AI risk & compliance intelligence
Regulatory exposure is not a checklist. It is an optimisation problem.
Introspect discovers your AI infrastructure, maps it to the EU AI Act, NIST AI RMF and ISO 42001, then uses agentic systems to generate compliance and remediation scenarios automatically. A constraint engine ranks what is actually feasible under budget, residual risk, and enforcement timing.
Built for regulated estates where budget, residual risk and enforcement timing collide.
EU AI Act
Obligations mapped to systems
NIST AI RMF
Controls traced to evidence
ISO/IEC 42001
Management system alignment
The problem we solve
Today's tools tell you what is wrong. They cannot tell you what to do about it.
Audit platforms produce findings. Governance suites produce registers. Both stop at the point where the real decision begins: what to fund, what to defer, and what residual risk the board is prepared to carry.
Meanwhile the estate keeps moving. Shadow models appear, vendors change terms, and enforcement dates arrive whether the remediation plan is affordable or not.
Introspect pairs agentic scenario generation with a constraint engine so AI risk becomes a set of quantified, optimisable decisions.
How it works
Five steps from unknown estate to defensible decision.
- 01
Discover
Surface every model, agent, API and data flow already running across the estate.
- 02
Map
Bind each system to obligations under the EU AI Act, NIST AI RMF and ISO/IEC 42001.
- 03
Model
Agentic systems automatically generate compliance and remediation scenarios. They probe timing, sequencing, and residual risk paths without manual scenario design.
- 04
Optimise
A constraint engine scores and prunes that agent generated space under regulatory, business, and scenario limits, then solves for the best feasible outcome.
- 05
Decide
Board ready trade offs: cost, residual risk, timeline and accountable owner.
Core capability
Agentic scenario generation. Constraint powered selection.
Specialist agents automatically propose remediation and compliance scenarios across your mapped estate. They sequence work, stress test timing, and explore alternatives humans would not enumerate by hand. A constraint engine then evaluates that space against hard limits and returns the efficient frontier: least cost for a target risk position, and lowest residual risk for a given budget. Agents expand the option set; constraints keep every outcome defensible.
Regulatory frameworks
Obligations, deadlines and evidentiary thresholds encoded as hard constraints, not checklist items.
Business limits
Budget envelopes, headcount, delivery capacity and risk appetite bound every scenario the engine proposes.
Scenario conditions
Enforcement timing, market entry, vendor change, model retirement. Tested as conditions, not assumptions.
Who this is for
Investors, practices and enterprises navigating AI exposure.
Investors & strategic partners
A clear category bet: agentic scenario generation bound by a constraint engine for AI risk, not another GRC checklist. Early conversations welcome for diligence and collaboration.
Strategy & risk practices
Scale AI risk engagements with agents that generate client scenarios automatically, then a constraint engine that ranks options under each client’s limits. Defend recommendations with scenario evidence, not maturity ratings alone.
Enterprise risk, compliance & legal
Know what AI is running, what each system obliges you to do, and which agent generated paths survive your budget, capacity, and risk appetite. Bring the board a decision, not a register.
Conversations
Interested in Introspect?
We speak with investors, strategic partners, advisory practices and enterprise risk functions. Share a little context and we will follow up with a focused conversation.
- ·Product walkthroughs for diligence and partnership
- ·Partnership and pilot discussions for B2B teams
Prefer email? partners@introspect.cloud