The UK data backbone for AI

Stop your AI hallucinating UK facts. Ground it on one connected data backbone.

Any AI system that needs UK truth — companies, ownership, contracts, places, supply chains, skills — ends up rebuilding the same fragmented integration. The Foundry replaces months of vendor procurement and entity reconciliation with one sovereign backbone of 40+ connected sources, resolved entities and provenance, exposed through stable APIs for training, retrieval, agents and predictive features.

Explore the AI backbone

Evidence, not promises

Every headline claim links to the provenance layer, source trail and live use-case proof point already documented on the site.

Open provenance

Time lost

13 UK-specific benchmark tasks operationalised

Provenance layer: Entity resolution
Source trail: Companies House → Canonical entity spine → Department-ready analytical view
Use-case proof: AI benchmarking and evaluation: AI benchmark answers can be grounded in verified UK entity evidence.

Value at risk

25-40% shorter supplier discovery cycles

Provenance layer: Quality layer
Source trail: Public procurement notices → Spend classification → Procurement intelligence layer
Use-case proof: Strategic procurement: Supplier shortlists can be expanded beyond obvious incumbents.

FOMO

500-2,000 high-potential SMEs surfaced per national scan

Provenance layer: Local intelligence
Source trail: Local economy and workforce signals → Place-linked entity → Local intelligence view
Use-case proof: SME growth discovery: High-potential SMEs can be surfaced before they appear in conventional networks.

Grounded by default

One backbone, every UK fact

40+ authoritative sources joined on a single canonical entity spine — companies, directors, owners, contracts, premises, skills and places — so models retrieve consistent UK facts instead of inventing them.

Provenance on every fact

Citations your auditors will accept

Source, timestamp and confidence travel with every record. Train, retrieve and infer with a per-fact lineage that holds up in regulated, enterprise and public-sector deployments.

Skip the integration tax

Months of plumbing, replaced by APIs

Lookup, search, graph traversal and resumable change feeds — one contract, one schema, one identity layer. Ship copilots, agents and predictive features in weeks instead of years.

17.2M

UK company and business records

A national-scale business intelligence estate ready to be structured for sovereign AI.

40+

authoritative data sources

Corporate, procurement, finance, ESG, property, skills, trade and derived intelligence.

canonical entity layer

A joined identity spine for companies, people, places, ownership and public-sector signals.

benchmark tasks

UK-specific evaluation tasks for AI models, agents and public-sector workflows.

A multi-billion, nation-wide problem

Bad UK data is already costing the country tens of billions a year

Fragmented entity data, unresolved ownership and missing provenance are not a backlog item — they quietly drain procurement budgets, AI investment, supplier resilience and SME growth across every sector. The numbers below are public, conservative and independently sourced.

National cost

£55–81bn

lost to UK public-sector fraud and error every year

NAO estimate for 2023-24 — much of it driven by unresolved supplier identities, undetected ownership links and weak provenance.

Sources & assumptionsv

Citations

How we translate this into UK savings

We use the NAO mid-point of ~£68bn/yr as the headline national cost.
We assume even a 1–3% reduction from better entity resolution and provenance is plausible — that alone is £680m–£2bn recovered per year.
Per large department, scaled by spend share: ~£20–80m/yr recoverable. We round down to a conservative £10–40m/yr in the 'what you save' panel.

National cost

Up to 6%

of UK enterprise revenue lost to poor customer & supplier data quality

Royal Mail Data Services found UK organisations lose up to 6% of annual revenue to dirty contact and entity data. Experian QAS earlier put the aggregate figure at £8bn+.

Sources & assumptionsv

Citations

How we translate this into UK savings

We apply the conservative end of the range (3–6% of revenue) to data-quality waste, not the full 6%.
For a £500m-revenue UK enterprise that is £15–30m/yr of avoidable waste; we attribute ~20–30% of that to fixable entity, ownership and supplier-data issues — i.e. £3–9m/yr.
Saving is recurring, not one-off, because feeds and entities continually drift without a backbone.

National cost

~45%

of every data & AI team's time spent on data preparation

Anaconda's State of Data Science survey (2,300+ respondents) found data scientists spend ~45% of their time loading and cleaning data — duplicated on every UK AI project.

Sources & assumptionsv

Citations

How we translate this into UK savings

We treat 45% of an AI team's loaded cost as 'integration tax' that a sovereign backbone removes.
For a 10-person AI team at ~£120k loaded cost each (£1.2m/yr) that is ~£540k/yr of recoverable capacity, per team.
Larger AI programmes with 5–10 such teams therefore recover £2.7–5.4m/yr in engineering capacity alone.

National cost

£140bn

GVA uplift if UK SMEs closed the productivity / capability gap

Be the Business Productive Business Index (Edition 8, 2025) puts the addressable SME productivity opportunity at £140bn of GVA over three years — held back in part by uneven access to data large firms take for granted.

Sources & assumptionsv

Citations

How we translate this into UK savings

We attribute a fraction (~5–10%) of the £140bn opportunity to data-access inequality between SMEs and large enterprises — i.e. £7–14bn of recoverable GVA.
Public-benefit SME access tiers on the backbone close the data gap without forcing SMEs to license enterprise-grade datasets.
Per SME, this surfaces as winnable tenders, qualified leads and earlier risk signals — typically £20k–£250k/yr of avoidable loss.

Figures are drawn from public reports (NAO, Royal Mail Data Services, Experian QAS, Anaconda, Be the Business). Translations into per-organisation savings are illustrative working assumptions, not guarantees — every figure is shown with its source so you can challenge them.

Where the waste lands

UK regional intensity

National total

£11.0bn / yr

Tap a bubble or a region chip below to see how the waste splits across procurement, risk and AI delivery.

Sector breakdown

London

~£3200m/yr of avoidable waste from fragmented UK entity, ownership and provenance data.

Procurement

30%£960m/yr

Wasted spend, duplicated supplier onboarding, missed SME bids and undetected related-party suppliers.

Risk & resilience

38%£1216m/yr

KYC/UBO rework, undetected concentration risk, supplier insolvency surprises, sanctions misses.

AI delivery

32%£1024m/yr

Engineering time lost rebuilding the same UK entity layer for every model, copilot and agent.

What drives the waste here

Financial services duplicating KYC/UBO graphs at every firm
Government AI programmes per department, not per nation
FinTech & insurtech rebuilding the same supplier risk graph

Regional totals are illustrative working estimates — derived by allocating the national £55–81bn fraud-and-error and 3–6%-of-revenue data-quality benchmarks (see citations above) across regional GVA, public-spend share and AI-cluster footprint. Treat them as order-of-magnitude, not audited figures.

What it means for you

A national problem you pay for in millions, every year.

A typical UK enterprise wastes £3–8m a year on duplicated data integration, failed AI pilots and procurement decisions made on stale or unverifiable supplier records. A single department or large buyer can lose multiples of that to fraud and error driven by unresolved entities.

The Sovereign Data Foundry collapses that waste into one governed backbone — and the saving compounds with every model, copilot and contract you run on it.

Typical enterprise saving

£3–8m / year

Removing duplicated data plumbing across AI, procurement and risk teams.

Per AI project

12–24 months saved

Skip vendor procurement, schema mapping and entity reconciliation entirely.

See the full opportunity cost

Build vs. backbone calculator

Estimate what you save by building on the backbone instead of from scratch

Model the difference between months of bespoke data integration per AI project and a governed backbone you can reuse across every model, copilot and agent.

Scenario inputs

Scratch build timeline30 monthsBackbone activation timeline6 monthsMonthly value delayed / cost at risk (£m)Reusable departmental projects

Time-to-impact

Start from scratch

30m

Build on the backbone

Estimated time saved: 24 months, making impact roughly 5.0× faster.

Cost at risk

£108.0m

Value potentially delayed while duplicated data plumbing is rebuilt.

Value of reuse

£21.6m

Indicative reusable value from applying the same governed foundation across 8 projects.

ROI by department

What the backbone is worth, team by team

Tap a department to see its annual savings range, the metrics that move and a concrete before/after workflow. Every estimate links back to the assumptions panel — no black-box ROI.

Procurement

Cut supplier onboarding from weeks to hours and surface SMEs your competitors miss.

Annual savings

£2.4–6.1m

Per £500m spend, from removing duplicate suppliers, fraudulent ownership and missed SME competition.

Onboarding time

−72%

Pre-resolved entities, ownership and risk flags replace manual KYC and due diligence.

SME bid coverage

+38%

Discoverable SMEs that meet capability + locality filters but were previously invisible.

Payback

<5 months

Typical first-category rollout breaks even before the next procurement cycle.

Example workflow

1. Define the requirement
Before
Buyer issues tender; suppliers self-identify with inconsistent names and registration numbers.
With the backbone
Buyer filters the resolved UK entity graph by capability, locality, financial health and ownership — generating a longlist of eligible suppliers in minutes.
2. Verify supplier identity
Before
Manual lookup across Companies House, sanctions lists, beneficial ownership and ESG registers — repeated per supplier, per tender.
With the backbone
One API call returns the canonical entity with ownership chain, sanctions status, financial signals and licence-bearing provenance per field.
3. Award & monitor
Before
Awarded suppliers re-onboarded into siloed systems; risk re-assessed annually if at all.
With the backbone
Change feeds push insolvency, ownership and sanctions changes into the procurement system within minutes — risk is continuous, not episodic.

How we modelled these numbersv

1–3% recoverable spend from fraud/error reduction (NAO benchmark mid-point).
20–30% of supplier-data work is automatable when entities arrive resolved with provenance.
SME bid uplift assumes a category with >200 active UK suppliers and existing local-first policy.

The missing layer for AI

Models and compute are commoditising. What stays scarce is connected, UK-specific entity intelligence — companies, ownership, contracts, places, supply chains and skills — joined and verifiable. That is the layer the backbone provides.

One contract, every workload

Replace dozens of vendor integrations with a single backbone that powers training corpora, RAG, agent tools, predictive features and evaluation — with the same identities, schemas and provenance throughout.

Deployable in regulated settings

Per-fact lineage, signed snapshots, licence metadata and audit trails make the backbone safe to ship into enterprise, financial-services and public-sector AI — not just demos.

What you lose without it

What every AI team loses by rebuilding the same UK data layer

Whether you're a foundation lab, an enterprise AI team, a startup or a government programme, the gaps are the same — time, trust, coverage and repeatability lost on every project that starts from raw fragments.

Years lost negotiating piecemeal access to partial data estates

Patchy national coverage with inconsistent local and SME visibility

No single provenance chain for explainable AI decisions

Duplicate spend across departments rebuilding the same entity joins

Weak benchmarks because test sets are not grounded in verified UK data

Reduced procurement intelligence, supplier discovery and resilience insight

Missed regional growth signals linking companies, jobs, skills and universities

Higher operational risk from incompatible schemas and unresolved entities

Less reusable infrastructure for national AI assets and enterprise intelligence

Who it helps

Choose your perspective and see the decisions SDF makes possible

The same connected data foundation can serve departments, regions and research teams with tailored evidence, benefits and decision support.

Department

Policy and delivery teams

For central departments that need trusted enterprise intelligence across procurement, security, growth, energy and public-service resilience.

Tailored benefits

Reusable entity intelligence across programmes
Supplier, ownership and risk visibility
Evidence for faster policy and spending decisions

Example decisions

Which suppliers can support a national priority?
Where are hidden ownership or resilience risks?
Which intervention creates measurable local growth?

Department impact cards

Select a department and see the evidence stack it can reuse

Each card connects departmental decisions to the most relevant provenance sources, grant-ready use cases and measurable outcomes already defined in the Foundry.

Sovereign AI Unit

Which UK data layer can support every national AI asset consistently?

A common foundation for model evaluation, enterprise intelligence and trusted AI agents.

Relevant datasets

Derived benchmark tasksAI benchmarking

Use cases

AI benchmarking and evaluationInitial benchmark suite in 10-12 weeks; signed releases every quarter.

Measurable outcomes

Repeatable benchmark scores across priority AI systems.

Built once, reused everywhere

One backbone for every AI workload that needs UK truth.

A consistent entity spine, knowledge graph, provenance trail and governed APIs — so foundation labs, enterprise teams, startups and public-sector projects all start from the same grounded UK context instead of rebuilding it.

Explore the AI backbone See the connected sources