Question 1

How is this different from buying an off-the-shelf AI tool?

Accepted Answer

An off-the-shelf tool asks you to reshape your process around its assumptions. We do the opposite: we study how your operation actually works, then engineer agents around it. You also get the source code and infrastructure, so the thing you paid for is genuinely yours.

Question 2

What does a typical engagement cost?

Accepted Answer

It depends entirely on scope, and we would rather scope it honestly than quote a number that has to be walked back. A single well-defined automation is a fixed-scope project; an ongoing programme is a monthly retainer. Discovery calls are free, and you will have a real number before any commitment.

Question 3

How long until something is actually running?

Accepted Answer

Discovery and architecture take two to three weeks. First working agents typically land in production shadow mode around week six, with full autonomy following once the evaluation numbers justify it. Simpler automations move faster.

Question 4

Do we need an in-house AI team first?

Accepted Answer

No. Most of our clients have strong operations people and no AI engineers — that is exactly the gap we fill. We hand over runbooks and train your team, so you are not dependent on us to keep things running.

Question 5

What happens to our data?

Accepted Answer

It stays in your cloud, under your keys and your access policies. We deploy into your accounts rather than hosting your operation on ours, and we scope model access to the minimum each agent actually needs.

Question 6

What if an agent makes a mistake?

Accepted Answer

We assume it will, and design for it. Every system ships with confidence thresholds, human approval gates on consequential actions, idempotent and reversible writes, full run traces, and a rollback path. Autonomy is earned incrementally, measured against a live evaluation suite.

Question 7

Which models and platforms do you build on?

Accepted Answer

We are deliberately model-agnostic and route to whatever fits the task, with failover between providers. Frontier models like Claude for reasoning-heavy work, smaller and cheaper models for high-volume classification, and no architectural dependency on any single vendor.

Question 8

Can you work with our existing no-code automations?

Accepted Answer

Yes. No-code platforms are genuinely good for simple, linear flows and we will tell you when to keep one. We are usually brought in when those flows have hit their ceiling — branching logic, real error handling, or judgment that a decision tree cannot express.

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