Will we be locked into you afterwards?

No, and the handover deliverables exist to prevent it: documented architecture decisions, a re-runnable evaluation harness, and a named internal owner. If a build leaves no transferable record it has failed regardless of what shipped.

Can you work with our existing engineers?

That is usually the better arrangement. Your engineers know the systems and the exceptions; the knowledge ends up where it has to live long term rather than leaving when the engagement does.

What if the model does not work well enough?

That should be established during a proof of value against a threshold agreed in advance, before a build is commissioned. A development engagement that starts without that evidence is carrying a risk somebody should have priced.

Do you build on open models or commercial APIs?

Whichever the constraints allow. Data residency, latency and cost usually decide it rather than benchmark scores, and for extraction and classification work a smaller self-hosted model is frequently sufficient.

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Custom AI Development

Off-the-shelf solutions don't fit every problem. We build custom AI applications designed specifically for your business.

What We Build

Intelligent Applications

Full-stack applications with AI at their core - from data pipelines to user interfaces.

Custom Models

Fine-tuned models trained on your data. Better accuracy, better results, built for your domain.

LLM Applications

RAG systems, AI agents, chatbots, and content generation tools powered by large language models.

AI Prototypes

Fast proof-of-concepts to validate ideas before committing to full development. See results in 2-4 weeks.

Our Tech Stack

AI & Machine Learning

PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, OpenAI API

Backend

Python, Django, FastAPI, Node.js, PostgreSQL, Redis

Cloud & Infrastructure

AWS, GCP, Azure, Docker, Kubernetes, Railway

Typical timeline

PoC: 2-4 weeks. MVP: 6-8 weeks. Full product: 3-6 months.

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When is custom AI development the right choice over buying?

Custom AI development is the right choice in three situations: the workflow itself is your differentiator and no vendor models it, the data cannot leave your environment for regulatory or contractual reasons, or the available products solve eighty percent of the problem and the remaining twenty percent is where the value sits. Outside those, buying is usually faster and cheaper, and we will say so.

What gets built is a production system rather than a demonstration: a model, the pipeline feeding it, an evaluation harness that can be re-run, monitoring that detects drift, documentation sufficient to defend the system to an auditor, and a handover that leaves a named internal owner.

The part teams underestimate is not the model. It is the surrounding system: how data arrives on a Tuesday, what happens when the service is unavailable, and who retrains it in month nine.

Who this is for

Teams whose process is genuinely non-standard and not served by a product.
Regulated organisations where data cannot leave a controlled boundary.
Companies that have outgrown a vendor tool and know exactly why.
Businesses needing a system they can document and defend to an auditor.

What we need from you

Representative data covering the variation you actually see.
A named owner who will run the system after handover.
The system the output has to land in, and access to it.
A baseline measurement of the process as it works today.

What you get back

A deployed system, not a notebook.
A re-runnable evaluation harness and a held-out test set.
Monitoring for data drift and performance drift.
Technical documentation matched to your EU AI Act tier.
Handover: architecture decisions written down, and a retraining plan.

What a typical engagement looks like

Document intelligence inside a data boundary

Extraction and classification over contracts, referrals or claims, deployed where the documents already live so nothing leaves the environment. Typically the fastest custom build to reach production.

A model where an off-the-shelf product nearly fits

Building only the part the product misses and integrating the rest, which is often a fraction of the cost of replacing the vendor and avoids owning what you do not have to.

A regulated system needing a defensible file

Development where the technical documentation, oversight design and traceability are deliverables alongside the model, because retrofitting them costs more than building them in.

Regulatory and data constraints

If the system lands in the EU AI Act high-risk tier, documentation, logging, human oversight and post-market monitoring are engineering requirements from the first sprint rather than a compliance exercise at the end. Teams that treat them as a final document discover it describes a system they cannot reproduce.

Where personal data is involved, data minimisation and retention are design decisions taken before collection. The GDPR duty to delete and the EU AI Act expectation of traceability pull in opposite directions, and the workable resolution separates the record you must keep from the personal data you must minimise, which only works if it is decided upfront.

Frequently asked questions

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