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03 — Service

AI Product Engineering

AI features that behave reliably in production — not demos that break under real data

LLM IntegrationRAG PipelinesAI Agents

Overview

AI features your customers can depend on, built to hold up on real data, not just in the demo.

An AI demo that nails ten curated questions and a production AI feature that survives real users are two different engineering problems. We build the second one: hybrid search and reranking so retrieval actually finds the right answer, evidence gates so the system abstains instead of guessing, tool-use agents scoped to least privilege instead of broad write access, and document processing pipelines that hold up on the messy files real customers upload.

Products where AI is a core feature, not a bolt-on.

What’s included

  • LLM Integration
  • RAG Pipelines
  • Tool-Use Agents
  • Document Processing

How we work

Our process

Four stages, one continuous thread — from your first idea to a product your customers use every day.

  1. 01

    Stage 01

    Discover

    We map your goals, constraints, and users so the team builds the right thing from day one.

  2. 02

    Stage 02

    Design

    Architecture, data models, and user flows documented before a line of production code is written.

  3. 03

    Stage 03

    Develop

    Iterative builds with weekly check-ins — you see working software throughout, not just at the end.

  4. 04

    Stage 04

    Maintain

    Full handover — code, deploy, and docs — then ongoing support as your product grows.

Pay for outcomes, not hours logged.

Every engagement is scoped and priced before a line of code is written. You always know what you’re paying for next.

Model A

Fixed price

Best for
Scope we can define up front
Examples
  • An MVP
  • A defined feature set
  • A focused integration
Terms
One price, one timeline, agreed before we start.

Model B

Milestone-based

Best for
Larger builds where scope evolves
Examples
  • A platform rebuild
  • A multi-phase modernization
Terms
Priced and paid in stages tied to delivered, working software — not hours on a timesheet.

No surprise invoices, no hourly creep. If scope changes mid-build, you hear what it costs before it costs you anything.

See how an engagement works

Our work(04)

A sample of what we’ve shipped — real projects, real constraints, real outcomes.

Ragix — Production RAG Platform — screenshot

01 / 04AI Platform

Ragix — Production RAG Platform

A production RAG system that cites its sources, refuses when it doesn't know, and enforces tenant boundaries at the database level — not in the prompt.

FastAPI / pgvector / Hybrid Search / Next.js / Supabase / Multi-tenancy

AI Support Queue with Human-in-the-Loop — Postgres-Enforced Safety — screenshot

02 / 04AI Automation

AI Support Queue with Human-in-the-Loop — Postgres-Enforced Safety

Shipped in eight hours, with the risk boundary enforced at the database level rather than in the prompt — a guarantee a system relying on LLM discipline alone can't make.

PostgreSQL / AI Tool Use / Row-Level Locking / Human-in-the-Loop

Secure E-Commerce Ordering POC — ASP.NET Core — screenshot

03 / 04E-Commerce

Secure E-Commerce Ordering POC — ASP.NET Core

Delivered fixed-price, full-stack (frontend, backend, and database), rated 5.0 by the client, who noted it was delivered on time, exceeded the original scope, and was "professional, responsive, and easy to work with."

ASP.NET Core / SQLite / JWT/OAuth / REST API

Arabic Hijri Datepicker — Angular 19 Component — screenshot

04 / 04Developer Tooling

Arabic Hijri Datepicker — Angular 19 Component

Delivered in a single day against an urgent brief, rated 5.0 by the client, who highlighted the fast turnaround and clear communication.

Angular 19 / TypeScript / ng-bootstrap / Reactive Forms

Ready to build? Book a free 30-minute discovery call.