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LLM Integration
We integrate large language models into your products and internal systems as engineered components — versioned, evaluated, observable, and reversible. Model choice follows from your constraints on data, latency, and cost, not from fashion.
When you need this
The situations we are called into.
A proof of concept works in demos but fails on real inputs, and nobody can say why.
You need model outputs your compliance team can audit and your engineers can regression-test.
Per-request costs are unpredictable and climbing with no attribution.
You must be able to substitute models — hosted or open-weight — without rewriting the application.
Approach
How the engagement runs.
Requirements and constraints
Accuracy targets, latency budgets, data-handling rules, and cost ceilings — agreed in writing before model selection.
Evaluation harness
Golden datasets and automated scoring built first, so every subsequent change is measured, not felt.
Integration architecture
A model gateway with routing, guardrails, fallbacks, and versioning between your systems and any model.
Hardening
Adversarial inputs, failure injection, and load testing before production traffic touches the system.
Operate or hand over
Dashboards, alerts, and runbooks — operated by us or transferred to your team with training.
Deliverables
What you hold at the end.
Evaluation harness with golden datasets
Model gateway and integration layer
Guardrail and fallback configuration
Observability dashboards and alerts
Operations runbook
Related research
The evidence behind this practice.
Technical report · 2026
Evaluating large language models for regulated enterprise workflows
A practical evaluation framework for LLM systems in banking, insurance, and government contexts — metrics, test harnesses, and a taxonomy of failure modes.
In preparation
Contact
Talk to our engineers.
Describe the situation you are in. The person who replies is the person who would do the work.