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Service

AI & machine learning built for production, not the pilot.

LLM applications, retrieval and agent systems, predictive models and computer vision, built with evaluation, monitoring and cost controls from the start.

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What it is

Most AI projects stall between a promising demo and a system people can rely on. We build for the second stage from the start: measurable quality, predictable cost and software your team can operate.

A production AI system with evaluation, monitoring and cost controls built in, not a demo that stalls after the pilot. Most clients start with a fixed-fee Discovery, then move to a dedicated pod.

What you get

A production AI system with evaluation, monitoring and cost controls built in, not a demo that stalls after the pilot.

  • A use-case assessment with agreed success criteria, before any build starts
  • A production LLM, retrieval (RAG) or agent system connected to your data and tools
  • An evaluation suite that measures quality before and after every change
  • Monitoring for quality, latency and spend, with cost controls
  • Predictive or computer-vision models packaged for cloud or edge deployment
  • An MLOps pipeline and documentation your team can run

How we deliver

Small, verified steps. Your tools, your time zone.

  1. Discovery

    We map the use case, the data available and the risks, and agree how success will be measured.

  2. Prove it on real data

    A working prototype is tested against an evaluation set built from your data, so decisions rest on evidence.

  3. Production hardening

    Guardrails, monitoring, cost controls and deployment, in your cloud and your repositories.

  4. Hand over or evolve

    We keep improving the system, or hand it to your team with documentation.

Related case study

Close-up of machined metal engine components and gears

Manufacturing · India

AI visual quality inspection on the production line

Problem
Castings and machined parts needed consistent, traceable visual quality inspection on the line.
What we delivered
Deep-learning inspection where every decision is linked to its images, model version and confidence score.
  • PyTorch
  • Edge inference
  • MLOps

Technology we use

  • Python
  • PyTorch
  • LangGraph
  • PostgreSQL
  • AWS
  • Azure
  • GCP
  • Kubernetes

We work in your cloud accounts and repositories, with access you grant and can revoke.

Related guides

Read before you scope it.

All guides

Questions

About ai & machine learning

How do we know if an AI use case is worth building?

That's what Discovery is for. In a short, fixed-fee engagement we look at the problem, the data and the risks, and agree how success will be measured before you commit to a build.

Who owns the models, prompts and code?

You do. As with all our work, intellectual property is assigned to you and everything is committed to repositories you control from day one.

Tell us what you're building.

A senior engineer, not a salesperson, replies within one business day. NDA available before any detailed discussion.

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