AI & Automation
From promising prototype to dependable production feature.
Most AI work stalls in the gap between a demo that impressed a stakeholder and a feature that can be trusted with real users. We close that gap: evaluation, guardrails, cost control, and the engineering that makes it production-grade.
We work in whatever the project already uses: its language, its framework, its cloud. Taking over a build means adopting the decisions already made, not restarting on ours.
The signals that bring this work to us
If more than one of these sounds familiar, an assessment is usually the cheapest next step. It replaces guesswork with a scoped plan.
A prototype that works in a notebook but has no path to production
An AI feature whose output quality nobody can measure or defend
Token or inference costs that make the feature uneconomical at scale
Manual internal processes that should have been automated already
Concrete work, not a capability list
LLM Application Engineering
Retrieval pipelines, tool-using agents, and structured-output flows built as maintainable software.
Evaluation Harnesses
Test sets and scoring so changes to prompts or models can be measured instead of guessed at.
Guardrails & Safety
Input validation, output constraints, fallback behavior, and human review paths where they matter.
Vector & Retrieval Infrastructure
Embedding pipelines and search infrastructure tuned for relevance, latency, and cost.
Workflow Automation
Repetitive internal processes replaced with reliable, observable automated workflows.