Build trust in
your AI

Ensuring reliable AI, whether you build it, buy it, or access it as a service.

AI Challenges

Generative (and agentic) AI systems work fundamentally differently than traditional software - they're probabilistic, not deterministic. This enables remarkable capabilities but creates new challenges around reliability, bias, hallucination, and security that standard testing can't address

The "Last Mile" of Trust

Generic AI platforms handle most needs, but the main source of failures lies in the final 20% - how AI performs with your specific data, business rules, and precise customer expectations. Here, probabilistic behaviour can cause costly missteps and erode contextual relevance.

Proof of Concept Purgatory

Many generative and agentic AI projects stall between pilot and full deployment. The real-world integration exposes unforeseen complexities spanning security, operations, data governance, and scalability, leading to unpredictable behaviours, eroded trust, and disrupted operations.

Staying inside the lines

While AI offers immense power, it becomes an unpredictable liability when pushed beyond its defined limits. Failing to establish clear boundaries and robust safeguards invites severe risks, including legal liabilities and irreversible reputational harm.

42%
Companies abandoning most AI initiatives (up from 17% last year)
46%
AI proof-of-concepts scrapped before production
67%
Enterprises unable to transition pilots to production
30%
AI projects that move past pilot stage (Gartner)

AI Project Success Metrics

METRIC PERCENTAGE IMPACT TREND
Projects reaching production 30% High failure rate ↓ Declining
Budget overruns 65% Cost concerns ↑ Increasing
Timeline delays 58% Delayed ROI → Stable
Quality below expectations 47% Trust erosion ↑ Rising
Compliance issues 39% Legal risks ↑ Growing
Integration failures 52% System disruption → Persistent

What We Do

AI Evaluation Pipeline

Custom testing infrastructure built for your specific AI systems

What we deliver:

  • Automated evaluation frameworks tailored to your AI applications
  • Statistical validation approaches for consistent performance measurement
  • Integration with your existing development and deployment processes
  • Team training on running and interpreting evaluations
  • Documentation and handover for long-term maintenance

Value

Enables confident AI deployment with measurable reliability evidence. Reduces risk of AI failures while building internal testing capability your team can maintain.

AI Governance Framework

Structured approach to AI oversight, risk management, and regulatory readiness

What we deliver:

  • AI risk assessment and mitigation strategies
  • Governance policies and oversight structures
  • Regulatory compliance readiness (EU AI Act, etc.)
  • Business impact measurement frameworks
  • Stakeholder reporting and audit trails

Value

Provides board-ready AI governance and regulatory compliance. Enables confident AI scaling while managing business and legal risks.

AI Program Assurance

Hands-on assurance leadership embedded within your AI programs to ensure reliable delivery

What we deliver:

  • Senior assurance practitioner embedded within your AI development teams
  • Hands-on testing, validation, and quality control throughout development cycles
  • Direct intervention when risks to reliability or performance are identified
  • Real-time guidance to development and product teams on assurance best practices
  • Active problem-solving and technical support to resolve assurance challenges

Value

Provides expert hands-on assurance without permanent hiring overhead. Ensures AI programs deliver reliable outcomes through active involvement in development and delivery processes.

Why Us

We unite the disciplines others keep separate

Most companies know they need better AI testing but struggle with systems that give different outputs for the same input. We solve this by integrating the disciplines that AI assurance actually requires:

ML Engineering

Deep understanding of LLM behavior, agent workflows, and RAG system performance across different scenarios and edge cases

Data Science

Statistical validation and confidence intervals for probabilistic system outputs

Quality Assurance

Systematic testing frameworks adapted for non-deterministic AI behavior

Product Knowledge

Focus on business-critical scenarios that matter for your specific use case

Delivery Assurance

Structured approaches to embedding AI testing within existing development workflows and delivery processes

Talk to Us

Ready to build trust in your AI? Get in touch to discuss how we can help ensure your AI systems are reliable, fair, and secure.

Email

Location

Manchester, United Kingdom and anywhere in the world we are needed

Founder

Sean Blezard on LinkedIn