Services
Three disciplines — consultancy, development and integration — that carry a project from first business case to a live, monitored system your organisation actually depends on.
Strategy and advisory that separates genuine opportunity from hype. We assess where AI can create measurable value in your organisation, pressure-test feasibility against your data and systems and hand you a costed, prioritised roadmap — vendor-neutral and grounded in what will actually ship.
Structured discovery across your workflows and data. We prioritise use cases on a value-versus-feasibility matrix, model expected ROI and total cost of ownership and sequence a delivery roadmap with clear success metrics.
An honest audit of data quality, availability and lineage; assessment of pipeline, infrastructure and MLOps maturity; and a gap analysis that tells you what needs to be in place before models go anywhere near production.
Reference architecture design and a clear-eyed build-vs-buy analysis — proprietary APIs versus open-weight models, managed platforms versus self-hosted — optimised for cost, latency, data residency and long-term ownership.
Responsible-AI framework, model-risk assessment and data-protection guidance aligned to GDPR and the EU AI Act — so security, auditability and accountability are designed in from day one, not retrofitted.
Focus areas
Custom AI and machine-learning engineering, built to your requirements and taken to production standard. From generative AI and LLM systems to predictive models and the applications around them — real, tested software, not proofs of concept that stall in a notebook.
Retrieval-augmented generation (RAG) over your own knowledge base, vector search and agentic workflows with tool use and function calling. Fine-tuning and parameter-efficient tuning (LoRA/PEFT), structured prompt engineering and evaluation harnesses with guardrails to keep outputs accurate and safe.
Forecasting, classification, anomaly detection and recommendation systems. Feature engineering, model training and validation and rigorous offline/online evaluation — with interpretability baked in so stakeholders can trust the outputs.
The software that wraps the model: full-stack web applications, conversational assistants, internal tools and dashboards. Clean APIs and human-in-the-loop UX so the technology is genuinely usable by the people it is built for.
The pipelines that feed everything: ETL/ELT ingestion, embeddings generation, vector and feature stores and batch or streaming data flows — the unglamorous foundation that makes AI dependable at scale.
Representative stack
Getting AI out of the lab and into daily operations — connected to the tools your teams already use, deployed reliably and kept healthy in production. This is where MLOps, security and adoption meet and where most AI projects quietly fail. We make sure yours does not.
Connecting models into your CRMs, ERPs, data warehouses and internal tools through APIs, webhooks and event-driven pipelines. Process automation that removes manual steps without disrupting the way your teams work.
Containerised deployment with Docker and Kubernetes, CI/CD pipelines for models and prompts, a model registry with versioning and rollback and infrastructure-as-code for reproducible, auditable environments.
Production telemetry for latency, cost and quality; data- and model-drift detection; continuous evaluation; and alerting and logging — so issues are caught early and performance is provable, not assumed.
Role-based access control, secrets management, PII handling and redaction and full audit trails. Private, VPC, or on-premise deployment options where data residency and confidentiality are non-negotiable.
Platform & tooling
VIDA