Document-heavy operations
Contracts, claims, invoices and reports read, classified and extracted into structured fields, with low-confidence cases escalated rather than guessed at.
Service
AI Development Services
AI is one of the things we build, not the only thing. It sits alongside the backends, applications and infrastructure we deliver, and it is engineered the same way: with a defined problem, a measurable outcome, and someone accountable for it once it is live.
That framing matters, because the hard part of an AI product is rarely the model. It is deciding which decision the system is allowed to make, getting the right context in front of it, wiring it into software that already has users and permissions, and knowing on any given Tuesday whether the quality has slipped.
We take AI work from a first conversation about the idea through to a system running under real load — and then keep tuning it, because an AI system that is never re-measured is one that is quietly getting worse.
AI capabilities we deliver
Custom AI Application Development
Complete products where the intelligence is the point — an interface, a backend and a model working as one system, built for your workflow rather than configured out of a generic tool.
Generative AI Development
Systems that draft, summarise, translate, extract and rewrite against your own material, with the structure, tone and review step the output has to pass before anyone acts on it.
Agentic AI Development
Software that plans a task and calls tools to complete it, with a narrow permission surface, durable state across steps, and hard limits on how far a single run can go.
LLM & RAG Development
Retrieval pipelines built around how your content is actually queried — chunking, hybrid search, re-ranking — plus prompt design and the honest answer when nothing relevant was found.
Machine Learning
Classification, forecasting, ranking, scoring and anomaly detection on your own data, including the unglamorous parts: labelling, feature pipelines, drift detection and retraining.
NLP & Computer Vision
Entity extraction, classification and search over text; detection, OCR and quality inspection over images and video — trained or adapted for the documents and footage you actually hold.
AI-Powered Automation
Manual, repetitive work moved into software: intake, triage, routing, reconciliation and approvals, with a human kept in the loop wherever the cost of being wrong justifies it.
AI Integration
AI added to the product you already run. Existing schema, existing authentication, existing release process — no parallel system for your team to maintain alongside the real one.
AI Backend Development
The services behind the model: queues, streaming, caching, rate limiting, provider fallback and per-tenant isolation, so AI traffic scales without destabilising everything else.
AI/MLOps & Deployment
Versioned models and prompts, evaluation suites running in CI, staged rollouts, tracing, cost attribution per feature and alerting on quality regressions — not just on uptime.
Idea to production, end to end
The same six stages carry every AI engagement. You can join at any one of them; we do not require that you start at the beginning.
Idea
Turning an ambition into a scoped problem: what decision the system makes, what a good answer looks like, and what it is worth when it is right.
Architecture
Choosing the model, the retrieval strategy and the data path, and writing down the constraints — latency, cost per call, residency — before any of them become expensive to change.
Development
Building the services, pipelines and interfaces, with an evaluation set from the first week so progress is measured rather than described.
Integration
Wiring the model into the systems you already run — your auth, your permissions, your database, your existing product — so it behaves like a feature and not a bolt-on.
Deployment
Shipping to your cloud or ours with versioned models, staged rollout, tracing on every call, and a rollback that works when the new prompt turns out to be worse.
Optimization
Tightening quality, latency and spend once real traffic exposes what the pilot could not: the odd inputs, the expensive paths and the cases nobody anticipated.
Where it earns its keep
The engagements that pay for themselves tend to share a shape: high volume, repetitive judgement, and a cost attached to every hour the work sits in a queue.
Support that resolves
Assistants grounded in your product data and ticket history that answer, act and hand over cleanly — measured on resolution, not on deflection.
Internal copilots
Search and question answering across private knowledge, respecting the same per-user permissions as the systems the content came from.
Forecasting & decision support
Demand, pricing, risk and churn models that produce a number an operator can act on, with the confidence interval shown rather than hidden.
Back-office automation
Intake, routing, reconciliation and approval chains executed by software, with an audit trail detailed enough to satisfy the people who ask about it later.
Vision & inspection
Detection, counting, OCR and defect inspection on images and video streams, deployed to the cloud or to hardware sitting next to the camera.
Technologies we work with
Chosen per engagement against your latency, cost and data-residency constraints, and kept behind an interface so a change of provider stays a configuration decision.
Models & providers
- Anthropic Claude
- OpenAI
- Google Gemini
- Llama
- Mistral
- Qwen
ML & AI frameworks
- PyTorch
- scikit-learn
- Hugging Face
- LangChain
- LlamaIndex
- vLLM
- ONNX Runtime
Retrieval & data
- PostgreSQL + pgvector
- Qdrant
- Pinecone
- Elasticsearch
- Redis
- Apache Airflow
Serving & infrastructure
- Python
- FastAPI
- Node.js
- Docker
- Kubernetes
- AWS
- Google Cloud
- Azure
Evaluation & observability
- MLflow
- Weights & Biases
- OpenTelemetry
- Grafana
- Custom eval harnesses
Start from the decision, not the model
The most useful question at the start of an AI project is not which model to use. It is which decision, currently made by a person, you want a system to make or prepare — and what it costs when that decision is wrong.
Answer that and most of the architecture follows. A decision that is cheap to reverse and expensive to delay can run automatically. A decision that is expensive to reverse gets a person in the loop and an interface built for reviewing quickly. The same model can serve both; the surrounding product cannot.
It also tells you when not to use AI at all. A good share of what arrives described as an AI problem is a reporting problem, a data-quality problem, or a workflow nobody has written down. We would rather say so in week one than bill for a model that was never the constraint.
Buy, adapt, or build
There are three honest answers to any AI requirement, and only one of them involves us building much. Sometimes an off-the-shelf product already does it, and the right advice is to buy it and spend the budget elsewhere.
More often the work is adaptation: a general model, your data and context, and an application built around it. This is where most business value sits today, and it is far cheaper and faster than training anything from scratch.
Training or fine-tuning a model of your own is the rarest case. It earns its cost when you have a genuinely proprietary dataset, a narrow repeated task, or a latency and unit-cost target that hosted models cannot meet. We will make that case with numbers before we make it with enthusiasm.
Launch is the middle of the project
Conventional software degrades visibly. An AI system degrades quietly: the inputs drift, an upstream document format changes, a provider ships a new model version, and the answers get slightly worse without a single error being logged.
So we build the instrumentation before the launch, not after. A fixed evaluation set that runs in CI, tracing on every model and tool call, cost attributed per feature, and alerts that fire on a quality drop rather than only on downtime.
Optimization then has something to work against. Routing the routine cases to a cheaper model, caching what repeats, tightening retrieval where recall is weak, and trimming the prompt paths that turned out to carry most of the spend.
Frequently asked questions
We have no AI in production yet. Where do we start?
With one workflow that is high volume and currently manual. We scope it, build it against a real evaluation set, and put it in front of users behind a flag. A first useful version is usually weeks rather than quarters, and it teaches you more about your data than any amount of planning will.
Can you add AI to our existing application, or does it need a rebuild?
Almost always it can be added. AI integration is the common case for us: your current schema, authentication and deployment process stay as they are, and the AI capability arrives as new services behind your existing API. A rebuild only makes sense when the underlying system was already due one.
Does our data get used to train anyone's model?
No. We configure providers with training and retention disabled, and where policy or residency requires it we deploy open-weight models inside your own cloud or on-premise environment instead. The trade-offs in capability and running cost are laid out before that decision is made, not after.
How do you know whether the AI is actually working?
Through an evaluation set built at the start: real inputs with known-good outputs, scored automatically and run on every change in CI. That plus per-feature tracing means a prompt or model change is measured rather than argued about, and a quality regression is caught before your users report it.
Who owns the models, prompts and code?
You do. Everything we produce — application code, prompts, evaluation sets, infrastructure definitions and any trained weights — is yours, in your repositories and your cloud accounts, with documentation written for the engineers who will maintain it after we hand over.
Have an AI idea that has to survive production?
Tell us the workflow you want to change and what it costs you today. We will come back with an honest read on whether AI is the right tool for it, and what the first version would take.