Find Where AI Systems
Fail
We help software companies, AI consultancies and system integrators evaluate AI-enabled workflows end to end — from model output and tool calls to the actual business state. We find where they fail and verify whether the fix worked.
Retrieval & grounding · Tools & workflow actions · Downstream state · Reusable release evidence
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.
AI Workflows We Evaluate
We evaluate the complete path the AI influences — not just the language model response.
RAG & Knowledge Workflows
Evaluate whether the workflow retrieves the right evidence, produces answers grounded in that evidence, cites sources correctly, handles conflicts and abstains when the information is missing.
- Retrieval precision and recall on representative queries
- Answer groundedness and citation accuracy
- Handling of missing, outdated or conflicting evidence
- Refusal behaviour and hallucination rate
Agents & Tool-Using Workflows
Evaluate whether the workflow completes the intended task, chooses and calls tools correctly, respects authority boundaries, recovers when a step fails and hands off to a human safely.
- 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
Connected AI Workflows
Evaluate the full path from user request to AI decision, tool calls and writes into APIs, databases, CRM or ERP — confirming the real system state matches what the AI claims happened.
- Correct writes to APIs, databases, CRM or ERP systems
- Reconciliation between the AI's claim and the real system state
- Idempotency, retries and duplicate-action handling
- End-to-end recovery when a downstream step fails
The Answer Can Be Right While the Workflow Is Wrong
Verinika checks whether the state of the real system matches what the AI claims happened.
An agent saying "the customer record has been updated" is not success. The CRM state must show the correct change.
AI Reliability Sprint
A focused evaluation of one critical AI-enabled workflow, giving IT partners 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
- Boundary and failure 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.
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