It sits on top
Participants keep the systems, vendors and reporting obligations they have today. A copy of an agreed set of information flows through one simple connection. The hard work happens above their operations, not inside them.
Data flywheels, the insight layer and the platform
Data that gets smarter every revolution.
A data flywheel is a self-reinforcing system where data usage generates better data, which enables better decisions, which creates more data. This page covers the whole of it: the loop, the shared insight layer that runs it across a market, and the federated platform underneath.
Insight
Collect
Enrich
Apply
Learn
Compound
What it is
Data that compounds. Like interest.
Most organisations treat data as a byproduct, something to store and occasionally report on. A data flywheel is the opposite: a deliberately designed system where every interaction, decision, and output makes the system smarter and more valuable.
The mechanism is self-reinforcing. Better data leads to better models. Better models lead to better decisions. Better decisions generate more and higher-quality data.
We've built data flywheels for e-commerce platforms that personalise at scale, for B2B businesses that predict churn before it happens, for logistics operators that reduce waste through feedback loops, and for SaaS companies that make their product smarter with every user session.
The result isn't just better analytics. It's a structural competitive advantage that grows harder to replicate over time. The flywheel, once spinning, becomes a moat.
How it works
Four stages. One perpetual loop.
01
Collect
Instrument your data sources
We audit existing data assets and instrument new collection points, capturing the signals that matter, cleanly and consistently.
02
Enrich
Build models that learn
Raw data becomes intelligence. We design enrichment pipelines and ML models that improve with each new data point.
03
Apply
Activate insight in workflows
Insights surface where decisions are made: in products, dashboards, and recommendations that act, not reports that sit unread.
04
Learn
Feed outcomes back in
Every decision outcome loops back into the system, refining models, improving collection, and tightening the flywheel with every revolution.
Run inside one company, the loop compounds one company's advantage. Run across a whole market, it becomes the insight layer.
Flagship: the insight layer
Shared intelligence from the data your market already collects.
Every organisation in a regulated market already collects the information that matters. Each collects it differently and keeps it to itself, so nobody can see the market as a whole. Flywheel adds an insight layer above the systems operators already run. No one replaces anything. One connection each, a complete view for the regulator, and useful intelligence back for every participant.
The problem
The whole is invisible.
Picture a few dozen operators answering to the same oversight body. All track broadly the same things, but each defines them its own way, reports on its own schedule, in its own format.
So a pattern spread across two or three operators looks like nothing to each of them individually. The oversight body receives fragments that arrive late and resist comparison. The aggregate view, the only view where the real signals live, is exactly the view nobody has.
Nobody is at fault. Any market whose participants built their systems independently ends up this way. Fixing it takes a layer above them all that speaks one language.
Operator A · fragmentlate · incomparable
Operator B · fragmentlate · incomparable
Operator C · fragmentlate · incomparable
? the aggregate view nobody has ?
How the layer works
One connection. One language. Nothing replaced.
Each participant makes a single connection, comparable to integrations it already maintains. Above the market's existing operations, information is standardised into one set of definitions, and the right view flows back to the right party. The data itself stays where it is.
The market today
Operator A
CSV · monthly
Operator B
XML · quarterly
Operator C
JSON · weekly
Operator D
own schema
Same questions · different definitions · fragments, late
The Flywheel Insight Layer
Sits above, replaces nothing
One connectionOne definitionAnonymised outputs
What comes back
The oversight body
A live, complete view of the whole market, and the evidence its mandate is met.
Each participant
Its own detail set in anonymous market context, and never another participant's data.
Research & public
Population-level knowledge, aggregated and anonymised before it leaves the platform.
Three commitments define it
What the layer promises.
One definition for everyone
Standardisation is the unglamorous source of most of the value. Once every participant's information speaks a common language, the same measure means the same thing everywhere, and comparison becomes possible. No participant can create that alone; no off-the-shelf product supplies it.
Everyone sees what they should, nothing more
The oversight body sees the whole market. Each participant sees its own detail alongside anonymous market context, never another participant's data. Anything released further is aggregated and anonymised before it leaves the platform.
Why combined is worth more
The value compounds. That is the flywheel.
One organisation's data answers operational questions about its own platform. Standardised and combined, the same data answers better ones: where each participant actually stands against the real market, and what is building that no one inside a single company can see yet.
Each participant that connects makes the shared picture sharper for everyone already in it. The more who take part, the more each of them gains.
participants in · picture sharpens
What each party gets
Every seat at the layer sees value.
The oversight body
- A live, complete view of the market it is responsible for
- Consistent, whole-of-market evidence behind every claim
- Signals that only exist in aggregate, found earlier
Participants
- Insight as a service: their own picture, in market context
- Early signals no single operator could generate alone
- A simpler reporting relationship: information flows once
Researchers & public agencies
- Anonymised, population-level knowledge in consistent form
- Evidence for policy built on a shared standard
- Access that improves as the market grows
Engagement tiers
Three shapes of engagement. Every one priced to the market it serves.
The layer is a managed service, not an infrastructure project, so cost tracks the number of participants, the depth of the insight set and the regulatory obligations in play. That makes every engagement bespoke. Tell us the shape of your market and we will come back with a firm number.
Tier 01
Foundation pilot
A small founding group of operators proving the layer on one insight area.
- Connection built for each founding participant
- One standardised insight set, agreed together
- Private market context returned to every participant
- Whole-market view for the oversight body
- Fixed pilot window with a clear success measure
Bespoke
Scoped on enquiry
Tier 02
Market programme
A jurisdiction moving from pilot to standing operation across most of the market.
- Onboarding for the wider operator base
- Expanded insight sets as coverage grows
- Reporting cadence aligned to regulatory obligations
- Anonymised aggregate access for approved research
- Ongoing stewardship of definitions and standards
Bespoke
Scoped on enquiry
Tier 03
Jurisdiction platform
Regulators and market bodies adopting the layer as durable public infrastructure.
- Multi-jurisdiction deployment on a shared standard
- Governance model, audit trail and access controls
- Custom insight areas beyond the initial application
- Integration with existing supervisory reporting
- Named team and defined service commitments
Bespoke
Scoped on enquiry
No published price list. Pricing depends on participant count, data volume, jurisdiction and reporting obligations, and we would rather quote something accurate than something generic.
How to get started
From first conversation to first signal.
Introductory call
30 minutes
We listen to how your market is structured, who would participate and what question you most need answered.
Scoping and pricing
1 to 2 weeks
We map participants, data availability and obligations, then return a written scope with firm pricing for your situation.
Founding group connected
Weeks, not quarters
Each participant makes one straightforward connection. Nothing changes in their systems and no new work lands on their teams.
First signal live
Ongoing
The layer starts returning market context and whole-market signal, then sharpens with every participant that joins.
The platform
Discharge the obligation. Keep the asset it produces.
A federated data platform for regulated markets. Four components sit inside each member company, an open protocol layer connects them, and a cooperative network turns the evidence they produce into shared intelligence.
Federated by designMember-set trustBuilt for regulated verticals
Why now: the agentic shift behind the platform →The platform is delivered as Attesia, the product arm of Flywheel Ventures Group: three products that turn obligation into evidence and evidence into an asset.
Explore Attesia →Alongside it, the group's M&A advisory arm helps partner companies build and scale inside the ecosystem and advises on transactions from an operator's seat.
Explore M&A Advisory →And the Build & Scale arm co-builds data products and commercial infrastructure alongside portfolio and partner companies, from dormant asset to live revenue line.
Explore Build & Scale →Architecture
Simple from the outside. Deliberate underneath.
Stage one runs entirely inside your business: your data never leaves until you decide it should. Stage two connects you to the wider network on terms you set. The detail of how that works is something we walk through in person, under NDA, once we understand your market.
The trust spectrum
Every dataset sits at a trust level you choose.
Sharing on the platform is never binary. Members decide, dataset by dataset, how open each one is, and three principles hold no matter what they choose.
Set per dataset
Trust is never all or nothing. Each dataset carries its own level of openness, chosen by the member that owns it, and different datasets can sit at different levels at once.
Consent from the start
Every data point carries a machine-readable consent record from the moment it is ingested, so what may be done with it is never a matter of interpretation after the fact.
Reversible by design
Nothing changes without the member's explicit decision, and any level of sharing can be withdrawn. Control stays with the party the data came from.
The cooperative principle
Every data broker keeps the proceeds. We share them back.
Members contribute data they control, receive collective intelligence in return, and earn a revenue share when their consented data creates value for external buyers. The revenue flows back to the members who created it. That difference is the whole point of the cooperative model.
Your records have market value
The routine operational data your systems already capture is exactly what the next generation of AI is being built on. Most companies are sitting on it without knowing what it is worth.
You keep control
Every dataset is shared only at the trust level you set, under terms you approve. Nothing moves without your consent, and everything is logged.
We do the work
Scoping, de-identification, structuring, governance and delivery all happen on our side. You bring the data as it exists today; we take it from there.
The engagement model
Four phases, from first paid engagement to membership.
You can stop after any phase and still hold something useful. Each one is priced on its own and produces an asset, not a recommendation.
Assessment
Health, Opportunity, Appraisal
A senior-level engagement that produces a clear picture of data-estate quality, regulatory exposure and what the asset could be worth. You keep the finding either way.
Roadmap
From where you are to where the value is
Short-term remediation executed by us, long-term architecture planned alongside it. Every engagement leaves behind a reusable platform asset rather than a report.
Federation
The platform, inside your four walls
A managed service deployed against your own source systems. The integration burden stays with us rather than your team.
Commons
Join the industry network
Share only what you choose, on terms you set, and take part in the value your consented data creates across the network.
Inside the Assessment
One honest read of your data estate.
The same assessment methodology runs wherever we price a data asset, including the readiness scoring inside the Build & Scale pipeline. One consistent approach, applied wherever we price a data asset.
Run the assessmentWe build first where the regulatory duty is real, the data is rich, and the operators already know each other. iGaming is one such vertical, and a partner channel for the group. The semantic layer built in a market like that becomes the pattern for every regulated market that follows.
Where we've built
Industries we've deployed flywheels in.
01
E-commerce & Retail
Personalisation engines, demand forecasting, and customer LTV flywheels that improve with every transaction.
02
B2B SaaS
Churn prediction, product usage intelligence, and expansion revenue signals feeding back into a smarter product.
03
Financial Services
Risk scoring, fraud detection, and client behaviour models that get more accurate with every data point.
04
Operations & Logistics
Route optimisation, waste reduction, and supplier intelligence loops that compound efficiency with every cycle.
05
Media & Content
Engagement flywheels that learn what resonates and feed editorial intelligence back upstream.
06
Professional Services
Client intelligence systems and knowledge flywheels that make your firm smarter with every engagement.
How membership works
Three steps from first conversation to first contribution.
01
Scope
A short, no-commitment conversation about what your organisation collects and what you would consider sharing. You decide what is on the table and what protections apply; nothing is shared until you approve it.
02
Preparation
Identifying details are removed and records are cleaned and structured to the standard the network expects. This work happens on our side, with the governance of every dataset documented as it goes.
03
Contribution
Your prepared data joins the network under the terms you set. You receive collective intelligence back, and a revenue share when your consented data creates value for external buyers.
Every step is covered by a confidentiality agreement before any detail changes hands. Request an NDA first if you would like one in place before we talk specifics.
Questions members ask first
Straight answers before you share anything.
What determines what my data is worth?
Three things: how relevant it is to real buyer demand, how complete and current the records are, and how clearly its provenance and consent can be shown. The data audit gives you an honest read on all three before you commit to anything.
Who can see the data I contribute?
Only the people and systems your chosen trust level allows. Every access across your data boundary is logged, members are never identified to buyers without consent, and you can change a dataset's level at any time.
Does my data ever become public?
No. Data is de-identified and prepared before it is used, it is never published or made publicly accessible, and use is restricted to what the terms you approved allow. Your organisation is never named as a source.
How does the revenue share work?
When your consented data creates value for an external buyer, a share of that revenue flows back to you as the member who contributed it. The split is agreed in your membership terms before anything is shared.
Ready to build?
The sooner the flywheel starts spinning, the faster it compounds.
We start with a data audit: a clear-eyed view of what you have, what you're missing, and what a flywheel could unlock for your business.