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Custom AI Development Shaped Around Your Process

Custom AI development produces a system shaped around your data, your vocabulary, and your process, instead of a generic product you configure around. That matters when the workflow is your competitive advantage — off-the-shelf tools cannot encode the exceptions your team knows by heart.

  • Private or on-premise deployment
  • Tuned against your real documents
  • Integrated where the work happens
Talk About a Custom AI BuildAll Services

How it runs · 5 steps

  1. Confirm custom is right
  2. Learn your domain
  3. Choose the deployment model
  4. Tune retrieval and prompts
  5. Integrate deeply

Built with

  • Llama
  • Mistral
  • Qwen
  • vLLM
  • Ollama
  • Private VPC
  • On-premise
  • pgvector

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Questions first?Talk to an engineer

Custom is not automatically the right answer

If the task is generic — transcription, note-taking, standard document search — a subscription product will beat a custom build on both cost and time, and we will tell you so. Custom earns its cost when your process is genuinely unusual, when your data cannot leave your infrastructure, or when integration depth is the whole point.

When it is the right answer, the difference shows up in the details: prompts and eval sets written against your terminology, retrieval tuned on your real documents, and output landing inside the system your team already works in rather than in another tab.

The Problem

What this usually fixes

  • A vendor settings screen that cannot express your exceptions

    The case handled differently for one client, the approval that skips a step — the things your team knows by heart and no product has a field for.

  • Data that may not leave your infrastructure

    Contractual, regulatory, or residency constraints rule out a third-party API, and the off-the-shelf options all are one.

  • Generic accuracy benchmarks

    A vendor's published accuracy says nothing about your documents, your terminology, or your edge cases.

  • Output that lands in another tab

    A tool that does not write back into your CRM, ERP, or internal system adds a step instead of removing one.

How We Work

The process

Each step produces something you can review — a document, an environment, or working software — rather than a percentage in a status report.

  1. Confirm custom is right

    We compare against the off-the-shelf options honestly, including total cost of ownership, and recommend the product where it wins.

  2. Learn your domain

    Your terminology, your exceptions, and your real documents — the material the prompts and eval sets will be written against.

  3. Choose the deployment model

    Frontier API, open-weight in your own VPC, or on-premise, decided against your residency and compliance constraints.

  4. Tune retrieval and prompts

    Measured on your documents rather than a public benchmark, because a generic score predicts nothing about your accuracy.

  5. Integrate deeply

    Connected to the CRM, ERP, support desk, or database your team already uses, so results land where the work happens.

What You Get

Why teams choose this

  • It encodes how you actually work

    Including the exceptions that live in your team's heads and cannot be expressed in a vendor's configuration screen.

  • Your data stays where you decide

    Open-weight models in your own cloud or on-premise where residency, contractual, or regulatory constraints require it.

  • Accuracy measured on your material

    Eval sets built from your documents and your vocabulary, so the number means something for your use case.

  • No per-seat ceiling

    Cost scales with usage and infrastructure rather than with headcount, which changes the economics at scale.

Stack

Technologies we build with

Tools we have delivered production work on, not a capability matrix. We pick per project and will explain the trade-off behind each choice.

Open-weight models
  • Llama
  • Mistral
  • Qwen
Serving
  • vLLM
  • Ollama
  • Private VPC
  • On-premise
Retrieval
  • pgvector
  • PostgreSQL
  • Hybrid search
Integration
  • REST
  • Webhooks
  • CRM & ERP connectors
Industries

Who this is for

Custom builds make sense where the process is the competitive advantage, or where the data cannot travel.

  • Healthcare
  • Financial services
  • Insurance
  • Legal & professional services
  • Manufacturing
  • Logistics
  • Public sector suppliers
  • Pharmaceuticals
Questions

Custom AI Development: common questions

Related reading

When is custom AI development worth it over an off-the-shelf tool?

Custom is worth it in three situations: your process is a competitive advantage that a configurable product cannot encode, your data cannot leave your infrastructure for contractual or regulatory reasons, or you need integration depth no vendor will build for you. Outside those, a subscription product usually wins on both cost and time to value. Generic tasks — transcription, note-taking, standard document search — rarely justify a custom build, and we will say so during scoping.

Can you run AI models entirely inside our own infrastructure?

Yes. Open-weight models such as Llama, Mistral, and Qwen can run in your own cloud account via vLLM, or fully on-premise where nothing may leave your network. The trade-offs are real and we will state them: self-hosting adds infrastructure cost and operational responsibility, and open-weight models still trail frontier APIs on the hardest reasoning tasks. For extraction, classification, and grounded question answering — the bulk of business use cases — the gap is usually not the deciding factor.

Do we need to fine-tune a model?

Usually not, and it is worth resisting until the cheaper options are exhausted. Better retrieval, clearer prompt structure, and a well-built eval set solve most accuracy problems at a fraction of the cost and without the ongoing burden of retraining as your data changes. Fine-tuning earns its place for consistent output formatting, a genuinely specialised vocabulary, or latency and cost reduction at high volume — and we will tell you which of those applies before recommending it.

Who owns the custom AI system you build?

You do, completely and by contract: source code, prompts, evaluation sets, fine-tuned model artifacts, infrastructure definitions, and documentation. There is no hosting lock-in, no licence fee on your own system, and nothing structurally withheld to make leaving expensive. You can run it in your own cloud accounts from day one. We consider this non-negotiable rather than a concession — an engagement should continue because the work is good, not because the exit is costly.

What are custom AI development services?

Custom AI development services build an AI system around one organisation's data, vocabulary, and process, instead of configuring a generic AI product. That includes retrieval grounded in your own documents, prompts and evaluation sets written against your domain, models selected or fine-tuned for your specific task, private or self-hosted deployment where data residency requires it, and integration into the systems your team already uses. Choose custom when your process is a competitive advantage, when your exceptions cannot be expressed in a vendor's settings, or when your data cannot leave your infrastructure. Choose an off-the-shelf AI tool when the task is generic — transcription, note-taking, and standard document search rarely justify a custom build.
Related

Services that pair with this

Is your process the thing off-the-shelf cannot handle?

Describe the exceptions your team works around today. That conversation usually settles the build-versus-buy question in one call.

Talk About a Custom AI BuildSee Our Work