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I will build custom ai solutions, ai saas, ai agents and chatbots
Umair Athar
About this gig
Most "AI" gigs deliver a ChatGPT wrapper. I build AI software that runs in production.
WHAT I ACTUALLY SHIP
- RAG systems - the AI answers from YOUR documents and data, with confidence scores and a review queue for anything uncertain. No invented facts.
- AI agents and automation - multi-step workflows that read, decide and act across your tools.
- Document and data processing - OCR, extraction, classification, auto-drafting.
- AI chatbots grounded in your real product, policies and pricing.
PROOF, NOT PROMISES
- Refine AI - multi-tenant compliance platform. Ingests policies, rewrites them to SOC 2 / ISO 27001, auto-drafts security questionnaires with RAG vector search.
- SurvaIQ - AI property valuation reports from live UK government data.
- Tretech - OCR plus Gemini turning study material into an AI tutor with generated exams.
- WCAG platform - GPT-4.1 generating code-level accessibility fixes.
STACK
- AI: OpenAI, Gemini, RAG, vector search, embeddings
- App: Next.js, React, Node.js, TypeScript
- Data: MongoDB, PostgreSQL, Redis
- Cloud: AWS, Docker, CI/CD, Stripe
Get to know Umair Athar
Umair Athar
AI and full stack engineer building production software
- FromPakistan
- Member sinceMay 2024
- Avg. response time1 hour
Languages
Urdu, English, Hindi
I'm Umair, a full-stack and AI engineer with 5+ years shipping production software, not prototypes.
What I do:
1. AI products: RAG systems, AI agents, document processing, and chatbots grounded in your data so they don't invent answers.
2. Full-stack builds: SaaS platforms, dashboards, CRM/ERP, eCommerce, and the APIs behind them.
Recent: a multi-tenant SOC 2 compliance platform with RAG search, an AI property-report engine on live UK government data, and a store running 6 payment gateways.
Stack: Next.js, React, Node, TypeScript, MongoDB, OpenAI, Gemini, AWS.
My Portfolio
FAQ
Will the AI make things up?
Not if it's built right. I ground every answer in your actual data through retrieval, attach confidence scores to generated output, and route anything low-confidence to a human review queue. That's the difference between an assistant that quotes your real refund policy and one that invents it.
Do you train custom machine learning models?
Rarely, because most business problems don't need it. I build on OpenAI GPT and Google Gemini through their APIs, combined with retrieval over your own data. You get frontier-model quality without training anything, and the budget goes into integration and guardrails - which is where these projects
Can you add AI to our existing app?
Yes, and that's most of this work. I work inside your current codebase and stack, add the AI layer behind clean APIs, and keep every change reviewable by your team. The Basic package is built for exactly this.
Who owns the code?
You do. Code, repositories and IP transfer to you on final delivery. I don't hold anything back as leverage.
What do you need from me to start?
A description of the workflow, sample data or documents the AI will work with, and access to any system it must connect to. If you don't have all of that yet, message me and we'll scope it together.
How do you ensure AI security?
Retrieval is scoped per user and tenant, so one account can never pull another's documents into an answer. Inputs are treated as untrusted to limit prompt injection, API keys live in environment config rather than the repo, and every generated answer is logged with its sources.
Is our data used to train the model?
No. The business API tiers from OpenAI and Google do not train on your data, and I configure it that way deliberately. Your documents stay in your own index and your own storage, and I document exactly what leaves your system and what never does.
What stops the AI costs running away?
Token budgets per tenant, caching on repeated queries, and usage visible from day one rather than discovered on an invoice. I also size the model to the task: most steps in a pipeline do not need the most expensive model available.
