From diagnosis to a running system, with one team
Timing depends on how many systems the first workflow touches, so we scope it with you before anything is committed. See how we work
- 01Diagnose
AI Audit
Your workflows, data and team readiness mapped in 4 to 8 weeks, ending in a readiness scorecard, an opportunity matrix and a 90-day roadmap. See the AI Audit
- 02Plan
Strategy and roadmap, set in the audit
The audit's final phases rank your use cases, cost the data gaps and set the architecture and governance model before a build is approved.
- 03Prove
AI pilot on your data
The top use case runs against your staging systems for 2 to 6 weeks and ends in a go/no-go report, and the pilot fee is credited toward the build. See the AI pilot
- 04Ship and run
Production build and support
Built into production with error alerts in place, then monitored, tuned and extended. See AI managed services
The four decisions, made for one dental care group's scheduling
A Dutch Odoo implementation partner, building for a dental care group that serves 25,000 dependent residents, spent a year trying to build scheduling natively in Odoo. Read the full pilot
| Decision | What was decided |
|---|---|
| Workflow | Appointment scheduling first: rules for 200 practitioners, multiple locations, treatment series with healing intervals and urgency limits had outgrown the ERP's data model. |
| Data | Odoo stays the system of record. Five sync workflows copy it nightly into a Supabase planning cache. |
| Architecture | The scheduling logic moved out of Odoo into an n8n orchestration layer, where an AI agent runs multi-stage matching. |
| Value | 1,500 appointments a week placed nightly, and every failed placement produces a report that says why, so a planner makes the call. |
Questions leaders ask about AI strategy consulting
Ask us something elseHow is an AI strategy different from an AI roadmap?
Both come out of the AI Audit. The strategy settles the decisions: which workflows change, where data and models live, and how value is measured. The roadmap, the audit's final output, sequences the work those decisions allow into 90 days, with an owner on every item.
Why do agentic AI projects get cancelled, and how do you prevent it?
Gartner names escalating costs, unclear business value and inadequate risk controls. The pilot's go/no-go report projects token cost and total cost of ownership at your volume, the AI Audit's ROI models define the value in advance, and your governance framework sets the guardrails.
Do you sell AI strategy consulting services without the AI Audit?
No. We set strategy inside the AI Audit, because the decisions depend on what the audit finds in your systems and data. The audit takes 4 to 8 weeks, costs $10,000 to $100,000, ends in a ranked 90-day roadmap, and its fee is credited toward your first build.
How do you handle data residency and sovereign AI?
Placement is decided per workload. The context layer enforces access rules at retrieval, and regulated records stay on-premises. Our write-up on context layers for protected healthcare environments applies the pattern to patient data.
When does the first workflow reach production?
It depends on how many systems it touches, and we scope it with you before you commit. For a sense of pace, GoKickflip's multi-agent content pipeline was built and shipped in 30 days. Read the GoKickflip case.
Will our teams be able to run what you build?
Adoption is planned with the build. Our corporate AI training programs are built around the workflows your people use every day, and AI managed services keeps each workflow monitored and extended after launch.
Put your first workflow on the board
Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.
