Applied AI
AI & LLM Integration
AI that ships, not demos. We put models behind guardrails and evals so the feature is still trustworthy in month six.
A prompt in a notebook is not a product. We build the unglamorous parts around the model — retrieval over your own data, tool and agent orchestration, evaluation suites that catch regressions before users do, and the cost and latency work that makes the economics survive scale.
Capabilities
RAG & retrieval pipelines
LLM application architecture
Agents & workflow automation
Evaluation harnesses & guardrails
Model, cost & latency tuning
Data pipelines & vector stores
How we work
01
FramePick the use case that actually pays back, and define what “good” means in numbers.
02
PrototypeA working slice on your real data within weeks — measured, not demoed.
03
HardenEvals, guardrails, fallbacks and cost budgets before anything reaches users.
04
OperateMonitoring, drift checks and a release loop for changing models and prompts.
