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Founder-led AI engineering · fixed scope, fixed price

AI that survives contact with your real data.

I build production AI assistants, launch AI products, and automate recurring data work — for founders and operators who need it working, not demoed. Fixed scope, fixed price, and you own every line.

Book a 30-min scoping call See 4 systems I've shipped

Free: what would your build cost? · 20+ products shipped · Free fixed-scope quote in 24 hours

20+Products shipped to production
5+Years building AI & backend systems
27Countries in one data pipeline
100%Of the code and IP is yours

Four ways to work together.

No hourly billing and no open-ended retainers you can't exit. Pick the outcome, agree the number, and I ship it.

From $8,000 · ~4 weeks

AI Assistant — RAG in 30 Days

An assistant trained on your own documents that answers accurately and shows where each answer came from.

  • Data ingestion, cleaning and embedding
  • Retrieval with evals and guardrails
  • Chat widget or API, your branding
  • Monitoring, docs and deployment

For teams buried in support tickets, internal Q&A, or document review.

Scope an AI assistant

From $12,000

AI Product MVP — Idea to Launch

A real product your users can sign into and use. Not a demo that dies on a laptop after the board meeting.

  • Architecture and sharp MVP scoping
  • Backend, frontend and LLM integration
  • Auth, billing and core workflows
  • Deploy, runbook and handover

For founders validating an AI product fast.

Scope an AI product

From $6,000 · 3–6 weeks

Data & Automation Engine

The manual data job someone does every morning, running by itself before anyone opens a laptop.

  • Stealth scraping with proxies and CAPTCHA handling
  • ETL, deduplication and enrichment
  • Scheduling with summaries and failure alerts
  • Delivery into your CRM, database or dashboard

For lead-gen, market intelligence and back-office data work.

Scope an automation

Monthly · pause anytime

Fractional AI & Backend Engineer

Senior engineering on tap, without the cost or the hiring risk of a full-time role.

  • Dedicated engineering days each month
  • Direct access — no account-manager layer
  • Architecture, builds and code review
  • Priority delivery, cancel whenever

For funded teams needing steady AI/backend firepower.

Discuss engineering support

Messy manual problems, turned into systems that run themselves.

Client names are withheld by agreement. The problems and the architecture are real.

Search Intelligence Pipeline

SerpApi · OpenAI · Python · SQLite

Problem
A market-intelligence client needed structured infrastructure-project data across 27 countries. Gathering it by hand was impossible, and keeping it current was worse.
Built
A large-scale pipeline on SerpApi covering Google Search and AI Overview, with automated CAPTCHA handling, structured parsing, and OpenAI batch processing into a queryable database.
Outcome
2,046 queries across 27 countries became one clean, queryable dataset — replacing weeks of manual research with an engine that repeats itself.

Automated Lead Engine

Playwright · Proxy rotation · Skip-trace APIs · GoHighLevel

Problem
A real-estate investment client checked dozens of US county court and sheriff sites by hand, every single morning, before the good leads went cold.
Built
A daily stealth Playwright pipeline with proxy rotation that harvests filings, dedupes them, enriches through skip-trace APIs, and pushes only qualified leads into the CRM — with daily summaries and failure alerts so a broken site is noticed the same morning.
Outcome
Fully unattended. Enriched, qualified leads land in the CRM every day with zero manual work.

PulseOps — Live DevOps Observability

FastAPI · Kafka · PostgreSQL · OpenSearch · Valkey

Problem
Engineering teams had no single live view of pushes, PRs, deploys and incidents as they happened.
Built
An event-driven platform streaming events through Kafka into PostgreSQL and OpenSearch with Valkey caching, pushing updates to dashboards over Server-Sent Events.
Outcome
Sub-second live observability — teams see every deploy and incident the moment it happens, not minutes later.

Verita AI — Voice-First Clinical Platform

Django · AssemblyAI · Claude · Groq · Gemini

Problem
Clinicians lose hours to note-taking after every consultation — most of it transcription rather than judgement.
Built
A voice-first platform combining AssemblyAI speech-to-text with Claude, Groq and Gemini for clinical entity extraction, multi-turn chat and auto-generated summaries.
Outcome
Consultation notes generate automatically from the visit itself. Hours of after-hours charting became seconds.

Two minutes, three real problems.

No pitch. Just the kind of manual work that quietly costs a business a day a week.

What an AI build actually costsReal numbers: $8k, $12k, $6k — and why most agencies won't give you one.
40 websites, checked by nobodyA daily manual job replaced by a pipeline that reports its own failures.
The same question, forty times a dayWhy front-desk load is a response-time problem, not a staffing one.

Four steps, and you know the number before step two.

Scope

A free call to find the real problem, define what success actually means, and map the simplest architecture that gets there.

Quote

A fixed scope and a fixed price within 24 hours. If I think you shouldn't build it, I'll tell you that instead.

Build

Tight weekly iterations with working software each week — typed, tested, reviewed. You see progress, not status reports.

Hand over

Containerized deploy with CI/CD, monitoring and a runbook. Clean repos, docs, and 100% of the IP transferred to you.

I'd rather lose the project than take the wrong one.

Good fit

  • You have a specific problem, not a mandate to "add AI"
  • You want it in production, used by real people
  • You'd rather pay once for something that lasts
  • You can give me access to real data, not a sample
  • You want to own and maintain what gets built

Not a fit

  • You're shopping purely on lowest hourly rate
  • You need a demo for a pitch and nothing after it
  • The scope genuinely can't be defined yet
  • You need a large team on site
  • You want someone to agree with every decision
Saif Ur Rehman

Saif Ur Rehman

Senior Python engineer, 5+ years building AI-powered products, multi-tenant SaaS and production APIs. The work spans LLM applications and retrieval systems, event-driven streaming, and deep roots in scraping and ETL.

Remote-first and product-minded. You get direct access to the person doing the engineering — not a layer of account managers translating between you and a team you never meet.

The things people ask before signing.

How much does it cost to build an AI app?
A production AI assistant starts at $8,000 and takes about four weeks. A full AI product MVP starts at $12,000. An automated data pipeline starts at $6,000 over three to six weeks. The scope and the number are agreed before work starts — no hourly billing, no surprise invoices.
Who owns the code?
You do. 100% of the source and IP transfers on delivery, with clean repositories, documentation and a deployment runbook. No license, no lock-in, and no dependency on me to keep it running.
Will I work with the person actually building it?
Yes. This is founder-led. No juniors learning on your budget, and no account-manager wall between you and the engineering.
Why do most AI projects fail?
They fail in the gap between the demo data and the real data. A prototype tested on a handful of clean documents performs beautifully, then meets scanned PDFs, contradictory versions, and questions phrased the way people actually phrase them. The fix is evals built before the system — real questions with verified answers, run on every change — so retrieval quality is measured instead of assumed.
What if my data is sensitive?
Systems can be built to run entirely on infrastructure you control, with your data never leaving it. Tell me the constraints on the first call and I'll tell you honestly whether the architecture you want is possible under them.
What technologies do you use?
Python, FastAPI, Django/DRF, NestJS and Celery on the backend. React, Next.js, TypeScript and Tailwind on the frontend. PostgreSQL, Redis, Kafka and OpenSearch for data. Playwright for scraping. OpenAI, Claude and Gemini for language models. Deployed on AWS or GCP with Docker.
How fast can we start?
A free strategy call, then a fixed-scope quote within 24 hours. Build slots usually open within one to two weeks.

Tell me what's broken.

The more specific you are, the more useful the first call is. You'll get a fixed-scope quote within 24 hours, or an honest no.

Specifics beat polish. One honest paragraph is plenty.

Or email saif@kortexlabs.dev directly.