Evals before opinions
Every pipeline ships with an evaluation harness and a baseline, so quality claims are measured, not asserted.
AI Consulting
GenAI implementation
RAG systems, agents and LLM features taken from validated idea to monitored production. Built on evals, not vibes.
What it is
GenAI implementation is where a validated use case becomes a system your business runs on. We design and build LLM features, retrieval pipelines and agent workflows, integrated with your existing systems and measured by an evaluation harness from day one. Production is the goal, and the pilot is a checkpoint on the way there.
The work runs in stages: architecture and data pipeline first, then a pilot on your real data measured against an agreed baseline, then production hardening with guardrails, monitoring, cost controls and a rollout plan. You see measured quality numbers at every stage, and a no-go is always an acceptable outcome, agreed before we start.
What changes for you: an AI feature with known accuracy, latency and cost per request, running inside your infrastructure and your compliance boundaries. Your team gets the evaluation harness, the runbooks and the training to go with them, so they can extend, retrain and operate the system without depending on us for every change.
Why it works
Production-grade from the pilot
The pilot is built on the architecture that will go live, so nothing gets thrown away when you scale.
Cost and latency by design
Model routing, caching and prompt budgets are engineered in from the start, not patched on after the first invoice.
Guardrails that hold
Input validation, output filtering and fallbacks for the failure modes LLMs actually have in production.
Your team can run it
Handover includes the eval harness, runbooks and training, so the system is yours in practice, not just on paper.
AI Consulting
How we run it
01
Scope and baseline
We fix the use case, the success metric and the baseline it must beat, then design the architecture and data flow.
02
Build the pilot
A working system on your real data in four to six weeks, evaluated continuously against the baseline.
03
Harden
Guardrails, monitoring, load and cost testing, security review, and integration into your deployment pipeline.
04
Roll out and hand over
Staged rollout with live metrics, then handover with documentation, runbooks and training for your team.
What you get
Production-ready GenAI system in your infrastructure
Evaluation harness with baseline and quality metrics
Guardrail and fallback layer for production failure modes
Monitoring dashboard for quality, latency and cost
Runbooks and handover training for your team
Common questions
Which models do you use?
The ones that win on your evals. We benchmark OpenAI, Anthropic and open-weight models on your data and pick per task, often routing between a strong model and a cheap one. The architecture keeps you free to swap models as the market moves.
Can this run inside our cloud and compliance boundary?
Yes. We deploy via Azure OpenAI, AWS Bedrock or self-hosted open-weight models when data must stay in your tenancy or region. Compliance constraints go into the architecture up front, not as an afterthought.
What happens if the pilot misses the baseline?
You get the measured results and an honest read on why: data, model or use-case fit. Sometimes the fix is a scope change, sometimes the answer is stop. Both cost far less than scaling a system that does not work.
Have a use case that is ready to build?
Bring it to a free 30-minute call. A senior engineer will tell you what it takes to get it into production.
Scope the build