AI Engineering
AI features that actually work in production.
No demos, no proofs-of-concept nobody uses. We build LLM integrations, RAG systems and AI workflows that handle real traffic — with observability, fallbacks, and unit economics that actually work.
The Problem
80% of AI projects never make it to production.
The reason is rarely the model. It's the missing engineering discipline around it: no tests, no evals, no rate limits, no cost observability, no structured prompt management. Prototypes break on first real traffic.
Our Approach
Engineers first, AI specialists second.
That means: every LLM integration comes with testing, monitoring, caching and clear cost budgets. Every feature has an eval dataset before it ships. Every prompt is versioned. We know what a p99 provider outage costs and design for it.
What we build
LLM Integration
Claude, GPT-4, Gemini, open-source models via OpenRouter. Provider-agnostic with fallback routing and per-feature cost budgets.
RAG Architectures
Retrieval-Augmented Generation with pgvector, Qdrant or Weaviate. Hybrid search, re-ranking, context-window management for 100k+ documents.
AI Agents & Tool Use
Multi-step agents with structured tool calling, state management and guardrails. MCP servers for integration into existing tools.
Evals & Observability
Braintrust, Langfuse or custom eval pipelines. A/B testing of prompts, regression detection, per-feature cost dashboards.
How we work
6-week cycles. Fixed price. NDA-first.
Discovery Call
30 min free. NDA upfront. We look at your problem and tell you honestly whether AI makes sense here — or whether there's a cheaper solution.
Fixed-Price Scope
Within 48h you get a concrete proposal with fixed price, timeline and clearly defined scope. No vague estimates.
Sprint to Production
6 weeks, weekly reviews, weekly deployments. At the end: your feature runs on real traffic, not staging.
Handover + Maintenance
Documentation, evals, dashboards — all handed over to your team. Optional: maintenance retainer for monitoring + incident response.
What you have after
- An AI feature that handles real traffic — with tests, evals and monitoring
- Clear cost economics: you know what every request costs and where to optimize
- Documentation your team can read — no black box
- Prompt versioning + eval setup for future iterations
- Fallback strategy for provider outages (at least 2 providers)