Enterprise AI QE Architecture / VeriCore
Establish the testing architecture for LLM, RAG, and agentic systems across development and production.
03 · ASSURE
Make AI systems safe, testable, governed, and enterprise-ready.
Captivolt helps enterprises validate AI behaviour, govern risk, secure agentic systems, and create the evidence required for responsible deployment.
The problem
AI Quality, Governance & Security
AI is entering production faster than the quality, governance, and security disciplines needed to oversee it — and regulators, boards, and auditors are starting to ask for evidence.
What we do
Our approach
Captivolt helps enterprises validate AI behaviour, govern risk, secure agentic systems, and create the evidence required for responsible deployment.
Capabilities
What this pillar covers.
AI Quality & Evaluation
LLM & GenAI Validation
Validate correctness, groundedness, consistency, relevance, safety, and policy alignment.
RAG Quality & Retrieval Accuracy Testing
Test retrieval precision, source grounding, access control, hallucination risk, and response reliability.
Agent Evals & Workflow Testing
Evaluate task completion, tool selection, tool input quality, instruction adherence, and workflow reliability.
Prompt Regression Test Suite Development
Detect failures when prompts, models, context, tools, or retrieval sources change.
AI Red Teaming & Safety Testing
Test for jailbreaks, prompt injection, unsafe outputs, data leakage, and adversarial behaviour.
AI Governance
AegisIQ AI Governance Framework
Build AI inventory, risk classification, policy controls, approval workflows, and evidence models.
AI System Inventory & Classification
Create a living register of AI systems, owners, models, data sources, integrations, and risk levels.
AI Risk & Impact Assessment
Assess privacy, fairness, safety, explainability, business impact, and operational risk.
AI Governance Framework Design
Define policies, review boards, evidence requirements, decision rights, and accountability structures.
RAG Integration Governance
Govern retrieval sources, access permissions, lineage, grounding quality, and leakage risk.
ISO 42001 Readiness
Prepare AI management-system controls, records, policies, and operating evidence.
EU AI Act / DPDP / GDPR Alignment
Map AI systems and data practices to relevant regulatory expectations.
AI Security
AI Security & Prompt Injection Defence
Protect agents and LLM applications from prompt injection, jailbreaks, tool abuse, and unsafe actions.
RAG Data Leakage Controls
Reduce unauthorised exposure through permission-aware retrieval, redaction, filtering, and testing.
AI Access Management & Data Classification
Align data classification, access policies, and model usage controls.
AI Security Incident Response Planning
Define playbooks, escalation paths, and recovery processes for AI-related incidents.
Application, API & Cloud Security Review
Assess code, APIs, architecture, and cloud environments for exploitable risk.
Threat Modelling & Architecture Review
Identify design-level risks before systems are built or released.
Compliance & Security Governance
ISO 27001 / ISMS Readiness
Build security governance, risk treatment, Statement of Applicability, Annex A controls, and audit evidence.
Supplier & Third-Party Risk Management
Assess vendors, platforms, and technology dependencies for security and compliance risk.
vCISO & Security Governance
Provide senior security leadership for risk, control maturity, board reporting, and audit readiness.
SOC 2 Type II Readiness
Prepare control evidence and operating cadence for SOC 2 readiness.
Cross-Framework Compliance Mapping
Reduce duplicated compliance work by mapping controls across ISO, SOC 2, DPDP, GDPR, and sector expectations.
AI Monitoring & Operations
Model / Agent Monitoring & Drift Detection
Monitor quality, behaviour, drift, safety, cost, and performance after deployment.
AI Cost, Latency & Reliability Tracking
Track operational cost, latency, token usage, and reliability against agreed thresholds.
Evaluation Feedback Loops
Route production findings back into evaluation suites, prompts, and retrieval improvements.
Use cases
Typical engagements.
- Pre-release validation of GenAI systems
- AI governance framework for a listed company
- RAG data leakage risk reduction
- Prompt injection defence
- ISO 42001 readiness
- AI system inventory and risk classification
- Security assurance for AI-enabled applications
What makes this different
- Combines AI-QE, governance, and security
- Converts AI governance into operating evidence
- Strong fit for regulated and listed enterprises
- Proprietary VeriCore and AegisIQ frameworks
What you receive
- AI system register and risk classification
- Evaluation suites, scorecards, and regression baselines
- Governance workflows and evidence model
- Security findings and remediation plan
- Monitoring and escalation runbooks
ENGAGEMENT SHAPE
Typically a governance or AI-QE gap assessment, followed by framework implementation and an ongoing assurance cadence.
Evaluation scorecard
What we measure before release.
Every dimension below becomes a measured baseline, not an opinion.
- Groundedness
- Retrieval precision
- Policy adherence
- Tool-use accuracy
- Prompt-injection resistance
- Task completion
- Cost / latency
- Human escalation quality
Related work
Where we have done this.
Enterprise AI Framework for an NSE-listed Company
Captivolt developed an enterprise AI framework covering use-case intake, governance, risk classification, accountability, evidence, and leadership oversight.
Enterprise AI QE Architecture
A structured architecture for testing and monitoring LLM, RAG, and agentic systems through evaluation datasets, prompt regression, retrieval testing, hallucination checks, and drift monitoring.
Related insights
Further reading.
Assess AI Risk and Readiness.
A structured first conversation about what you are trying to build, govern, or scale.