LLM Application & Agent Engineering
Building chat, retrieval, and agentic systems held to the same engineering bar as the rest of your production stack — not a prototype that quietly became load-bearing.
Request an assessmentWhat we deliver
Conversational & agentic application design
Chat, copilot, and multi-step agent systems designed around the workflow they're replacing, not a wrapped API call.
Tool use & systems integration
Agents that take real actions against your APIs, internal tools, and data stores, scoped with the guardrails to keep them inside their lane.
Retrieval-grounded responses
Wiring the application to retrieval over your actual data, so answers are grounded instead of hallucinated.
Evals & guardrails built into the app
Automated checks for relevance, safety, and regressions shipped alongside the feature, not bolted on after a bad headline.
Use cases
Customer-facing chat or support agent
Production-grade conversational systems with retrieval and guardrails instead of a thin wrapper around a model API.
Internal copilot or workflow automation
Agents that take action across internal tools and systems, scoped and monitored rather than given free rein.
Retrieval-augmented search over proprietary data
Grounding responses in your own documents, code, or tickets instead of the model's general knowledge.
Hardening a prototype that became load-bearing
Taking a proof-of-concept LLM feature that quietly turned into production infrastructure and rebuilding it to hold.
Engagement model
LLM application engagements typically start with a short discovery sprint — what the system needs to do, what data it touches, and what failure looks like. From there we scope a fixed-price build of the application or agent itself, with evaluation and guardrails included from the start rather than treated as a follow-on phase.