Question 1

How is this different from hiring an internal AI team?

Accepted Answer

Building an internal AI team typically takes 12 to 18 months to hire, onboard, and ramp before it ships anything to production, and good AI engineers are expensive and hard to retain. We have already built these systems on similar stacks for similar use cases, so you get working production code in 8 to 16 weeks. And because you own everything we build, your team can take it over and maintain it once it is live.

Question 2

What stack and models do you build on?

Accepted Answer

We pick the tools that fit the use case rather than forcing a favourite. We work across Anthropic, OpenAI, and open-source models, and build with frameworks like LangGraph, LlamaIndex, and n8n alongside custom Python where it is warranted. Because the architecture is model-agnostic, we can swap or upgrade the underlying model later as better or cheaper options appear, without rebuilding the system.

Question 3

Who owns the code and the intellectual property?

Accepted Answer

You do, entirely. The source code, documentation, and all credentials are handed over and belong to you, with no licensing strings and no dependency on us to keep it running. Your own engineers or another vendor can read, extend, and operate everything. We build it to be handed over cleanly, not to keep you tied to us.

Question 4

How do you handle data security and compliance?

Accepted Answer

We run on your infrastructure or your cloud, never ours, so no customer data leaves your perimeter. The systems are built to align with GDPR and ISO 27001 practices, with logging and access controls that fit your existing policies. Your data is never used to train foundation models, and for regulated workloads we can keep sensitive processing fully within your environment.

Question 5

What if requirements change mid-build?

Accepted Answer

Requirements often evolve once people see the first working version, and that is fine. We handle changes with a written change request, a new fixed price for the delta, and an updated timeline, so there are no silent overruns. Working in short cycles with frequent demos means changes get caught early, while they are still cheap to make rather than after everything is built.

Question 6

How do you make sure the system keeps working in production?

Accepted Answer

Before handover we test against real data and real edge cases, and we build in logging, alerting, and monitoring so you can see how the system behaves once it is live. We document everything and walk your team through running and maintaining it. If you would rather we stay involved, we offer ongoing support, but the point is that you are never stranded if you choose to run it yourselves.

Custom AI builds for when off-the-shelf isn't enough.

Built before the hype. Shipped every time.

Three things that make it production-grade.

Data Foundation

Production Engineering

Handover

Watch your system get built.

Scope

Design

Build

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Systems built for your data and stack.

RAG Systems

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Custom ML Models

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Questions, answered.

Ship the AI build your team can't.