AI & Automation Engineering
AI in production, with guardrails and evaluations.
LLM-powered features, retrieval systems, and workflow automation — built with evaluation suites, cost controls, and a human in the loop where it matters.
Overview
Demoing an LLM feature takes an afternoon. Running one in production — accurate, affordable, auditable, and safe with customer data — is a different engineering problem. We build the second kind: evaluation suites that catch regressions, prompt and retrieval architecture that can be changed without a rewrite, cost ceilings, and human review on anything consequential.
What's included
- LLM application development
- Retrieval-augmented generation (RAG) systems
- AI agents and Model Context Protocol integrations
- Document processing and data extraction
- Business process and workflow automation
- AI feasibility assessments and prototypes
How the engagement runs
- 1
Feasibility assessment
An honest read on whether AI is the right tool for the problem, what accuracy is realistically achievable, and what it will cost per request at your volume.
- 2
Evaluation suite
A test set with scored outputs, so a prompt or model change is validated against real cases instead of vibes.
- 3
Guardrails
Input validation, output constraints, PII handling, rate limiting, and logging of every model call for audit purposes.
- 4
Human-in-the-loop design
Clear escalation paths so low-confidence cases route to a person with the context attached, rather than failing silently.
What you get out of it
- AI features that survive contact with real users and edge cases
- Manual, repetitive processes reduced to review-and-approve
- Measured accuracy, latency, and per-request cost before rollout
- Clear boundaries on what data goes where
This is a good fit when
- A team spends hours on work an LLM could draft and a human could approve
- You have documents or tickets nobody has time to read
- An AI prototype works in demos and falls apart on real inputs
- You need to know what AI would cost before committing to it
Typical stack
- Claude
- Gemini
- Model Context Protocol
- Python
- TypeScript
- Vector databases
Industries we deliver this for
Ways to buy this
- Discovery Sprintfrom $1,5001–2 weeks · Fixed fee
- Fixed-Scope Buildfrom $12,0004–16 weeks · Milestone-billed
- Dedicated Teamfrom $4,000/month3 months minimum · Monthly retainer
- Care & Support Retainerfrom $900/monthRolling monthly · Monthly retainer
Starting points, not a rate card — how our pricing works.
Other services
Need ai & automation engineering?
Tell us what is in your way. Thirty minutes, no pitch — and an honest answer on whether we are the right team for it.
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