AI development services that reach production
Most AI work stalls between a promising demo and something a business can depend on. We are hired for the second half.
AI development is the work of turning a model into a system your business can run on — data pipelines, evaluation, guardrails, monitoring and the integration into the software people already use. We build generative, agentic and predictive AI for companies that need it to survive contact with real users, real data and real auditors.
What we build
Four kinds of work, all of them ending in something deployed rather than something demonstrated.
Generative AI and RAG
Assistants and document systems grounded in your own content, with retrieval that is tuned and measured rather than assumed. We build the evaluation set before the feature, so "is it better?" has an answer.
Agentic workflows
Agents that call your tools and APIs to complete real tasks, with explicit boundaries on what they may do, human approval where the cost of being wrong is high, and a full trace of every action for review.
Predictive and classical ML
Forecasting, scoring, routing, anomaly detection and recommendation, where a well-specified gradient-boosted model beats a language model on cost, latency and explainability — and we will say so.
MLOps and platform
Versioned data and models, reproducible training, staged rollout, drift and cost monitoring, and rollback that works. The unglamorous half that decides whether the system is still trusted in month nine.
What changes
The point is not that you have AI. The point is what stops being expensive.
Work that scales without headcount
Triage, extraction, summarisation and first-line response absorb volume growth that would otherwise need hiring — with the exceptions still routed to a person.
Decisions made on evidence
Scoring and forecasting move judgement calls off intuition and onto measurable inputs, with the reasoning visible enough to challenge.
A system you can defend
Logged prompts and outputs, documented evaluation, data-handling you can describe to a regulator, and a named owner for every model in production.
The engagements that go well tend to start from one of these positions.
- A proof of concept works, and nobody is willing to put it in front of customers yet
- You have data and a hypothesis, but no in-house ML engineering to test it properly
- An AI feature shipped, and its cost or accuracy is now the problem
- You need AI capability inside a regulated process, with the audit trail to match
Chosen per engagement. We are not resellers and hold no quota on any of it.
- Python
- PyTorch
- TensorFlow
- Hugging Face
- LangGraph
- Amazon Bedrock
- Azure OpenAI Service
- Vertex AI
- pgvector
- Ray
- MLflow
From first call to scale.
- 01
Discovery
We start by listening — your goals, constraints, and where technology can create real leverage.
- 02
Solution design
Our engineers shape the architecture, scope, timeline, and team before any code is written.
- 03
Build & iterate
We ship in focused increments, measure impact, and refine with you at every step.
- 04
Scale & support
We harden, optimize, and grow the solution — and stay on to support it as you scale.
AI development, answered
The questions that come up in almost every first call.
01 How long does it take to get an AI system into production?
A focused, well-scoped use case typically reaches a production pilot in 8 to 12 weeks: two to three weeks to agree the use case, the data access and the evaluation criteria, then iterative build against that evaluation set. Systems that touch regulated data, or that need a new data pipeline built first, run longer — we scope that explicitly rather than discovering it in month four.
02 Do we need our own data to build something useful?
Not always. Generative systems built on a foundation model plus retrieval over your existing documents need no training data at all. Predictive models do need history — typically a year or more of labelled outcomes. Part of the first phase is telling you honestly which category your problem is in.
03 Which model should we use?
It depends on the task, the latency budget, where the data is allowed to be processed and what the failure costs. We benchmark candidates against your evaluation set rather than defaulting to whichever model is currently in the news, and we design so the model can be swapped without rewriting the system around it.
04 How do you stop an AI system from producing wrong answers?
You cannot eliminate it, so you engineer around it: grounding answers in retrieved source material with citations, constraining outputs to structured formats, checking them against rules before they reach a user, keeping a human in the loop wherever a mistake is expensive, and monitoring in production so quality drift is caught by a dashboard rather than by a customer.
05 Who owns the model and the code?
You do. Source, prompts, evaluation sets, infrastructure definitions and documentation are yours, in your repositories, from the first commit. We build to hand over.
Rarely just AI
AI work almost always arrives attached to the platform it has to run on.
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Custom Software Development
Web apps, SaaS platforms, and APIs engineered for real users and real scale — modern stacks, clean architecture, cloud-ready from day one.
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Mobile App Development
Native and cross-platform iOS and Android apps — fast, reliable, and designed around how people actually use their phones.
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Cloud & DevOps
Migration, modernization, and CI/CD automation across AWS, Azure, and Google Cloud — infrastructure that scales securely.
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Cybersecurity
Assessments, continuous monitoring, and incident response that keep your apps, data, and infrastructure resilient.
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Let's engineer what's next.
Tell us where you want to take your business. We'll map the architecture, timeline, and team to get you there — and reply within 24 hours.