PRODUCT · LLM, RAG & Agent Evaluation
VeriCore AI Evaluation Studio
A quality and evaluation layer for LLM, RAG, and agentic systems.
At a glance
The short answers.
- What is it?
- PRODUCT · LLM, RAG & Agent EvaluationVeriCore is a configurable evaluation capability implemented within the client environment, combining reusable architecture, evaluation workflows, datasets, scorecards and monitoring components.
- Who is it for?
- Chief AI Officer / Head of AI
- What problem does it solve?
- GenAI systems pass demos and fail in production because nobody tests them like enterprise software.
- What does it actually do?
- A structured evaluation architecture: datasets, regression suites, scorecards, and monitoring wired into your delivery pipeline.
- Where does it run?
- Your environment and your CI/CD, against your systems and your data. Evaluation cases are yours and do not leave.
- What evidence does it produce?
- Scorecards per release, regression results, adversarial suite results, release gate decisions, and drift measurements after release.
- What does the client receive?
- Evaluation harness and datasets
- Regression baselines and scorecards
- Release quality gates
- Monitoring configuration
- How do we manage evaluation?
As a pipeline: a versioned dataset, a runner that exercises the system, scores rolled into release gates, and low scores root-caused and fed back.
The evaluation architecture →- How do we establish reusable AI capabilities?
You keep the harness, the datasets, the baselines and the gate definitions. Your team runs the suite and reads the result; the decision to ship stays with you.
What you own afterwards →
AI evaluation pipeline · RAGAS-based
An AI evaluation architecture.
How we test LLM, RAG, and agent systems like enterprise software: measured against a dataset, gated on release, and continuously improved.
Build the test set
Questions and ground truth are structured into a versioned evaluation dataset.
Run against the system
A test runner invokes the RAG or agent system and captures the context and answers returned.
Score with RAGAS
Embedding and LLM-judge layers grade context precision, context recall, answer relevancy, and faithfulness.
Aggregate & gate
Scores roll up into a scorecard, with pass/fail release gates and ongoing monitoring.
Close the loop
A feedback agent root-causes low scores and feeds tuning back into the pipeline: evaluation that improves the system.
Evaluation scorecard
Dimensions evaluated: illustrative structure, not live scores.
Video · 3 min
One release, through the gate.
Why a demo that passes proves little, and what VeriCore does instead: a versioned test set agreed with the business, a run on every change, scores from known answers to a calibrated judge, a gate that holds a release whose faithfulness falls below its mark, and monitoring against the same baseline after launch. An animated explanation rather than product screenshots: the release, its scores and its costs are illustrative.
VeriCore – a release gate for enterprise AI · 2:35
Read the video as text
- The demo passed. A knowledge assistant in pilot is asked “What is the refund window for enterprise contracts?” and answers “within 60 days of purchase”; Policy §4.2 says 30 days, and a customer found it. Demo passed, pilot passed, production failed. “Generative AI passes the demo. Then fails in production.”
- The shift. Traditional software is deterministic: the same calculation run three times returns the same total, and the test stays passed. An LLM, RAG or agent system is probabilistic: the same refund question run three times returns “30 days, per Policy §4.2”, “within 60 days of purchase” and “it depends on your contract tier”. Behaviour changes with a model update, a prompt edit, a source document or a retrieval setting, with no code change. “Same input. Different outputs.”
- The gap. The evidence available at each stage today: unit tests on code at build, impressions at the demo, anecdotes in the pilot, no gate at sign-off, user complaints in production, and changes often not re-tested. Three outcomes follow: ship blind (released on confidence, not evidence), never ship (stuck at sign-off, because no one can prove it is ready) or degrade silently (behaviour drifts after launch, unnoticed). “Enterprise AI has no release gate.”
- VeriCore, AI Evaluation Studio by Captivolt: the evaluation and release-gate layer for enterprise AI, in five stages: test set (a versioned dataset), run (captured outputs), score (per-case scores), gate (the release decision) and monitor (drift, cost and latency). “A recorded pass or fail — on every release.”
- Test set and run. A versioned test set built with domain experts (known-answer cases with an expected answer and source, retrieval cases where the correct document must be found, and adversarial prompts that must be refused or stay in policy), with pass marks agreed with the business, per metric. A model change detected in CI/CD triggers the evaluation harness, which runs every case three times and traces every output to the dataset version. “Define what right looks like. Run it on every change.”
- Score. Scoring methods run from the most objective to the most judgement-based: known-answer checks, retrieval metrics, rule and policy checks, a calibrated model-as-judge, and repeat-run consistency. The release candidate’s scorecard, in illustrative figures, passes correctness, retrieval recall, policy adherence and run consistency, while faithfulness falls below its mark; the adversarial suite is reported separately and held policy. “Every output scored — from known answers to a calibrated judge.”
- The gate. The release gate holds: faithfulness is below its pass mark. Failed-test analysis finds the cause: retrieval returns a superseded policy instead of the current §4.2, because an index was not refreshed, affecting refund and renewal cases. The release owner chooses to fix and re-run rather than override with a recorded reason; after the fix and re-run, the release goes ahead, and a gate record keeps the dataset, build, model, owner and time. “Automated where it can be. Human where it must be.”
- After release. Production faithfulness is tracked against the release baseline until drift crosses the pass mark; cost per thousand queries and p95 latency stay within budget and target; the regression opens a new release candidate and the cycle starts again from the test set. Evaluation cases never leave your environment. “Watched after launch. Against the same baseline.”
- “From opinion to evidence.” Released on confidence becomes released on evidence. “Models are rented and will change. The definition of right is yours.”
- VeriCore, AI Evaluation Studio by Captivolt: “A release gate for enterprise AI.” THINK → BUILD → ASSURE → SCALE, with ASSURE highlighted.
Example output
Reference architecture.
Test harness
Execution & capture
RAGAS evaluation engine
Scoring & reporting
Feedback & tuning loop
↺ Tuning feeds back into the pipeline: evaluation that improves the system, not just scores it.
Illustrative reference architecture · representative stack, adapted per engagement · no client data shown.
Where the work happens
Ten surfaces, and what each one is for.
Described rather than screenshotted: product imagery waits on client-approved assets, and a mock-up presented as the product would be exactly the fabricated artefact an evaluation studio has no business shipping.
- 01
Evaluation dashboard
The current state of every evaluated system: what passes, what regressed, and what is blocking a release right now.
- 02
Dataset management
Evaluation cases drawn from real business inputs, versioned with the system they test, with expected outcomes agreed rather than inferred.
- 03
Test execution
Runs a suite against a specific build and a specific model version, so a result always names what produced it.
- 04
Retrieval evaluation
Scores what came back before scoring what was said: precision, coverage, and whether the passage supports the claim made from it.
- 05
Prompt regression
Re-runs the agreed cases when a prompt, model, tool or retrieval source changes: the four things that change silently.
- 06
Agent task evaluation
Judges the workflow rather than the sentence: task completion, tool selection, input quality and instruction adherence.
- 07
Scorecard
The dimensions with their pass conditions, and which are must-pass. The artefact a release decision is actually made against.
- 08
Failed-test analysis
Groups failures by cause instead of listing them, because one retrieval fault produces forty failing cases and one fix.
- 09
Release gate
Compares the run to the release conditions and returns release, hold or a recorded override. It is able to say no.
- 10
Production monitoring
Watches the same dimensions after release, against the baseline the release produced, and routes a breach back into evaluation.
What it measures
Twelve dimensions, scored against your cases.
Traditional CI/CD tests software behaviour. VeriCore extends quality engineering into probabilistic AI behaviour.
- Correctness
- Relevance
- Grounding
- Retrieval quality
- Hallucination
- Task success
- Tool use
- Safety
- Policy adherence
- Cost
- Latency
- Drift
Which is also the limit. A test suite tells you how a system behaved on the cases you ran, not that it is safe, the same way a green pipeline has never meant software is correct. VeriCore measures, records and gates. Deciding what is acceptable, and accepting what is left, stays with the people accountable for it.
In practical terms
What gets installed, what you own, and how it connects.
The seven first questions are answered at the top of the page. These are the three a buyer asks next.
- What gets installed or configured?
- The evaluation harness, datasets built from your real cases, regression baselines, scorecard definitions, release gate configuration and monitoring hooks into your pipeline.
- What do you own afterwards?
- The harness, the datasets, the baselines and the gate definitions. Your team runs the suite and reads the result; the judgement of whether to ship stays with you.
- How does it connect to Captivolt services?
- It is the measurement side of the ASSURE lifecycle: stages 02 Evaluate through 04 Gate, and 07 Monitor. AI Quality, Governance & Security →
Modules
What is inside.
- LLM output validation
- RAG retrieval quality
- Prompt regression
- Agent task evaluation
- Safety & policy checks
- Drift monitoring
- Cost & latency tracking
- Enterprise AI QE Architecture: Proprietary framework
See VeriCore in Action.
We will walk through the architecture and how it maps onto your environment.