Intake & alignment
We document goals, constraints, data sources, and who owns outcomes — so the project starts with shared context, not assumptions.
Workflow mapping
We shadow how work actually moves — tools, handoffs, bottlenecks, and repeat tasks — before recommending where AI earns its place.
Opportunity scoring
Not every task should be automated. We rank use cases by time saved, revenue impact, risk, and how ready your data is.
Guardrails & success criteria
We define what good output looks like, where humans must approve, and what gets logged — before prompts or code get written.
Proof of concept
A thin slice of the workflow goes live in a sandbox so stakeholders can judge quality on real inputs, not slide decks.
Knowledge preparation
Docs, examples, FAQs, and edge cases get cleaned, tagged, and structured so models retrieve the right context every time.
Operator interface
Your team gets screens to trigger runs, review output, override decisions, and see what the system did — and why.
Integration build
Models, APIs, CRMs, inboxes, spreadsheets, and internal tools get wired into one pipeline with error handling baked in.
Validation & hardening
We stress-test outputs, permissions, latency, failure recovery, and approval paths with real data before anyone depends on it daily.
Team enablement
Live walkthroughs, playbooks, and admin docs so operators know how to run, tune, and escalate — without opening a ticket.
Production rollout
We flip the switch in phases, watch early usage, tune prompts from feedback, and keep improving after day one.
