Established in 1965. Built on practical delivery.
Datam (Data Management Group Ltd) is a UK technology company with a long track record of helping organisations organise data, automate operations, and run with stronger control — using best-fit technology and clear governance.
Our story in brief
A simple timeline of how the company evolved into what it is today.
Founded in North London
The business began in a small North London parade — a ground-floor office with the owner’s accommodation above. Early work supported local clients with practical business services.
Transition and expansion
In 1977 the company transitioned to new ownership and expanded beyond its original service base, focusing on management reporting, systems analysis, and practical automation for small and medium-sized organisations.
Technology-enabled growth for SMEs
As computers, networks, and software platforms matured, Datam applied appropriate tools to reduce manual work, improve speed-to-market, and strengthen control across a range of SME industries.
Product platforms
Datam now builds product-grade platforms that can operate standalone or as a suite: travel operating tools (hotels, experiences, tour templates), enterprise risk governance, and food operations (MageAhara).
Systems that teams can actually run
We focus on adoption and operational reality: clean data models, strict validation, versioning, audit trails, and workflows that match how organisations work.
- API-first design with clear schemas and consistent validation
- Incremental delivery: phase by phase, with measurable outputs
- Governance built-in: roles, approvals, evidence, and traceability
Turning data into outcomes
Whether the goal is faster travel product creation, better enterprise control, or streamlined operations, our approach is the same: strong data foundations + usable workflows.
Governable AI — safe and human-controlled
We apply Daytaem principles so AI remains a tool, not an authority — with explicit governance, decision integrity, and audit-ready evidence.
Structured outputs
AI-assisted workflows are constrained by strict, human-defined schemas (allowed fields, required data, validation rules), making outputs predictable, reviewable, and safe to operationalise.
Validation gates
We reduce hallucination risk by validating outputs, rejecting non-conforming results, and requiring source-linked inputs where appropriate. If it cannot be validated, it does not pass.
In practice this means: schema-first prompts, controlled retry loops for missing fields, evidence logging for what was used and why, and retrieval-augmented generation (RAG) that draws only from curated, approved sources. Where semantic matching is useful, we use vector retrieval (e.g., pgvector-backed search) to find the closest relevant references before generating any suggestion.
Decision integrity
AI may assist and suggest — but it must never decide. A human owner approves outcomes, supported by evidence, controls, and a clear audit trail.
Want the short version?
Tell us your use case and we’ll recommend the quickest path to a working system — proof first, or production build.