End-to-End AI Engineering
Twelve services, from architecture and model training through integration to running the system live. Each stands alone; taken together, the handover cost between them disappears.
AI architecture and model engineering
Enterprise RAG architecture and vector database integration
Standard large language models cannot reach private in-house data, fall out of date, and produce confident answers with no basis in fact when questioned.
See the serviceFine-tuning, LoRA/QLoRA and model distillation
General-purpose models fall short against the jargon of a given sector — law, medicine, finance, manufacturing — or against business logic specific to one company; and running general models in production brings high API and hardware costs.
See the serviceMetric-driven evaluation and comparative model test infrastructure
Model performance is judged by surface-level observation, and once systems are live there is no analytical way to track accuracy and safety rates.
See the serviceSoftware adaptation, legacy systems and microservice architecture
Legacy system modernisation and intelligent API layers
The previous-generation ERP, CRM, mainframe or bespoke database systems a business has run for years — the ones at the centre of its workflows — cannot technically accommodate AI integration.
See the serviceTwo-way AI integration across departments and business software
AI stays a static chat window that only produces text, and never turns into action inside the business software already in use — HR, sales, operations, finance.
See the serviceAI-ready microservice and distributed system architecture
Monolithic AI applications collapse under heavy user load or sudden traffic spikes, cannot scale, and take the rest of the system into deadlock with them.
See the serviceProcess automation, autonomous agents and workflows
Autonomous agents and multi-agent architecture
Complex business processes that are non-linear, carry several decision points and involve more than one department cannot be solved by traditional rule-based automation.
See the serviceIntelligent document processing and complex data pipelines
In logistics, customs, insurance and finance, thousands of documents arrive every day in different formats — PDFs, scans, handwriting, complicated tables — and entering them by hand costs time and money.
See the serviceDecision support and forecasting modules, by department
Decision-makers lose time working through mountains of data, and the analysis they get stays stuck in the past.
See the serviceSecurity, data privacy and LLMOps
On-premise and private-cloud LLM deployment with data security layers
In regulated sectors — finance, healthcare, defence, law — sending sensitive data to third-party APIs creates legal exposure under data protection law.
See the serviceReal-time LLMOps: monitoring, guardrails and rollback
Models in production lose performance over time as the data drifts, produce harmful or inappropriate answers under prompt injection, and there is no way to intervene the moment it happens.
See the serviceCompute, token and infrastructure cost optimisation
As users and queries grow, GPU and API token costs in live AI systems get away from you.
See the serviceHow a piece of work moves through us
Discovery
We write the problem, the data and the success measure together. The output is a measurable target, not a proposal.
Prototype
The riskiest assumption is tested in week one. If it will not work, learning that early is the cheapest outcome.
Production
We ship with monitoring, rollback and a cost budget. A release is routine, not an event.
Operation
We measure, improve and hand over to your team. Dependency is not a business model.
Which one shall we start with?
See it on your own data in one session, or write first and ask what we do.