AI Engineering
LLM applications, RAG, agents and automation that hold up on real data.
We design and build AI features and applications — from retrieval-augmented assistants to multi-step agents — with the evaluation, guardrails and monitoring they need to be trusted in production.
The problem
Most AI prototypes impress in a demo and break on real data: confident wrong answers, unpredictable costs, and no reliable way to tell whether a change made things better or worse. The hard part isn't calling a model — it's retrieval quality, evaluation, latency, cost control and safe failure.
How we approach it
We start by defining what "good" means for your use case and turning it into an evaluation set. Every design decision after that — chunking strategy, retrieval method, model choice, prompt structure, tool design — is measured against it. You get an AI capability with a known baseline, clear cost per request, and behaviour you can explain to your users.
Capabilities
What we build
- LLM applications and copilots
- Retrieval-augmented generation (RAG)
- AI agents and tool use
- Document intelligence and extraction
- AI workflow automation
- AI integrations into existing products
- Evaluation, guardrails and monitoring
Deliverables
What you receive
- Model and architecture rationale, written down
- Evaluation suite with a measured baseline
- Production API or in-product integration
- Guardrails, fallbacks and human-in-the-loop paths
- Cost and latency dashboard
- Runbook and handover documentation
Technology
Tools we use for this work.
Chosen per project for fit, maturity and your team's ability to maintain it — not novelty.
AI & LLM
- OpenAI API
- Anthropic Claude API
- LangChain
- LlamaIndex
- pgvector
Frontend
- TypeScript
Backend
- Python
- FastAPI
Data
- PostgreSQL
Related work
AI Engineering in practice.
- Candidate
- Application
- Résumé parser
- AI scoring
- Database
HR & people operations · AI application
Mereb Talent: AI-assisted recruiting for a developer talent pool
A talent platform that connects developers with project opportunities. Behind registration, AI parses résumés, scores and filters candidates, and automates the recruiting workflow.
- AI workflows
- Intelligent document processing
- Résumé parsing
Generative AI
AI Photo Editor
- Upload
- Pre-processing
- AI model
- Transformation
- Output
Team experienceAI · Generative AI
AI Photo Editor: a multi-step image pipeline
An AI photo-editing application that runs images through a multi-step pipeline — AI enhancement, image transformation and automated processing.
- AI workflows
- Image processing
- Image enhancement
Questions
Before you ask.
How do you price projects?
Fixed-scope projects are priced per milestone after a short discovery phase. Dedicated teams are billed monthly. Either way, you receive a written proposal with scope, assumptions, milestones and pricing before anything is committed.
Who will actually work on my project?
A team led by a technical lead, with a project manager as your main point of contact. Every team member is visible in your client portal.
Who owns the code and infrastructure?
You do. Source code, infrastructure and documentation live in your own accounts from day one, so there is no lock-in.
Can you take over an existing codebase?
Yes. We start with a short technical assessment — architecture, code quality, security, test coverage and deployment — and give you a written report with a prioritised plan before proposing any work.
Start a project
Planning an AI project?
Three short questions about your project. We reply within one business day — with questions that show we read it.