Put AI where the work actually is
Most AI projects fail because they add a chat box next to the problem instead of removing the step that costs the hours. We start from the workflow.
The problem
Your team spends hours on work that follows a rule. Somebody reads a document, copies four fields into a system, checks it against a policy and forwards it. It is not hard work. It is expensive work, and it does not scale with headcount.
You are probably here if
If three or more of these are true, this pillar is where your constraint lives.
- 01A shared inbox is the real system of record
- 02New hires need six weeks before they can process a case alone
- 03The same three questions consume most of support
- 04Reports are assembled by hand every month
- 05An AI pilot ran last year and nothing shipped
What we actually do
AI copilot
An assistant inside your product or your internal tool, grounded in your own data, with a review step before anything reaches a customer.
Document and case processing
Extraction, classification and routing for invoices, claims, referrals or contracts. Confidence thresholds decide what a human sees.
Agentic workflows
Durable multi step jobs with retries, audit trails and a kill switch. Built on a queue, not on a prompt chain.
Private deployment
Inference inside your VPC or tenancy when data cannot leave. Local embeddings, no retention, no third party training.
How it runs, and where you can stop
Each phase ends with something you own. Stopping after any of them leaves you better off than before it.
- 011w
Map
Shadow the workflow. Time every step. Find the step that actually costs the hours.
- 022w
Prove
One workflow, one measurable baseline, running against real data in a sandbox.
- 033-5w
Harden
Evaluation set, guardrails, review queue, observability, rollback.
- 041w
Hand over
Runbook, ownership, and the metrics your team will watch after we leave.
What you receive
Artifacts, not a slide deck. Each one is usable by your team without us in the room.
- Workflow map with measured baseline timings
- Working system in your environment
- Evaluation suite with pass thresholds
- Review queue and escalation path
- Observability dashboard and alerting
- Runbook and named internal owner
What this is usually built on
One engagement from this pillar
The chatbot we talked them out of building
Losing deals to response time, with every proposal assembled by hand from prior engagements.
Read the full case- Turnaround
- 5.5 days5 hours
- Hours returned
- 058
- Win rate
- 1.0x1.11x
Six months post launch against the prior six. Source: CRM proposal log, May 2025
Not a fit if
We would rather lose the engagement on this page than at week six.
- You want a demo for a board meeting rather than a system in production
- The workflow has no owner who can approve a change to it
- There is no measurable baseline and nobody wants to establish one
Asked on almost every first call
Which model do you use?
Whichever is cheapest for the accuracy the workflow needs. We benchmark on your data before choosing, and the system is written so the model is swappable.
Will our data train someone else's model?
No. We use zero retention endpoints, or run inference inside your own tenancy when the contract requires it.
What happens when the model is wrong?
Confidence thresholds route uncertain cases to a human queue. Nothing reaches a customer without passing the threshold or a review.
How do you price this?
Fixed price per phase. You can stop after Prove if the baseline does not move.
Can our team maintain it?
That is the point of the hand over phase. If your team cannot run it without us, we did the job badly.
Bring the constraint, not the brief
The first call is 45 minutes and free. If ai and automation is the wrong pillar for your problem we will say so and point you at the right one.