Your data estate, discoverable, trusted, and AI-ready.
Privacy-first, AI-native metadata and data enrichment, at enterprise scale. Dtechtive turns fragmented, hidden data (whether open, internal or shared) into discoverable, trustworthy, compliant and usable datasets, over 100x faster than manual approaches, with all processing inside your own boundary.
Privacy-first
Sovereign by design
Standards-aligned
Recommended by data-rich enterprises
GovernmentDefenceHealthcareFinancial servicesEnergyTelecomsEnvironmentManufacturingand more
Dtechtive built a strong partnership with the Data Platforms Team, helping improve metadata standards, support a major data migration, and enhance data discoverability while understanding the constraints of public sector clients.
– Senior Business Analyst, The Scottish Government
Through the CivTech programme, the Scottish Government’s Digital Directorate and NatureScot collaborated with Dtechtive to create find.data.gov.scot, addressing the challenge of public sector data discoverability. This innovative solution not only offers enhanced metadata and intelligent search algorithms but also provides valuable insights into user behaviour. The CivTech process enabled a successful partnership with Dtechtive, resulting in a cutting-edge tool for accessing a vast array of Scottish public data.
– Head of Technical Data Policy, The Scottish Government
The Challenge
70%+ of data is hidden and underutilised.
Missing metadata, unstructured formats, no quality signals and compliance gaps prevent humans and AI agents from using data reliably, delaying data-driven decisions and increasing the risk of inaccuracy.
55–80%
Data is “dark”
Stored but never used: no titles, descriptions, tags, quality ratings or privacy flags.
Gartner, IBM, Splunk
£260bn
Wasted annually
The global cost of storing and managing dark data that delivers no value.
DataStackHub, 2025
£9.5m
Per organisation, per year
The average cost of poor data quality to a single organisation annually.
Gartner
15–25%
Of revenue lost
The share of revenue firms lose to bad data across operations and decisions.
MIT Sloan
AI assistants and agents fail
Without a semantic layer of context, AI hallucinates, retrieves the wrong sources, or stalls entirely.
Teams waste over 50% of their time
Data consumers spend more than half their week searching, understanding and preparing data instead of using it.
Stewards lose visibility
Administrators and data owners cannot see what exists, what data teams search for, or what value their data delivers.
Data and AI sovereignty is at risk
Most enrichment tools send data or AI inference to third-party clouds, creating compliance and security exposure.
Traditional catalogues suffer from catalogue rot
When metadata, ownership and quality signals fall out of date, definitions stop matching how teams work. Trust and adoption decline, leaving another stale system of record instead of a reliable route to usable data.
The Solution
Turn hidden data into trusted, AI-ready resources.
Dtechtive transforms private/internal and public/web data, however fragmented, hidden or unstructured, into discoverable, trusted and AI-ready datasets. Months of manual effort become hours, and your data and AI inference never leave your environment.
Any format in, AI-ready assets out, with every step running inside your own environment.
01 – Enrich metadata
AI-native metadata enrichment
Self-hosted open-source models (e.g. Llama) or your own cloud AI generate rich metadata across spreadsheets, databases, maps and documents.
Metadata generation: AI-created titles, descriptions and tags, zero manual effort
Data quality scoring: completeness, validity, consistency, uniqueness, with history
Sensitive information (e.g. PII) detection: at row, field and document level, with privacy flags embedded
Unstructured data becomes structured, analysis-ready and AI-ready, with every step running inside your boundary.
Format conversion: tables extracted from PDF, HTML and Word into CSV or XLSX
Data harmonisation: consistent formats, units, coordinate systems and naming
Sensitive information redaction: compliance-safe, shareable versions of sensitive datasets
Synthetic data: statistically representative, privacy-safe data for AI training
Impact: From Data to Decisions
Connect fragmented data to the experiences people and AI need.
Dtechtive works alongside your existing infrastructure, turning data from cloud, inside your boundary, APIs and documents into trusted resources that power search and analysis.
01
Fragmented data
Cloud · On-Prem · Web
Context-less and locked away across systems, in formats nobody can search.
02
Dtechtive
Metadata + data enrichment at scale
Privacy-first, AI-native processing that slots in beside what you already run.
03
AI-ready data
Catalogued · Tagged · Trustworthy
Discoverable, usable and governed across the whole data estate.
Contextual search
Natural-language semantic search across the full estate, with ranked results and filtering by quality, privacy, domain, owner and recency.
AI summary
Generated overviews surface the most important context above search results so teams understand relevant data faster.
Conversational search & analysis
People and AI agents can ask natural-language questions and receive grounded answers without code or dashboards.
Why Dtechtive
Positioned in the market based on your needs
Most tools cover one half of the problem, in someone else’s cloud. Dtechtive covers both halves, inside yours.
Open, internal and shared data
We work across public/web, private/internal and shared data estates, using the same standards and approach either side of the firewall.
Data and AI sovereignty
Solutions are fully deployed inside the client boundary: inside your boundary, or air-gapped. Data never leaves your environment, and AI inference runs there too.
Metadata and data enrichment
We cover both, unlike other players who do one or the other. Context and the underlying data can both be enriched at scale.
Plugs in, no vendor lock-in
An API-based modular tool that integrates with your existing catalogues, BI tools and warehouses. Open standards keep your metadata portable.
Case Studies
Live engagements across government, research and enterprise.
Over 100,000 datasets being enriched using privacy-first AI, from national open data to internal estates.
Privacy-by-design architecture. Your data and your AI inference never leave your boundary.
GDPR compliant
Privacy-by-design throughout, with data protection considerations built into every stage of enrichment and cataloguing.
Data sovereign
Self-hosted or deployed in your own cloud tenancy (AWS, Azure, GCP). No third-party SaaS data transit.
AI sovereign
Self-hosted Llama or bring-your-own cloud AI (Bedrock, Azure OpenAI, Vertex AI). No data sent to commercial LLM APIs.
Certifications
Penetration testing and CyberEssentials certification underway for full enterprise assurance.
Pricing
From free demo to full production.
Pricing covers delivery stages rather than individual processing runs: enrichment runs take hours once deployed, depending on dataset volume and complexity; the pilot is 1–3 months, while integration, security review and rollout are separate months-long stages within the 6–24 month implementation.
01 · Evaluate
Enterprise free demo
Experience in 30 minutes
Free
Demonstration using public data and selected AI features. Zero commitment.
On top of the base fee, usage scales across dataset volume (tiered by format complexity), AI processing credits (client cloud-native or client self-hosted), data connectors (structured, documents & BI, GIS & enterprise), user licences (Data Consumer, Data Steward/Owner, Admin), and a deployment fee based on hosting model. Optional add-ons: PII redaction, conversational search, synthetic data, and MCP server.
FAQ
Common questions
Straight answers on capability, sovereignty, standards and cost.
What is AI-native metadata and data enrichment?
AI-native metadata and data enrichment uses AI models to automatically generate titles, descriptions, tags and quality scores for datasets, and to transform the underlying data into structured, analysis-ready formats. Dtechtive does both, where most tools cover only one, so an entire data estate becomes discoverable, trusted and AI-ready without manual effort.
What is data and AI sovereignty?
Data sovereignty means your data never leaves your environment. AI sovereignty means AI inference also runs inside your boundary rather than being sent to a commercial LLM API. Dtechtive delivers both: it is deployed inside your boundary, using a self-hosted open-source model such as Llama, or your own cloud AI such as AWS Bedrock, Azure OpenAI or GCP Vertex AI.
How much faster is Dtechtive than manual data enrichment?
Dtechtive is over 100x faster than manual enrichment. Manual metadata and data enrichment takes a skilled person at least one day per dataset; Dtechtive completes it in under a minute. Large estates that would take months to enrich by hand can be completed in hours once deployed, depending on dataset volume and complexity. Pilot, integration, security review and rollout are separate months-long stages.
Does Dtechtive replace our existing data catalogue?
No. Dtechtive uses an API-based modular architecture that plugs into existing catalogues, BI tools and data warehouses. There is no rip-and-replace and no vendor lock-in. Metadata is output in open standards including DCAT3, Croissant, Schema.org, Dublin Core and INSPIRE, so it stays portable.
Which data formats does Dtechtive support?
Dtechtive works across spreadsheets and tabular data (CSV, XLSX), databases, documents (PDF, Word, HTML), geospatial and GIS formats (SHP, KML, GeoJSON, TIFF), and structured exchange formats (XML, JSON). It also extracts tables from unstructured documents and converts them into analysis-ready spreadsheets.
Is Dtechtive GDPR compliant?
Yes. Dtechtive is built with privacy-by-design architecture, with data protection considerations built into every stage of enrichment and cataloguing. It detects sensitive information at row, field and document level, embeds privacy flags in metadata, and can redact or anonymise it. Because everything runs inside your own boundary, your data never transits third-party infrastructure.
How much does Dtechtive cost?
Dtechtive starts with a free demo you can experience in 30 minutes. An enterprise pilot covering up to 1,000 datasets costs £10,000–£25,000 over 1–3 months. Full implementation and the annual licence are both priced bespoke: a base fee covers core platform access and support, with modular usage costs for dataset volume, AI processing, connectors, users and deployment.
Can Dtechtive work with open, internal and shared data?
Yes. Dtechtive works with public/open, private/internal and shared data. It powers find.data.gov.scot, covering 25,000+ open datasets across 70 Scottish public-sector data portals, and also enriches internal estates behind an organisation’s firewall.
Get in touch
AI readiness, simplified.
See Dtechtive running on public data over a free demo, then scope a pilot on your own estate. No preparation needed.
Dtechtive is also on a mission to make open and commercial data more discoverable, trustworthy and AI-ready for everyone – not just inside the enterprise. Explore our public Web Data Search Engine.
