RAG, AGENTS + EVALUATION
AI & LLM applications
Source-grounded assistants and intelligent product features with explicit permissions, measurable quality, and a human path when confidence is low.
01Knowledge
02Retrieval
03Reasoning
04Action
IDEAL FIT
Teams with useful knowledge or repetitive decisions that need a controlled AI layer—not an opaque chatbot bolted onto the side.
DESIGNED OUTCOMES
Answers linked to approved evidence
Model choice without product rewrites
Safe escalation and observable quality
CAPABILITY
One delivery system, not disconnected workstreams.
Strategy, interface, architecture, and verification stay connected so decisions survive the move from a diagram into production behavior.
01
Retrieval-augmented generation
02
Provider-neutral model routing
03
Tool-using assistants and guardrails
04
Evaluation, tracing, and feedback loops
ENGAGEMENT PATH
Start with the uncertain part.
01
Frame
Clarify the operating problem and the people inside it.
02
Prove
Test the riskiest product and technical assumptions.
03
Build
Ship complete, observable vertical slices.
APPLIED AI
Let’s map the useful first version of ai & llm applications.
We’ll help frame the product boundary, the useful first release, and the technical decisions that deserve care.