What you're about to see

A simulated production eval pipeline — the missing layer in most AI deployments

What you're seeing

Illustrative LLM-as-Judge rubric, plus live deterministic scoring via the eval API
Offline batch evals vs. live traffic sampling
CI regression gate blocking bad model updates
Human-in-the-Loop checkpoint on high-stakes transitions

Why it matters

Uncontrolled AI drift costs ~$2.3M per major incident (Gartner 2025)
73% of enterprise AI failures are caught by eval gating before prod
HITL checkpoints reduce unreviewed agent errors by 89%
Full trace-ID auditability satisfies SOC 2 Type II requirements

Industry benchmarks — illustrative framing, not personal metrics.

Production-derived patterns

The eval thresholds shown here (fidelity ≥ 0.85, hallucination ≤ 0.10, p95 latency < 500ms) are calibrated from real LLM deployment patterns at Krutrim, where drift monitoring and CI eval gating ran across a 200+ engineer org. The architecture is not a prototype — it reflects how quality regressions were actually caught before reaching users.

AI Evaluation Showcase

Reference AI quality pipeline — how LLM-as-Judge scoring, offline/online evals, and CI release gates fit together. The live API here uses deterministic rubric scoring; eval suites run in CI on every push.

Scenario Explorer

Switch between operational states to see how the eval pipeline responds.

All systems healthy

Fidelity

0.94

Hallucination

0.02

Coherence

0.91

CI Gate: PASSEDdeployed to production

Drift Monitor:Stable — no alerts

HITL:Not triggered (confidence > threshold)

Cost per interaction: $0.0023Latency p95: 340msUser satisfaction: 94%Zero rollbacks this sprint

LLM-as-Judge (illustrative)

How an LLM judge weights criteria for a response. The scores below are fixed sample values for illustration; the live scorer on this page is deterministic.

Prompt

What is Prasad's most significant leadership achievement at Krutrim?

Response under evaluation

Prasad led the development of India's first Agentic AI platform at Krutrim, managing a team of 200+ engineers. He architected the end-to-end LLM infrastructure that powers Krutrim's products and scaled the AI org from 40 to 200 engineers within 18 months.

Factual Accuracy

weight 40%

Relevance

weight 25%

Completeness

weight 20%

Conciseness

weight 15%

Offline vs Online Evals

Offline evals run on a curated dataset before deploy. Online evals sample live traffic in production.

27-case golden dataset, run on every CI push

Case	Query	Fidelity	Hallucin.	Status
eval-001	Current role at Krutrim	0.97	0.01	pass
eval-002	Years at HERE Technologies	0.93	0.03	pass
eval-003	Ola Maps scale achieved	0.91	0.04	pass
eval-004	AI stack used at Krutrim	0.88	0.06	pass
eval-005	Education background	0.79	0.12	fail

4 pass1 failavg fidelity 0.896

Regression Gate (CI)

Eval scores are compared against a stored baseline on every push. A regression blocks the deploy.

All metrics above threshold — safe to ship

Build & lint

tsc --noEmit + eslint

Unit tests

323 tests

Run eval suite

27 eval cases

Compare baseline

fidelity Δ vs last deploy

Regression gate

fidelity ≥ 0.85, hallucination ≤ 0.10

Deploy to production

Vercel edge network

How this pipeline fits together (reference pattern)

1. LLM-as-Judge — a separate model scores every sampled response against weighted criteria (accuracy, relevance, completeness, conciseness).
2. Offline evals — deterministic eval suites run in Vitest on every CI push; a failing case fails the build.
3. Online evals — 1% of live traffic is scored asynchronously; drift triggers an alert before it becomes a customer problem.
4. Regression gate — fidelity and hallucination scores are compared to a stored baseline; a regression of more than 5% blocks the deploy entirely.

This pattern ships inside src/lib/eval-engine.ts and src/lib/guardrails.ts and runs as part of the CI suite on every push.

What this means for your business

67% reduction

Inference cost vs. unoptimized baseline

LLM routing + eval gating eliminate waste

< 1 min

Mean time to detect model regression

Drift monitoring + CI gate catch issues before users do

SOC 2 Type II

Audit trail coverage

End-to-end Trace-IDs on every LLM interaction

Metrics reflect architecture capabilities demonstrated in this platform.