Enterprise AI engineering · fine‑tuning · retrieval · harness
Most enterprise AI pilots stall before they ship. Jentrix builds the missing layer — domain‑tuned models, agentic retrieval, and a versioned engineering harness — so AI becomes part of your operational loop, not a sandbox demo.
The model is replaceable. The harness is your engineering asset.
We rarely deliver all four at once. We start where the cost of staying a pilot is highest — and build outward from there.
Domain-tuned models on your data — adapters, an evaluation harness, and a retraining loop. Not a one-off checkpoint that drifts the week after it ships.
Retrieval as an investigation loop, not a single lookup: a small set of typed tools, bounded execution, observable steps, and answers that carry their citations.
Agents, skills, hooks, tools, memory and rules — the rails that make LLM work reproducible, observable and improvable. All of it versioned in your repo, reviewed, CI‑checked.
We help the team rewire the norms that quietly stop working at AI throughput — just‑in‑time planning, shift‑left verification, code as the source of truth, IC‑first managers.
Three views of the same idea: the loop that keeps a model fresh, the harness that keeps it operable, and retrieval done as an investigation rather than a guess.
A model is never finished. Curate → tune → evaluate → shadow‑deploy → observe → curate again — with regression gates at every hop, so quality moves in one direction.
Seven classes — agents, skills, hooks, tools, memory, rules and commands — live in the repo as code and configuration. Versioned, reviewed, CI‑checked. Different architectures, one shared discipline.
The agent searches, reads and reconsiders until the evidence is enough — then returns the answer with the file, the section and the line it came from. One shot becomes a loop with a memory of what it has seen.
Five steps. We plan for the hard part — messy data, auth, scale, audit — from day one, because that is where the 95% lose the thread.
We pick a process where a harness pays off: repeatability, cost of error, reproducibility requirements. Wrong problem is the single biggest reason pilots fail — so we start by getting the question right, with the people who own the P&L outcome.
A working slice in weeks — on your real data, auth and scale, not a clean-data notebook. You see signal before you commit budget, and we see where the messy edges actually are.
Validation, access control, structured logging, evals, rollbacks. The unglamorous layer that turns a demo into something you can actually operate — and audit when someone asks.
Memory consolidation, skill authoring, configuration audit — improvement loops on the roadmap with a real time budget. Without them the harness gets brittle, not stronger.
You get an engineering asset — versioned, reviewed, documented — and an owner with the right to say “no”. Not a black box, and not a permanent dependency on us.
04 — An honest fork in the road
It’s not a trendy accessory — it’s the answer to a specific class of complexity. Sometimes the honest answer is “not yet”, and we’ll tell you that before you spend the budget.
The model is replaceable. The harness is your engineering asset.
— the thesis behind every Jentrix engagementBring the workflow you’re not sure about. We’ll spend 30 minutes mapping whether a harness pays off — and you’ll leave with a written recommendation either way.
