We build AI that touches real work: the contract nobody has time to read, the ticket queue that never clears, the forecast still living in a spreadsheet. Then we measure it, so you know whether it is helping.
A question box over your policies, contracts, and wikis that answers with citations and respects who is allowed to see what.
Multi-step automation for one process end to end — intake, triage, drafting, routing — with a human approving the parts that matter.
Extraction and classification for invoices, claims, statements, and forms, with confidence scores and an exception queue.
Demand, churn, and risk models built on your warehouse, retrained on a schedule instead of once, in a notebook, by someone who left.
Tooling, prompts, review practice, and guardrails so your own developers ship with AI safely and consistently.
Test sets, drift monitoring, cost dashboards, and audit logs — the part that decides whether the pilot survives contact with users.
Model-agnostic by default. Everything runs in your cloud accounts, under your keys, with your retention policy.
Chat, search, an embedded panel in the app your team already uses, or an API other systems call.
Scoped tools, step limits, retries, and a human approval gate wherever an action is irreversible.
Chunking, embeddings, hybrid search, and permission filtering applied before the model sees anything.
Connectors, sync jobs, and the warehouse or lake the rest of your reporting already trusts.
Eval sets per use case, PII handling, prompt and output logging, per-team spend limits and alerts.
If the workflow underneath is unclear, AI makes the confusion faster. We fix the process definition first, and sometimes that is the whole project.
A demo that impressed one room is not evidence. Every use case gets test cases and a passing bar before it goes live.
No proprietary runtime, no per-seat fee to us, no hostage data. If you replace us, the system keeps working.
One workflow, one measurable outcome, one fixed fee. At the end you have a system in production and enough evidence to decide what happens next.
Week 1–2 — workflow mapping, data access, and a written scope
Week 3–4 — working system on your data, reviewed with your users
Week 5–6 — evaluation, hardening, deployment, and handoff
An eval report with pass rates, latency, and running cost per task
Runbooks, architecture notes, and a walkthrough with your team
A recommendation on what to build next — or what to stop
The best first conversation is about a specific, annoying, expensive task. Tell us which one and we will tell you if AI is the right tool for it.
IT consulting and AI engineering for teams that need the work done, not the deck.
prtechnologiesus.com