LLM Evaluation & Benchmark Engineering
LLM Evaluation Services to Enhance Your Applications with Powerful AI Capabilities
Our LLM evaluation services give you a measurable answer to "is this actually good enough to ship?" — instead of eyeballing a few chat outputs and hoping. We build the benchmark suites, scoring pipelines, and regression tests that catch failures before your users do.
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The Problem With "It Looks Good in the Demo"
Most teams ship an LLM feature after a few dozen manual test prompts look fine, then find out three weeks later — from an angry customer, not a dashboard — that it fails badly on a category of input nobody tested. Worse, every model or prompt update is a roll of the dice: nothing tells you if last week's "improvement" quietly broke something else.
Without a real evaluation pipeline, you're flying blind on accuracy, safety, and regression — exactly the three things that turn into support tickets, churn, or PR problems.
What Is LLM Evaluation, and Why Not Just Test Manually?
Manual spot-checking catches obvious failures. It does not catch rare edge cases, silent regressions across model versions, or systemic bias in outputs — because no human has time to read 10,000 outputs every time a prompt changes.
Manual QA
Best for: Quick sanity checks before a small prompt tweak
Coverage: A handful of hand-picked examples
Repeatability: Inconsistent — depends on who's reviewing and when
Catches regressions: Rarely, unless someone happens to re-test the exact failing case
Cost to run continuously: High (constant human time) and still incomplete
Automated Evaluation Pipeline
Best for: Every prompt change, model upgrade, and production release
Coverage: Hundreds to thousands of test cases, including adversarial ones
Repeatability: Consistent, version-controlled, runs in CI
Catches regressions: Automatically, before deployment
Cost to run continuously: Low marginal cost once built — runs in minutes
Most teams that come to us have manual QA and assume that's "evaluation." It's a starting point, not a substitute. If you're not sure how exposed you are, our AI Strategy & Architecture Audit includes a quick eval-gap assessment.
What We Build
Custom benchmark/eval suites built around your actual use case and failure modes — not generic public benchmarks that don't reflect your product
LLM-as-judge scoring pipelines for subjective quality dimensions (helpfulness, tone, faithfulness) that simple string-matching can't capture
RAGAS-based evaluation for retrieval-augmented systems — precision, recall, faithfulness, and answer relevance scored automatically
Regression test suites that run in CI on every model or prompt change, flagging drift before it ships
Red-teaming and adversarial testing to surface jailbreaks, prompt injection risks, and harmful-output edge cases before launch, not after
Eval-in-production monitoring — ongoing scoring of live traffic samples to catch drift months after launch, not just at build time
Build vs. Buy
Generic eval-as-a-service platforms give you off-the-shelf metrics that often don't match your actual failure modes — they'll tell you a response is "fluent" while missing that it's confidently wrong about your product's pricing. A custom AI eval pipeline development engagement starts from your real failure cases and builds scoring around what actually matters for your product, at the cost of more upfront engineering time.
Who This Is For
AI product teams shipping LLM features who need a real go/no-go signal before each release, not a vibe check
Startups using RAG or agents who need RAGAS-style scoring layered on top of their existing system
Regulated industries (healthcare, finance) needing red-teaming and safety evaluation as part of compliance documentation
Teams that have been burned by a model upgrade silently breaking something users only discovered in production
Trusted Across 50+ Countries
Codersarts maintains a 4.9/5 client satisfaction rating across hundreds of engagements. Clients consistently highlight follow-through and technical depth — Vivek (India) described the team's ability to break down complex concepts into something genuinely usable, while Salim (UAE) pointed to the team's responsiveness in meeting tight deadlines.
Results
A consumer AI startup cut its hallucination rate by 32% after we built a continuous eval-in-production pipeline that flagged drift weekly instead of waiting for user complaints.
An enterprise SaaS company caught a silent regression introduced by a model provider's update during red-team testing — before it reached production, avoiding what would have been a customer-facing incident.
A fintech AI team built a RAGAS-based regression suite that now runs automatically on every prompt change, cutting manual QA time by roughly 70%.
(Client names withheld under NDA; case studies available on request.)
Pricing
Starter
Scope: Single eval pipeline, core accuracy/safety metrics, one-time benchmark report
Price: $15,000–$25,000, or $3,000/mo retainer
Production
Scope: LLM-as-judge scoring, RAGAS integration, CI-integrated regression suite
Price: $25,000–$40,000, or $5,000/mo retainer
Enterprise — LLM Red Teaming Services
Scope: Adversarial/red-team testing, continuous eval-in-production monitoring, compliance documentation
Price: $40,000–$50,000+, or $8,000/mo retainer
For context: enterprise-grade LLM evaluation programs typically range from $125,000 to $820,000 annually in the US market. Our pricing reflects high-quality offshore delivery at a fraction of that, scoped to what most mid-market and startup teams actually need.
How We Work
Failure-mode discovery (Week 1) — review your product, past incidents, and known weak spots
Build (Weeks 2–4) — benchmark suite, scoring pipeline, CI integration
Calibration (Week 5) — tune scoring against human judgment until it's reliable
Launch & retainer — ongoing monitoring, new test cases as your product evolves
Why Codersarts
As an LLM benchmark testing company, we build evaluation around your product's real failure modes, not generic leaderboard metrics that don't predict what actually breaks in production. You get an engineering team under a fixed-scope contract, with eval pipelines designed to integrate directly into your existing CI/CD, not a one-off report that goes stale the moment you ship the next update.
Related Services
RAG Engineering & Deployment — for teams that need both the system and the eval harness built together
AI Agent Development — agentic systems need their own evaluation approach beyond standard LLM-as-judge scoring
MLOps / LLMOps Infrastructure — for teams that want eval wired directly into production monitoring
AI Strategy & Architecture Audit — if you're not sure how exposed you currently are
Get Started
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FAQ
How is this different from just using a public benchmark like MMLU? Public benchmarks measure general model capability, not whether your specific product fails on your specific edge cases. We build evaluation around your actual failure modes and user inputs.
Do you cover red-teaming for jailbreaks and prompt injection? Yes — our Enterprise tier includes adversarial testing specifically for jailbreaks, prompt injection, and harmful-output edge cases, with documentation suitable for compliance review.
Can this integrate with our existing CI/CD pipeline? Yes — the Production and Enterprise tiers are built to run as part of your CI pipeline, so evaluation runs automatically on every prompt or model change rather than requiring manual triggering.
We already have a RAG system. Can you just add evaluation on top? Yes — this is one of our most common engagements. We layer RAGAS-based scoring (retrieval precision/recall, faithfulness, answer relevance) onto an existing system without requiring a rebuild.
How long until we see results? Starter tier delivers an initial benchmark report in 2–3 weeks. Production tier's full CI-integrated suite typically takes 4–5 weeks to calibrate and launch.