Find Where the AI Systems
You Deliver Fail
Verinika helps software companies, AI consultancies and system integrators evaluate RAG systems and tool-using agents—white-label or alongside their delivery teams.
Retrieval and grounding · Agent tasks and tool use · Calibrated scoring · Quality checks after every change
Your Team Builds the System. We Make Its Behaviour Measurable.
AI implementations can look convincing in a demo while still failing on real documents, edge cases, multi-step tasks and changing model versions. Verinika gives IT partners a specialist evaluation layer without requiring them to build a complete evaluation practice internally.
Evidence for Client Acceptance
Replace subjective demonstrations with documented criteria, test datasets and reproducible findings.
Faster Diagnosis and Remediation
Separate retrieval, generation, tool-use and workflow failures so your team knows where to intervene.
Reusable Test Coverage for Future Changes
Turn confirmed failures into reusable tests that verify the problem does not return after prompt, model, data or workflow changes.
Systems We Evaluate
Two primary system types, with targeted adversarial scenarios added where relevant.
RAG & Knowledge Assistants
Evaluate whether the system retrieves the right evidence, produces answers grounded in that evidence, cites sources correctly and handles missing or conflicting information appropriately.
- Retrieval precision and recall on representative queries
- Answer groundedness and citation accuracy
- Handling of missing, outdated or conflicting evidence
- Refusal behaviour and hallucination rate
AI Agents & Tool-Using Systems
Evaluate whether agents complete the intended task, choose and call tools correctly, respect authority boundaries and recover safely when a workflow fails.
- Task completion and goal accuracy
- Correct tool selection and tool-call arguments
- Respect for authority and permission boundaries
- Safe recovery when a step or workflow fails
Adversarial Evaluation
Where relevant, we add targeted adversarial scenarios such as direct and indirect prompt injection, manipulated context, unsafe tool requests and sensitive-information exposure.
Adversarial AI evaluation is not a penetration test, source-code security audit, compliance audit or certification.
AI Evaluation Baseline Sprint
A focused engagement for IT partners that need a defensible baseline before client acceptance, remediation or further optimisation.
Suitable for
- Preparing for a client acceptance milestone
- A RAG or agent system that already works in demos
- Uncertainty about real-world reliability at scale
- A need for reproducible evidence before go-live
What it includes
- Scoping of system, users, data and failure modes
- A representative test dataset
- Component and end-to-end evaluation
- Targeted adversarial scenarios where relevant
Deliverables
- A documented evaluation report
- Prioritized findings with severity levels
- A reusable quality test set for future changes
- A readout session with your team
Built to Strengthen Your Delivery, Not Replace It
Your client relationship remains yours. Verinika adds specialist evaluation capacity while your team retains ownership of implementation, remediation and ongoing client delivery.
White-label
Evaluation delivered under your brand, with Verinika operating as a specialist subcontractor behind the scenes.
Co-delivery
Verinika works visibly alongside your team and presents evaluation findings together with the implementation partner.
Specialist support
Targeted support for dataset design, scoring calibration, test architecture or a specific high-risk workflow.
We do not use an engagement to bypass the implementation partner or take over the client relationship.
Clear Roles, Shared Outcome
Your team owns the system and the client. Verinika owns the evaluation methodology and evidence.
| IT Partner | Verinika | Together |
|---|---|---|
| Builds and owns the AI system | Designs the evaluation methodology | Agree on acceptance criteria |
| Owns the client relationship | Runs specialist evaluations and produces evidence | Review findings and priorities |
| Implements remediation | Provides and reruns reusable quality tests | Confirm improvements before release |
Our Methodology
A reproducible path from mapping the system to test coverage your team can rerun.
Scope
Record intended use, architecture, risks and acceptance criteria.
Design
Develop representative test cases, datasets and scoring rules.
Calibrate
Compare automated scoring against human judgement where the decision requires it.
Evaluate
Test components and the complete system reproducibly, with versioned configurations and traces.
Verify
Manually confirm findings and turn them into reusable quality tests.
Verinika does not implement remediation itself, but can advise concrete improvement options and priorities.
Evaluation Evidence in Practice
Our evaluation examples and sample deliverables are currently being prepared around controlled RAG and agent evaluation cases. Partner pilots can be scoped against an existing test environment and representative client workflows.
Ready to Evaluate the Systems You Deliver?
Tell us what your team is building and what must be demonstrated before the next client or release decision.
Discuss a Partner Pilot