Problem
Critical decisions still depend on fragmented, hard-to-audit data.
Investors & Partners
One Trusted Data Plane. One flagship evidence market. Multiple regulated workflows later.
3BrainAI is building a reusable Trusted Data Plane for regulated evidence workflows. The first flagship commercial track is CRI: Evidence Packs for construction, infrastructure and real-estate collateral risk monitoring.
We are not betting on one model or a generic AI wrapper. We are building the governed layer that turns fragmented inputs into review-ready outputs with provenance, uncertainty, review state and audit trail.
Product
Trusted Data Plane turns uncertain inputs into trusted records, Evidence Packs and safe action workflows.
Beachhead
CRI serves banks, infrastructure owners, insurers and regulated stakeholders exposed to physical-asset risk.
Business model
Recurring monitored-asset or governed-record workflow subscription, with setup, calibration and escalation.
Expansion logic
DACH-facing validation discipline, selected CEE use cases and broader European rollout after workflow acceptance.
Why now
AI is moving into real workflows. Trust, provenance, uncertainty and reviewability become the missing layer.
How this becomes a business
A customer usually does not start with a full rollout. The commercial path starts small, validates workflow fit and expands only when the output is readable, useful and routable inside the organisation.
Step 1: Shadow-mode caseA small number of monitored sites, assets or records are tested in parallel with the existing process. Outputs are used for review and learning only.
Step 2: First paid pilotA defined monitored sample, usually 2–3 locations or workflow cases, runs for 8–12 weeks with agreed cadence, setup and calibration.
Step 3: Portfolio workflowIf accepted, the same Evidence Pack logic expands to a portfolio of 20–50 significant monitored projects, locations or record sets.
Step 4: ExpansionThe workflow can be reused across departments, regions, asset classes or adjacent regulated users.
Why portfolio expansion matters
One pilot proves readability and workflow fit.
One portfolio proves recurring revenue.
One group rollout proves repeatability.
The first paid pilot is not expected to be large. Its role is to prove that the output is understandable, useful and routable inside the institution. The portfolio phase is the relevant revenue step.
Indicative commercial scenarios
These scenarios are illustrative and investor-facing. Actual pricing depends on scope, cadence, review tier, escalation requirements, integration needs and customer procurement rules.
| Scenario | Commercial meaning | Indicative revenue |
|---|---|---|
| First paid pilot | 2–3 monitored locations or workflow cases, 8–12 weeks, including setup and calibration | approx. €10k–€12k |
| First portfolio workflow | 20–50 significant monitored locations or record sets under one institutional customer | recurring subscription based on coverage, cadence and review tier |
| 25-location model | A 25-location monitored portfolio under an accepted workflow | approx. €18k–€20k monthly recurring revenue |
| 50-location model | A larger portfolio under similar logic | approx. €450k annual recurring potential |
| Group or regional expansion | The same workflow repeated in another department, region, subsidiary or adjacent regulated use case | additional portfolio revenue |
Why customers pay
Better timing of attention
Evidence Packs help teams see which project or location deserves review before the next formal control point.
More consistent review material
Teams receive comparable outputs with status, confidence, reason codes and uncertainty instead of rebuilding the story from fragmented files.
Less reconstruction pain
Versioned packs and audit trails help answer what was known, when, why and with what uncertainty.
What this model does not assume
- It does not assume replacement of site visits.
- It does not assume automated final decisions.
- It does not assume immediate full bank integration.
- It does not require raw imagery to become the customer-facing product.
- It starts with low-friction, shadow-mode validation.
Want to review the commercial model?
Investor materials can provide more detail on pilot packaging, portfolio expansion logic and funding needs.
Public ecosystem signals include OVHcloud Startup Program and EY Startup Academy Frankfurt. Additional validation and partner discussions remain NDA-bound until approved for public disclosure.