Private AI for decisions that must withstand scrutiny
MatuLabs is building a closed-loop assurance workspace that turns meetings, workbooks, controls, actuarial models and risk operations into review-ready decisions—each linked to the evidence, assumptions and human accountability behind it.
One assurance operating system
One secure intake, one evidence ledger and one review queue connect every MatuLabs workspace like a fast enterprise nervous system. Teams move from source to decision without losing provenance or accountability.
Files, workbooks, folders, audio and video.
Hash-bound source, transformation and decision lineage.
Uncertainty, alternatives and decision-critical exceptions.
Human-owned approval before final use or delivery.
Specialized workspaces
Start with the problem your team feels today. Add specialist workspaces without rebuilding the controls, evidence or review model underneath.
Turns multilingual meetings into English board minutes, decisions, actions, owners and deadlines—with every critical item linked back to a reviewable evidence window.
Challenges whether controls are merely documented or actually evidenced, then links gaps to findings, owners and corrective action in one traceable loop.
Finds formula breaks, assumption conflicts, unexplained movements and lineage gaps across IFRS 17 workbooks and review notes—before they reach a decision.
Connects risk, owner, action, SLA and board visibility so management can see what changed, who owns it and what happens if nothing moves.
Maps each risk to the control, evidence, exception and action needed to close the loop, with an integration path for process-model ecosystems such as ADONIS and iGrafx.
Decision intelligence
Each specialist workspace is being designed to return traceable findings, two evidence-bound recommendations and two conditional foresight signals—without turning a review draft into a final legal, audit, financial or actuarial opinion.
Which board decision has no owner, deadline or evidence?
Which control looks complete on paper but fails in operating evidence?
Which IFRS 17 movement is unexplained, inconsistent or unsupported?
What changed in the workbook—and which conclusion moves because of it?
What should management do next, and what is likely if nothing changes?
Trust by architecture
The differentiator is not another chatbot. It is a controlled chain from source to conclusion, bounded by explicit permissions, evidence receipts and human release points.
Customer material can remain inside the controlled environment.
New knowledge moves through replay, validation and receipt gates.
Important outputs stay connected to reviewable source windows.
Unresolved material uncertainty remains visible and cannot finalize silently.
How MatuLabs works
Receive the work in its native format.
Extract structure, terminology and relationships.
Find inconsistencies, gaps, risks and alternatives.
Bind findings to source and transformation lineage.
Route only material judgment to authorized people.
Invisible quality engines
Software Agent, Quality Measurement, Council review and Security Mesh operate behind the customer workspace to improve reliability without turning governance into user friction.
Public development status
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