LLM Evaluation and AI QA
Know whether your AI is right before your customers do.
We build evaluation for LLM applications: test sets from real questions, accuracy and groundedness scoring, safety and red-team checks, cost and latency tracking, regression tests on every change and monitoring after launch.
- Test sets
- Groundedness scoring
- Regression tests
Free 30-minute call with a senior specialist. Reply within one business day. Based in Jaipur, working with businesses across India.
- Discovery callFree 30 minutes with a senior specialist
- Scoped first phaseWritten scope, timeline and fixed price
- Build with weekly demosWorking software you can see every week
- Launch and supportGo-live with your team ready, then ongoing support
What is LLM evaluation?
LLM evaluation measures how well an AI application answers real tasks, using test sets, scoring for accuracy, groundedness and safety, and regression checks whenever prompts, models or data change. Stack18 builds evaluation for your chatbot, agent or RAG system so quality is measured, not assumed.
Last reviewed 30 October 2026 by the Stack18 delivery team in Jaipur. Security and AI governance · Pricing · About Stack18
| What it covers | Test set design; Scoring; Regression testing; Red-teaming; Production monitoring. |
|---|---|
| Typical first step | Discovery Sprint: 2 weeks, fixed price. |
| Build route | Fixed-scope first release: agreed in discovery; fixed price after discovery. |
| Long-term option | Transformation Pod: 12–36 months, annual contract. |
| Measured by | Answer accuracy; Groundedness rate; Regression failures caught; Safety violations. Each against an agreed baseline. |
| Governance | Human approval at 5 governance gates, every AI agent action logged, NDA available on request. |
| Delivery team | Based in Jaipur, India. A senior specialist replies within one business day. |
You need this when…
Tap the ones that apply to you.
LLM Evaluation and AI QA, end to end.
Each capability is delivered by the same team on the Agentic SDLC, with your approval at every critical step.
Test set design
Questions that reflect real use.
- Real and synthetic questions
- Expected answers and sources
- Coverage by topic
Scoring
Measure what matters.
- Accuracy and groundedness
- Refusal and safety checks
- Human and automated grading
Regression testing
Catch breakage before release.
- Run on every change
- Version comparison
- Release gates
Red-teaming
Test for misuse.
- Prompt injection tests
- Data leakage checks
- Policy violation probes
Production monitoring
Quality over time.
- Sampled review
- Drift and cost tracking
- Feedback loop
A typical engagement, step by step.
Senior people lead every phase. Specialised AI agents on the Agentic SDLC handle the repetitive work, and nothing ships without your approval.
Discovery
Map how AI quality works today, the systems and data involved, and agree the first measurable outcome.
Design
Design the workflows, roles, integrations and approval points for AI quality.
Build in weekly demos
Your team sees working AI quality software every week, with changes agreed in the open.
Launch and support
Data migration, training, go-live support and a measured 30-day review against the baseline.
Start small. Scale when it works.
Every engagement begins with a short, fixed-price step so you can judge the fit before committing further. See pricing.
Discovery Sprint
Teams deciding scope and priorities before they commit.
- Duration
- 2 weeks
- Pricing
- Fixed price
- Scoped plan and timeline
- Team and fixed price
- Process map or prototype
Fixed-scope first release
Teams that know what they need built.
- Duration
- Agreed in discovery
- Pricing
- Fixed price after discovery
- Written scope and acceptance criteria
- Weekly demos
- Handover with documentation
Transformation Pod
Multi-system programmes that run for years.
- Duration
- 12–36 months
- Pricing
- Annual contract
- Dedicated cross-functional team
- Quarterly business reviews
- One accountable partner
What we have shipped, and what a typical engagement looks like.
Delivered projects link to the full case study. Illustrative scenarios describe typical engagements and are not named client results.
Company with a support chatbot
- Challenge
- Accuracy unknown; prompt changes sometimes made answers worse.
- What we do
- Test set, scoring and release gates for every change.
Scoped in a 2-week sprint
Free AI quality review
Tell us where AI quality hurts most. We map what to fix first, whether ready-made or custom fits, and a realistic first step.
- Where to start and why
- Ready-made vs custom view
- Indicative timeline
We agree targets before we start.
Every engagement has a baseline and a target on the measures that matter to you, reported at every read-out.
LLM Evaluation and AI QA questions.
Can you evaluate an AI system someone else built?
Yes. We can assess any LLM application and report findings and fixes.
How big should the test set be?
Large enough to cover your key topics. We size it in discovery and grow it from real usage.
Is automated grading reliable?
It helps at scale, so we calibrate it against human review and keep humans on sensitive cases.
Do you test for prompt injection?
Yes. Red-team tests cover injection, leakage and policy violations.
What do we get?
A test set, scoring harness, baseline report, release gates and a monitoring set-up.
Guides, comparisons and proof.
AI Agents for Business: A Practical Guide
What AI agents are, how they differ from chatbots, where businesses use them, and how to start with governance…
Read →CompareReady-Made vs Custom Software: How to Choose
Ready-made systems go live in weeks at a fixed price.
Read →IndustryManufacturing ERP & Digital Transformation
ERP and operations software for Indian manufacturers: purchase, stores, production, dispatch and GST on one…
Read →GuideSoftware Development Cost in India: What Drives It
What drives custom software cost in India, how fixed price, time and materials and dedicated teams differ, and…
Read →Let's scope your LLM Evaluation and AI QA project.
Tell us where you are and where you want to be. A senior specialist will reply within one business day with next steps.
- Free 30-minute consultation
- Indicative estimate after the call
- NDA available on request