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Geeks & NomadsAI for Businesses

Service 02 · 4–6 weeks to production

Custom AI Software Development

LLM applications, agentic workflows and AI features engineered into your own product.

The problem it solves

“We know what we need built. Nobody we have spoken to can actually build it.”

Why it persists

Most AI on offer is a thin wrapper around somebody else’s tool, demonstrated on a happy path and unowned the moment it meets real data. Production AI is a different discipline: retrieval that stays correct as the source changes, evaluations that catch regressions before users do, guardrails sized to the risk, cost control, and an upgrade path for the day the model underneath you is deprecated.

THE PATH OF ONE REQUEST IN PRODUCTIONRequestRetrieveModelGuardrailsYour appEval gate in CIREGRESSION BLOCKS THE RELEASESENT BACKINSTRUMENTED FROM DAY ONETracing on every callCost per transactionLatency budgetAudit log

Capabilities

What sits inside this practice.

Named individually, so you can scope, budget and buy them separately if that is what your situation calls for.

01

LLM application development

TypeScript and Python services on Claude, GPT, Gemini or open-weight models you host, built to your architecture rather than to a vendor’s.

02

RAG and knowledge systems

Hybrid retrieval across your documents and databases, citation enforcement, refusal design, and re-indexing that keeps up with the source of truth.

03

Agentic workflow automation

Multi-step work that plans, calls your systems, retries sensibly, and stops for a person wherever being wrong is expensive.

04

Document and data extraction

Messy real-world documents into validated structured data, pushed into your ERP or CRM, with an accuracy baseline and an exception path for the rest.

05

AI features inside your product

Shipped behind your API, in your repository, through your release process. We work to your definition of done, not ours.

06

Evaluation, observability and cost control

Eval harnesses, regression gates in CI, tracing, per-tenant cost ceilings, and model migration handled when providers deprecate versions.

What lands

Fixed scope, agreed up front.

Timeline

4–6 weeks to production

Who signs off

CTO · VP Engineering · Head of Product

Best for

Teams with a defined problem, real data, and an engineering standard we have to meet.

  • Working software in your repository, not a prototype in ours
  • An eval suite and an accuracy baseline you can hold us to
  • Guardrails, approval gates and audit logging sized to the risk
  • Cost telemetry against a ceiling agreed before launch
  • Architecture documentation and a handover your engineers can pick up

What changes

AI that survives real users, real data, and the day the model underneath it changes.

The number that moves

Accuracy against threshold · cost per transaction · latency · regression rate

We baseline this before we start, so there is something to judge us against later. If it does not move, that is a conversation we will start rather than avoid.

Not sure this is the one you need?

That is what the free diagnostic is for. We look at how your business actually runs and tell you which of the six would move your numbers — and which we would leave alone.