System and risk model
An inventory of models, data, prompts, tools, integrations, permissions and human-oversight points.
AI Assurance & Governance
Know whether your AI system is ready for safe production.
We independently measure quality, security and control across the AI system. Instead of assurances, you receive test results, a risk register and an evidence-based go/no-go decision.
Why assurance
A working demo is not yet a safe system.
A model may perform well in a presentation and fail on edge cases, sensitive data, or after a prompt, model or knowledge-base change. Without a reference set and repeatable evaluations, you cannot tell whether quality is improving or merely changing.
AI assurance combines technical testing with governance and AI Act requirements. It can cover a system built by Semitora, your internal team or another supplier — without replacing the architecture or taking the project away from its delivery team.
Entry product
AI Assurance & Governance Sprint
We scope the sprint around the system’s risk and architecture. It ends with a working evidence pack for the business owner, IT, compliance and the delivery team.
Golden set and acceptance criteria
Representative cases, expected outcomes and measurable quality thresholds defined before assessment.
Evaluation report
Results from quality, security, source-grounding and edge-case behaviour tests.
Remediation plan and go/no-go
Risk priorities, recommended guardrails, action owners and conditions for production release.
Test scope
We test the system, not only the model.
Tests are selected for the use case, data, integrations and consequences of a wrong answer or action.
Quality and grounding
Correctness, completeness, source citations, hallucinations, refusal behaviour and stability across paraphrases.
Data security
Sensitive-data exposure, permission enforcement, prompt injection and uncontrolled data flows.
Agent boundaries
Allowed tools and actions, human escalation, idempotency and behaviour when an integration fails.
Auditability and cost
Decision logs, prompt and model versions, source lineage, unit cost and the ability to reproduce an outcome.
How we work
From criteria to a production decision.
01
Scope and risk
We identify the system owner, critical scenarios, data, integrations and consequences of failure.
02
Criteria before testing
We build the golden set and agree acceptance thresholds before seeing final results.
03
Evaluations and red teaming
We run repeatable functional, security and edge-case tests.
04
Evidence and remediation
We deliver results, control gaps, priorities and go/no-go conditions without hiding weak outcomes.
After deployment
AI Quality & Compliance Operations
A one-off test is not enough when models, prompts, data and regulations change. The retainer maintains a continuous control loop.
Regression evaluations
Repeat tests after changes to a model, prompt, knowledge base, tool or integration.
Quality and cost monitoring
Quality trends, failures, fallbacks, human escalations and the cost of one operation.
Incidents and change control
An AI incident register, root-cause analysis and control of changes that affect risk or compliance.
System-owner reporting
A recurring summary of evidence, open risks, remediation actions and decisions.
Common questions
Assurance without taking over delivery.
Yes. We work with agreed access to the system, documentation and logs. The goal is independent measurement and a remediation plan, not an automatic supplier replacement.
It does not replace legal advice or a formal conformity assessment. It provides technical evidence, risk classification and materials for the system owner, compliance team and legal adviser.
No. We can work with a PoC, test environment or production system. Scope and thresholds are matched to the stage and the consequences of failure.
Have an AI system and need evidence that it can be trusted?
Tell us about the system, its delivery stage and the most important risk. We will propose an independent evaluation scope and acceptance criteria.