Why one workflow, not a transformation.
Companies don't fail at AI because the models aren't good enough. They fail because they treat AI as a platform rollout — buy the suite, integrate everything, change every team's tools, train everyone simultaneously. The work stretches into quarters and the value never lands.
We pick the opposite shape. One workflow. One team. One success metric you'd already pay to improve. The co-worker connects to your real data — CRM, docs, tickets, warehouse — and drafts work that a human approves before anything ships externally. Where the data isn't yet shaped for AI, that prep is part of the engagement — not a six-month precondition you have to deliver first.
"The co-worker doesn't replace anyone. It removes the boring half of one role, under approval, so the team can spend more time on the half that matters."
Four stages, one calendar. Each stage has a single deliverable and a single decision — continue or close.
Discovery — find the one workflow.
Two to three working days. We talk to the team that owns the problem and the team that owns the data. We don't talk to procurement yet — that's stage three.
What we do
- Walk through the candidate workflow with the people who run it today, end to end. Stopwatch in hand.
- Identify the single repeatable decision the co-worker would draft. Examples: "which accounts are at risk this week?" "what's the right reply to this ticket?" "where did this number come from?"
- Confirm what data the co-worker needs read access to, and what action (if any) it needs write access to — usually nothing, just drafts.
- Pick the success metric. Time saved per draft, reply latency, accuracy versus baseline. One number.
What you get
A one-page memo describing the workflow, the data path, the metric, the approval gate, and the team that will use it in week three. If the data needs shaping, that work goes into week one of the engagement — we build the prep into the same calendar, not as a prerequisite you have to deliver before we start.
Build — connect the ecosystem.
One to two weeks. The co-worker reads from the systems you already pay for and drafts work into a queue the assigned humans approve. Nothing leaves your environment without a signed-off draft.
The co-worker reads from systems you already own. It produces drafts, not actions — humans decide what ships.
What we build during these weeks
- Read connectors against the specific tables, channels, and folders identified in discovery. Least-privilege, audited.
- The drafting agent — answers questions with citations back to the original source row, doc, or message.
- The approval queue — every draft has an explain panel, the source data, a one-click approve, and an edit-and-send path.
- The audit log — who approved what, when, with which data. Always exportable.
What we deliberately don't build
- No silent autonomous actions in week one. Approval gate stays on.
- No new system of record. The co-worker writes back to the systems you already use.
- No model fine-tuning. We rely on the citations and the approval gate to keep quality measurable, not a one-shot training run.
Pilot — three to five days, real users.
The assigned team uses the co-worker against real work, drafts flow through the approval queue, and we measure against the discovery-stage metric. Daily check-ins. We adjust the prompts, the citations, the routing — not the architecture.
By day five we have an honest answer: is the co-worker faster than the previous baseline, accurate enough to keep approving, and trusted enough that the team wants to keep it?
If any answer is "not yet," we name what's blocking it — usually data shape, threshold tuning, or approval-flow friction — and finish that work inside the same engagement. The next stage is then a continuation, not a restart: same co-worker, more workflows, more sources, the same gate.
The same gated loop runs in week three and in year three. Scale comes from more loops, not from removing the gate.
What you keep — after the engagement.
The co-worker, the connectors, the approval queue, and the audit log all run in your environment. We don't lock you into a model, a vendor's runtime, or a proprietary canvas. If you decide to bring the operation fully in-house, the handoff is documentation and a Slack channel — not a migration project.
- The workflow itself, now drafted by the co-worker, approved by your team, with every decision auditable.
- A working pattern for the next workflow. Most engagements expand to a second within the first quarter.
- A measurable baseline for AI ROI inside your company — not a slide, a number.
- The right to shut it off on any working day. Approval gate, audit log, and read-only data path make rollback boring.
One workflow. Two to four weeks.
If you have a candidate workflow in mind, we can usually tell within the first call whether it's a fit. The call is 45 minutes, the assessment is free.