AI Engineering & Leadership
LLM Apps, Agents & RAG Systems
Production-grade LLM features, AI agents and retrieval systems, secure by design.
- Senior engineers only
- NDA on request
- Reply within 1 business day
Fit
Who it's for
A good fit if one of these sounds like you.
Companies building AI features into their products
Copilots, search and assistants your customers can rely on.
Teams building internal AI tools and assistants
Assistants over your own documents and systems, with access controls built in.
Products moving an AI prototype into production
Evaluation, monitoring and cost control, so it works beyond the demo.
Scope
What we do
Discovery and prototyping
Validate the use case quickly, with real data, before committing.
RAG and knowledge systems
Retrieval over your documents and data, with sources and access control.
AI agents and orchestration
Agents that call tools and APIs, with least-privilege permissions.
Evaluation and guardrails
Test sets, quality metrics and guardrails that catch regressions.
Secure deployment
Secrets, data handling, logging and abuse protection built in.
Ongoing optimization
Quality, latency and cost improved over time.
Deliverables
What you get
Concrete deliverables your team keeps and can build on.
- Working AI feature or system in production
- Evaluation suite and quality metrics
- Security and data-handling documentation
- Monitoring and cost dashboards
Process
How it works
- 1
Discovery
Use case, data, users and what good output looks like.
- 2
Prototype
A working prototype on real data to validate the approach.
- 3
Build
Production system with evaluation, guardrails and security.
- 4
Operate
Monitoring, optimization and improvements over time.
FAQ
Frequently asked questions
RAG or fine-tuning?
Most business use cases start with retrieval (RAG) because it keeps answers grounded in your current data. Fine-tuning helps in narrower cases, and we'll tell you which fits.
Which models do you use?
We're vendor-neutral and choose per use case, based on quality, cost, latency, data residency and risk.
How do you measure output quality?
With evaluation sets built from real examples, automated checks, and human review where it matters, run on every change.
Who owns what you build?
You do. Code and deliverables belong to the client as set out in the contract.
Insights
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Get in touch
Tell us what you're building.
Share a few details and a senior engineer will reply within 1 business day.
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