Services · Last updated June 2026

AI your board and your auditors will accept

Blash AI is the artificial-intelligence division of Blash Advisory, a London-headquartered corporate finance and advisory firm specialising in AI governance and assurance, serving boards, corporates and funds across the UK, EMEA and the Far East.

This is the difference between an AI demo and an AI system a regulated business can run. We put the controls, monitoring and reporting around AI that a board, a CFO and an auditor expect.

DPIA completed before any higher-risk processing goes live
Audit trail every action recorded with the basis for it
Rollback and human approval on consequential actions
What we build
  • Controls and approval gates on consequential actions
  • Monitoring of what each system decides and refuses
  • Audit trails that record every action and its basis
  • Reporting a board and an auditor can read
  • A review of AI you already run, against these standards
Why it matters

A generic AI supplier ships agents with no controls. A large consulting firm delivers a roadmap and a closing email. We come from a corporate-advisory firm, so governance shapes how we build from the first day.

  • AI you can defend to a board or a regulator
  • A clear record of what every system does and why
  • Confidence to grant autonomy on evidence
How it actually works

We define the controls before the build: where a human must approve, what gets logged, and how performance is measured and reported. For AI you already run, we review it against the same standards and close the gaps, so the systems in your business are ones you can stand behind.

Built to be governed

This service is the governance. The output is a control framework, a monitoring and audit-trail setup, and board-ready reporting, mapped to how your firm is overseen and built with reference to ICO guidance on AI and data protection and the NIST AI Risk Management Framework. It is the assurance layer that lets the rest of your AI run safely.

Our method

The Blash AI Assurance Framework

  1. Define controls and approval points before the build
  2. Log every action and the basis for it
  3. Monitor performance and measure the error rate
  4. Report in terms a board and an auditor accept
Proof

Where this has paid back

Legal services

Structured matter intake for a law firm

Fee-earners received cleaner intake packs while legal judgement stayed with the lawyer.

72%fewer incomplete intake packs
90 minutesaverage time to first internal summary, down from 24 hours
Regulated advice

Compliant enquiry intake for a regulated advice firm

More enquiries were captured without crossing advice boundaries.

41after-hours enquiries captured in the first month
96%enquiries logged with consent status and source
Fintech

A private knowledge assistant for a fintech team

Staff could answer internal policy and product questions faster with source traceability.

Under 2 minutesaverage policy-answer retrieval, down from 20 minutes
100%answers required an approved source or refusal
See all proof →
Questions about AI governance and assurance
Why does AI governance matter for us?

Because an AI system that touches money, customers or regulated data needs the same controls as any other part of the business. Governance is what lets you use AI without taking on risk you cannot see or defend.

Can you review AI we already run?

Yes. We assess existing systems against our control standards, identify the gaps, and put the monitoring, approvals and reporting in place to close them.

What does board-ready reporting include?

What each system did, what it decided and refused, the error rate, the approvals applied, and the data it touched, presented in terms a board and an auditor can read.

Is this only for large firms?

No. Any business using AI near money, customers or sensitive data benefits from clear controls. The depth scales to the size of the risk, not the size of the company.

How do you handle our data?

The systems we build run inside your own accounts and systems, so your data stays where it already lives. Model providers are used under API terms that exclude training on your data, permissions are enforced, every action is logged, and a data protection impact assessment is completed before any higher-risk processing goes live.

Who is accountable if an automated action goes wrong?

You own and operate the system, and the controls are designed so nothing consequential happens without human approval while autonomy is being earned. Every action is logged with the basis for it, so responsibility can be established from the record rather than argued about after the fact.

Ready to build this?

Book a call and we will map the first workflow worth automating

Book an AI audit
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