑷𝑰: Express.js handling AI workflows, user management, subscriptions, and integrations with AI services. 𝑨𝑰 𝑰𝒏𝒕𝒆𝒈𝒓𝒂𝒕𝒊𝒐𝒏𝒔: OpenAI for chat and vision processing, along with external APIs for image generation and virtual try-on. 𝑫𝒂𝒕𝒂𝒃𝒂𝒔𝒆 & 𝑺𝒕𝒐𝒓𝒂𝒈𝒆: MongoDB for storing user data, wardrobe items, chat history, product data, and sourcing insights. 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝 𝑾𝒆𝒃 𝑨𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏: React + Vite-based frontend providing a fast, responsive, and interactive user experience. 𝑼𝒔𝒆𝒓 𝑫𝒂𝒔𝒉𝒃𝒐𝒂𝒓𝒅:Interface for managing preferences, wardrobe, AI chats, and generated styling results. 𝐌𝐞𝐝𝐢𝐚 & 𝐀𝐈 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝑰𝒎𝒂𝒈𝒆 𝑷𝒓𝒐𝒄𝒆𝒔𝒔𝒊𝒏𝒈 𝑷𝒊𝒑𝒆𝒍𝒊𝒏𝒆: Integration with AI services for generating try-on visuals and mockups. 𝑭𝒊𝒍𝒆 𝑺𝒕𝒐𝒓𝒂𝒈𝒆 & 𝑺𝒆𝒓𝒗𝒊𝒏𝒈:Public access system for uploaded images and generated media assets. 𝑬𝒎𝒃𝒆𝒅𝒅𝒂𝒃𝒍𝒆 𝑷𝒍𝒖𝒈𝒊𝒏 𝑺𝒚𝒔𝒕𝒆𝒎:Lightweight chat widget for third-party websites with configurable branding, prompts, and API keys. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐀𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐲𝐬𝐭𝐞𝐦: JWT-based authentication for secure user sessions and access control. 𝐀𝐏𝐈 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥𝐬: Protection against vulnerabilities including SSRF risks and secure handling of external requests. 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: Designed to support multiple users, AI requests, and real-time interactions efficiently. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Secure handling of user data, API keys, and uploaded media. 𝐒𝐮𝐦𝐦𝐚𝐫𝐲 Kadio modernizes the fashion and eCommerce experience by combining AI-powered styling, virtual try-on technology, and intelligent product recommendations into a unified SaaS platform. With additional capabilities like embeddable store chat, reseller sourcing insights, and automated content generation, it empowers both users and businesses to enhance engagement, streamline operations, and scale personalized fashion experiences efficiently.
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Cover image for 𝐋𝐢𝐜𝐞𝐧𝐬𝐞 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐨𝐧 – 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐚𝐧𝐝
𝐋𝐢𝐜𝐞𝐧𝐬𝐞 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐨𝐧 – 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐚𝐧𝐝 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 License Companion is an AI-enhanced mobile and web platform built on the MERN stack (MongoDB, Express.js, React, Node.js) to simplify the management, tracking, and renewal of professional licenses, certifications, and related documents for individuals like medical professionals. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐔𝐬𝐞𝐫-𝐅𝐫𝐢𝐞𝐧𝐝𝐥𝐲 𝐎𝐧𝐛𝐨𝐚𝐫𝐝𝐢𝐧𝐠 Personalized welcome screens with guided setup walk users through adding their licenses, certifications, and key documents from day one. 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 Licenses, CE certificates, medical certifications, and insurance documents can be uploaded and organized in one secure and accessible location. 𝐑𝐞𝐧𝐞𝐰𝐚𝐥 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 Renewal timelines, associated costs, and required CE credits are monitored automatically with reminders sent well in advance of deadlines. 𝐌𝐮𝐥𝐭𝐢-𝐒𝐞𝐜𝐭𝐢𝐨𝐧 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞 An intuitive dashboard organizes licenses, certifications, associations, and documents into clearly separated sections for effortless navigation. 𝐒𝐞𝐜𝐮𝐫𝐞 𝐀𝐜𝐜𝐨𝐮𝐧𝐭 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 Account setup and sign-in support multiple options including email, Face ID, Google, and Apple for a fast and flexible onboarding experience. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐔𝐩𝐝𝐚𝐭𝐞𝐬 Upcoming reminders and renewal status are displayed with actionable prompts, keeping professionals informed and always one step ahead. 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 Node.js with Express.js handles all API operations and data processing efficiently across the platform. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 MongoDB stores all user data, licenses, certifications, and renewal schedules in a flexible and scalable structure. 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝 React delivers a responsive and dynamic user interface across both web and mobile experiences. 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐔𝐩𝐥𝐨𝐚𝐝𝐬 Secure file handling manages all license and certificate uploads with reliability and privacy at every stage. 𝐔𝐈 𝐑𝐞𝐧𝐝𝐞𝐫𝐢𝐧𝐠 Optimized layouts ensure a smooth and consistent experience across both mobile and web views. 𝐖𝐞𝐛 𝐚𝐧𝐝 𝐌𝐨𝐛𝐢𝐥𝐞 𝐔𝐈 Interactive screens support account creation, document uploads, and renewal tracking in a clean and accessible interface. 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 A centralized view with dedicated tabs for licenses, CEs, associations, and documents gives users complete control over their professional records. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐒𝐞𝐜𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 All personal and professional data is stored with full encryption to protect user privacy and maintain the highest standards of data security. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Fast loading times and real-time renewal timeline updates ensure a smooth and responsive experience for every user. 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 The platform is designed to support a growing user base with efficient data management that scales without compromising performance. 𝐒𝐮𝐦𝐦𝐚𝐫𝐲 License Companion empowers professionals to effortlessly manage their licenses, certifications, and documents through a seamless, AI-supported experience built on the MERN stack. With automated renewal tracking, secure document storage, and an intuitive multi-section dashboard, it ensures timely renewals and organized record-keeping for every professional who relies on it.
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Cover image for 𝐂𝐥𝐢𝐞𝐧𝐭 𝐀𝐜𝐜𝐨𝐮𝐧𝐭 – 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐚𝐧𝐝
𝐂𝐥𝐢𝐞𝐧𝐭 𝐀𝐜𝐜𝐨𝐮𝐧𝐭 – 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐚𝐧𝐝 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 Client Account is an ASIC-regulated, technology-driven platform built for law firms to streamline client fund management. Operating under an Australian Financial Services Licence (AFSL), it replaces traditional trust accounts with a secure, compliance-embedded digital system for custodial, depository, and non-cash payment services. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐀𝐅𝐒𝐋-𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐞𝐝 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 All transactions are processed within a framework authorised and overseen by ASIC, providing firms with full regulatory assurance at every step. 𝐓𝐫𝐮𝐬𝐭 𝐀𝐜𝐜𝐨𝐮𝐧𝐭 𝐀𝐥𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐯𝐞 Manual trust account processes are replaced entirely while maintaining the highest standards of client fund protection and compliance. 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐏𝐚𝐲𝐦𝐞𝐧𝐭 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 Electronic disbursements, incoming payments, and matter-level fund allocation are all supported within a single streamlined digital system. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 Built-in controls align with ASIC and legal obligations, keeping firms audit-ready at all times with minimal manual effort required. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐕𝐢𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 Dashboard access delivers instant insight into balances, transaction history, and fund movements for complete operational transparency. 𝐂𝐮𝐬𝐭𝐨𝐝𝐢𝐚𝐥 𝐒𝐞𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 Client money is held under a regulated custodial structure that ensures proper separation and full protection at all times. 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐀𝐜𝐜𝐞𝐬𝐬 Multi-user permissions are aligned with firm governance and internal controls to ensure the right people access the right information. 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 An AFSL-compliant custodial engine powers all payment processing, reconciliation, and audit trail functions with precision and reliability. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐚𝐧𝐝 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 Cloud-based encrypted storage handles all client fund data and transaction logs securely in transit and at rest. 𝐖𝐞𝐛 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 An interactive interface provides full access to transactions, reporting, and real-time fund tracking from a single centralized view. 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐕𝐢𝐞𝐰𝐬 Audit-ready displays are designed for both regulators and internal review teams, making compliance reporting straightforward and efficient. 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞𝐬 Secure, role-based access to balances, fund movements, and matter-level allocations ensures every team member sees only what they need. 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 A centralized view across multi-partner practices, matters, and audit logs gives leadership complete visibility and control over all fund activity. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐂𝐥𝐢𝐞𝐧𝐭 𝐅𝐮𝐧𝐝 𝐏𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧 Custodial segregation ensures all client money is handled securely and kept fully separate from operational funds at every level. 𝐄𝐧𝐜𝐫𝐲𝐩𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 All data is encrypted both in transit and at rest, meeting the highest standards of information security and confidentiality. 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 Automated reporting workflows are fully aligned with ASIC requirements, reducing manual compliance workload for firms significantly. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Fast, real-time dashboard updates and efficient fund tracking ensure a smooth and responsive experience for all platform users. 𝐒𝐮𝐦𝐦𝐚𝐫𝐲 Client Account modernizes client fund management for law firms by combining AFSL-regulated custodial services, digital payments, and automated compliance in a single platform. It reduces operational complexity, enhances transparency, and provides embedded regulatory assurance — enabling firms to manage client money confidently, efficiently, and securely.
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Cover image for 𝐁𝐢𝐚 – 𝐀𝐈-𝐃𝐫𝐢𝐯𝐞𝐧 𝐀𝐠𝐞𝐧𝐭 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦
𝐁𝐢𝐚 – 𝐀𝐈-𝐃𝐫𝐢𝐯𝐞𝐧 𝐀𝐠𝐞𝐧𝐭 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 Bia is an intelligent agent platform that automates business workflows by connecting AI agents with your existing systems, tools, and processes. It enables teams to run complex operations, handle repetitive tasks, and coordinate multi-step processes efficiently with minimal manual intervention. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐓𝐚𝐬𝐤 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 Agents perform tasks, generate outputs, and make decisions based on structured workflows with minimal human input required. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧-𝐑𝐞𝐚𝐝𝐲 Seamlessly connects to tools like Slack, Discord, Linear, Notion, Google Workspace, and more to fit into any existing tech stack. 𝐇𝐮𝐦𝐚𝐧-𝐢𝐧-𝐭𝐡𝐞-𝐋𝐨𝐨𝐩 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 Workflows pause automatically for approvals or clarifications via comments or tickets, keeping humans in control when it matters most. 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 Multi-step processes are handled with conditional logic, automatic retries, and event-driven execution for reliable end-to-end automation. 𝐀𝐠𝐞𝐧𝐭 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 Multiple AI agents can coordinate with one another to solve complex workflows that require parallel or sequential task execution. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐥𝐞𝐫𝐭𝐬 Track workflow progress, receive timely notifications, and intervene whenever needed to keep operations running smoothly. 𝐑𝐞𝐮𝐬𝐚𝐛𝐥𝐞 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐏𝐥𝐚𝐧𝐬 Ready-made templates for recurring tasks and workflows reduce setup time and ensure consistency across repeated operations. 𝐒𝐞𝐜𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 Enterprise-ready infrastructure with encrypted data handling and robust multi-user management supports organizations of any size. 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝 A web-based platform dashboard supports workflow design, agent management, and analytics in one centralized view. A collaboration interface handles human approvals and comments, while real-time workflow visualization keeps teams fully informed of progress at every stage. 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐚𝐧𝐝 𝐀𝐈 AI agents powered by large language models handle autonomous task execution across diverse workflow types. An orchestration engine supports retries, conditional logic, and parallel execution, while dedicated integration modules connect seamlessly to external tools and APIs. 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 Task execution logs and historical workflow data are stored for full operational visibility. User comments and human-in-the-loop actions are tracked alongside templates and agent training data to support continuous improvement. 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 A cloud-native scalable architecture supports growing teams and workloads without performance limitations. Secure multi-tenant access enables organizations and teams to operate independently, while an event-driven system with webhook support allows external triggers to initiate workflows automatically. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 Encrypted communication and secure data storage protect all platform activity at every level. Role-based access control governs who can interact with specific workflows and agents, while comprehensive audit logs capture all agent actions and approvals for complete accountability and transparency. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 Manual tasks are reduced significantly and workflow efficiency is improved across departments and functions. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐓𝐞𝐚𝐦𝐬 Ticket triaging, responses, and follow-ups are automated to free support teams for higher-value interactions. 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 Multi-agent workflows coordinate complex projects across teams with speed and precision. 𝐑𝐞𝐦𝐨𝐭𝐞 𝐚𝐧𝐝 𝐇𝐲𝐛𝐫𝐢𝐝 𝐓𝐞𝐚𝐦𝐬 Human-in-the-loop interventions for approvals and clarifications keep distributed teams aligned and workflows on track. 𝐃𝐚𝐭𝐚-𝐇𝐞𝐚𝐯𝐲 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 Automated reporting, document parsing, and structured task management handle high-volume data workflows with ease. 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 Tasks are executed end-to-end with minimal supervision, allowing teams to focus on strategy rather than execution. 𝐄𝐯𝐞𝐧𝐭-𝐃𝐫𝐢𝐯𝐞𝐧 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 Tasks pause, retry, and resume automatically based on triggers, ensuring no workflow step is ever skipped or lost. 𝐇𝐮𝐦𝐚𝐧-𝐢𝐧-𝐭𝐡𝐞-𝐋𝐨𝐨𝐩 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 Manual review and feedback are integrated directly within automation flows for balanced and controlled operations. 𝐌𝐮𝐥𝐭𝐢-𝐒𝐲𝐬𝐭𝐞𝐦 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 Existing apps and platforms connect seamlessly so teams can automate without rebuilding their entire toolset. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 Workflow performance and agent outputs are monitored continuously for full operational transparency and control. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬 Slack, Discord, Linear, Notion, Google Workspace, and a wide range of other business tools are supported out of the box. Webhooks and API-based triggers connect external systems directly into the workflow engine. Templates and reusable agent workflows further simplify the automation of recurring tasks across the organization. 𝐒𝐮𝐦𝐦𝐚𝐫𝐲 Bia is an AI-driven agent platform designed to modernize business workflows and operations. By combining autonomous AI agents, human-in-the-loop interventions, and seamless integrations, Bia enables organizations to automate complex tasks, improve efficiency, and gain real-time operational visibility — all within a single unified platform.
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AI Chatbot Developer(1)
Usama Imran's avatar
Usama Imran
Lahore, Pakistan
Full-Stack AI Developer |Agentic AI • Product Design & UI/UX
Full-Stack AI Developer |Agentic AI • Product Design & UI/UX
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