Why does 'testing' matter for AI?

Most AI pilots fail in production because nobody guardrailed or evaluated them. Our founder's background is in software testing and quality engineering, so we treat reliability as the point, not an afterthought.

Which model do you use?

Whichever is smallest and cheapest for the job. A focused task rarely needs the biggest, most expensive model — smaller is faster, cheaper, and easier to keep correct.

Can you work with sensitive or regulated data?

Yes — we design for it, with appropriate handling and guardrails. We'll be candid about what's safe and what isn't for your situation.

Is the chatbot on this site an example?

Yes — it's a lightweight, rule-based assistant running entirely in your browser, with no per-message cost. AI doesn't always mean the biggest model.

— Strategy + Build

AI integration

AI that's tested to work in production — not a demo that dies.

Who it's for

Businesses drowning in repetitive support, data entry, or document lookup
Owners who want AI that works reliably, not a flashy pilot that fails quietly
Teams in regulated or high-stakes settings where wrong answers have a cost

What's included

An honest assessment of where AI helps and where it doesn't
Chatbots, internal copilots, question-answering over your own documents, or workflow automation
The smallest, cheapest model that solves the problem
Guardrails, evaluation, and testing so it behaves in production
Integration into your existing tools and a plan for monitoring it

How we approach it

1. Find the real use case

We start with where AI genuinely saves time or money — and tell you where it's just hype.

2. Build small and grounded

We pick the smallest model that works and ground it in your real data.

3. Test like it matters

Our QA background means we evaluate and guardrail the system before it ever meets a customer.

Timeline: A focused integration ships in weeks; broader automation is staged.
Investment: Quoted per scope. Choosing the smallest model that works keeps both build and running costs down.
Stack & tools: Right-sized AI models · Document search & retrieval · Evaluation & guardrails · Your existing stack

AI integration: common questions

Why does 'testing' matter for AI?: Most AI pilots fail in production because nobody guardrailed or evaluated them. Our founder's background is in software testing and quality engineering, so we treat reliability as the point, not an afterthought.
Which model do you use?: Whichever is smallest and cheapest for the job. A focused task rarely needs the biggest, most expensive model — smaller is faster, cheaper, and easier to keep correct.
Can you work with sensitive or regulated data?: Yes — we design for it, with appropriate handling and guardrails. We'll be candid about what's safe and what isn't for your situation.
Is the chatbot on this site an example?: Yes — it's a lightweight, rule-based assistant running entirely in your browser, with no per-message cost. AI doesn't always mean the biggest model.

Start your AI integration project

Tell us about your business. We'll start with the audit and only build what's worth building.

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Free 30-minute call. No deck, no pitch.