AI Implementation
From AI strategy to systems in production.
Most AI projects stall between a promising demo and a reliable product. We've built the bridge — RAG systems, agent workflows, evaluation harnesses, and the platform plumbing that makes AI safe to deploy and easy to improve.
- RAG and search-grounded assistants
- Agent and tool-calling workflows
- Evaluation harnesses and offline regression
- Model selection, routing, and cost control
- Data and retrieval pipelines
- Governance, safety, and human-in-the-loop
01
Why most AI projects stall
The demo works. The pilot doesn't. We see the same root causes: no evaluation harness, no clear retrieval strategy, no observability, no plan for when the model is wrong. We design around those failure modes from day one.
02
What we build
Practical, well-instrumented AI systems. A few engagement shapes:
Production RAG
Retrieval-augmented assistants over your knowledge base, with chunking, hybrid search, citations, and freshness controls.
Agentic workflows
Tool-calling agents for back-office work — quoting, support triage, document review — with guardrails and human checkpoints.
Evaluation platform
Golden datasets, offline regression suites, online A/B, and a workbench your domain experts can actually use.
AI platform foundations
Model gateway, prompt registry, tracing, cost controls, PII handling, and the policy work to deploy with confidence.
03
How we work
We start with the business metric. Then we design the smallest end-to-end slice that can move it, instrument it, and ship it behind a flag. Once evals are stable, we scale. We're provider-agnostic and pragmatic about open-source vs. frontier models — the right answer is usually a portfolio.
Engagement
A clear, repeatable process
- 01Frame
The business metric, the user, the data, and what 'good' means.
- 02Prototype
End-to-end slice with evals from day one. No demo theater.
- 03Harden
Tracing, fallbacks, safety, cost controls, human-in-the-loop.
- 04Scale
Roll out behind flags, tied to metrics finance will accept.
FAQ
Frequently asked questions
- We have a great prototype. Why isn't that enough?
- Prototypes work on the happy path. Production needs evals, fallbacks, observability, cost controls, and a path for domain experts to improve the system. That's the work.
- Are you tied to a specific model or vendor?
- No. We've built on OpenAI, Anthropic, Google, Mistral, and self-hosted open-source models. The right choice depends on latency, cost, privacy, and quality on your task.
- Can you work with our existing engineering team?
- Yes — that's our preferred model. We bring AI-specific patterns and leave a team that can extend the system on their own.
- What about safety and compliance?
- We design with PII handling, prompt-injection defenses, audit logs, and human review built in. For regulated industries we'll work with your legal and security teams from day one.
Ready to move forward with confidence?
Tell us about your situation. We'll respond within one business day.