Start small. Prove it fast. Pay for what it delivers.
AI is too important to buy on faith, and too fast-moving to plan for six months before you see anything. So every engagement starts with a two-week proof of concept, and every build after that is priced around results.
Ten working days, four steps.
- 1Days 1–2
Discover
A workshop with your team. We pick one process, agree on success metrics, and set up data access.
- 2Days 3–5
Design
We map the workflow, connect to a sample of real data, and choose the model and guardrails.
- 3Days 6–9
Build
A working AI prototype on your data, with agents, integrations and a simple results dashboard.
- 4Day 10
Prove
A live demo against the agreed metrics.
What you get at the end
A working prototype, results measured against the success criteria, a production architecture and security plan, and a fixed quote with an outcome-based option.
What we need from you
One process owner, sample data or sandbox access, and 3–4 hours of subject-matter-expert time each week.
Pick the model that fits the work.
| Model | How it works | Best for |
|---|---|---|
| 2-Week POC | Fixed fee, 10 working days, credited against the build if you proceed | Proving value before committing |
| Outcome-based build | A reduced base fee plus success fees tied to 2–4 agreed KPIs, tracked live on the Command Center | Automations with clear, measurable results |
| Fixed-scope build | One price for a defined scope and timeline | Well-defined AI applications |
| AI-augmented managed services | A monthly fee tied to SLAs and outcomes, not headcount | Running and improving AI in production |
| Time & materials / staff augmentation | AI engineers embedded in your team | Adding capacity to an internal AI program |
Paid when the outcomes are delivered.
Before we build, we agree with you in writing on two to four measurable outcomes and a starting baseline. Typical examples are invoice cycle time, hours of manual work returned, first-pass accuracy, or the size of a backlog. Results are tracked live on a shared dashboard. When targets are met, success fees apply. When they aren’t, you pay only the base fee. Because we’re paid when the outcomes are delivered, we have the same goals you do.
Questions we hear first.
Who owns what you build?
You do: the source code, prompts, workflows and any models trained on your data.
Does our data leave our environment?
Not by default. We deploy into your cloud tenant or data center and use enterprise model endpoints that don’t train on your data. Fully private, self-hosted models are available where required.
Which AI models do you use?
Whichever fits the job, the budget and your security rules. We design so you can switch models later without rebuilding.
What if the POC shows AI isn’t the answer?
Then you’ve learned it in two weeks instead of six months, and you get a written finding explaining why and what would work instead.