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Teams & Engagement

QA & AI Evaluation

Testing for software and AI: automated tests, LLM evaluation sets, red-teaming and regression checks.

Book a discovery callHow we build

In short

What do LLM evaluation services include?

LLM evaluation services measure whether an AI feature gives good answers, using an evaluation set: real inputs with the answers or behavior you expect, scored automatically and by people on every change. KaliIO builds evaluation sets, LLM judges, red-teaming and regression checks, alongside automated tests for the rest of the software.

What's included

Quality you can measure, not guess

If you can’t measure an AI feature, you can’t safely change it. Evaluation is what makes improvement possible.

  • Evaluation sets built from real cases and edge cases
  • Automated scoring: rules, reference answers, LLM judges
  • Human review rounds with your domain experts
  • Red-teaming for unsafe, off-policy or leaked output
  • Regression checks in CI on every prompt or model change
  • Automated UI, API and load tests for the rest of the app
scope/qa-ai-evaluation.yaml
set:       real questions + edge cases
score:     reference, rules, llm judge
human:     expert review of a sample
red team:  jailbreaks, pii, policy
ci:        block release on regression
report:    score trend per release

How we build

Five steps from idea to production

Every engagement follows the same path, so you always know what comes next and what you get at each step.

  1. Discover

    We learn your goals, data and constraints, agree on what success looks like, and check that AI is the right tool for the job.

    What you get

    • Problem statement and success metrics
    • Review of your data and systems
    • Scope and delivery plan

  2. Prototype

    A working prototype on your own data, so you can judge the approach before committing to a full build.

    What you get

    • Working prototype
    • Approach and model options
    • Risks and open questions

  3. Pilot

    A limited rollout to real users, measured against the success metrics agreed in discovery.

    What you get

    • Pilot with real users
    • Evaluation results
    • Go / no-go recommendation

  4. Production

    We harden it for real traffic: security, integrations, monitoring and documentation.

    What you get

    • Production deployment
    • Integrations and access control
    • Monitoring and runbooks

  5. Run & improve

    After launch we track quality, cost and drift and keep improving it, or hand it over to your team.

    What you get

    • Quality and cost monitoring
    • Regular improvements
    • Handover to your team

Use cases

When evaluation pays for itself

  • Switching models

    Compare a new model or provider on your own cases before you switch.

  • Regulated answers

    Show reviewers how often answers are correct and how failures are caught.

  • Customer-facing assistants

    Catch wrong or off-brand answers before customers see them.

  • Agents that take actions

    Test that agents use tools correctly and stop when they should.

  • Inherited AI features

    Measure an AI feature someone else built before you decide to fix or replace it.

Case studies

AI that ships, not slideware

What we built, for whom, and what changed. Every result shows how it was measured.

Case study Trilliant Software · India

Website and SEO content pages for an HR SaaS: Trilliant Software

KaliIO built the full website for Trilliant Software, an HRMS and payroll SaaS in India, including SEO content pages: 364 HR glossary terms, 47 free HR tools and 10 guides.

HR glossary pages live
364
Published at /hr-glossary/ on trilliantsoftware.com, September 2026
Free HR tools live
47
27 letter generators, 11 calculators, 4 job-description pages, 5 policy and document tools; published at /tools/, October 2026
  • HR & Recruitment
  • SaaS & Startups

Case study GSynq · India

GSynq: a motion-rich website its SEO team edits itself

GSynq, a digital marketing studio in India, launched a custom WordPress site by KaliIO with scroll-driven motion that its SEO team edits in Elementor.

  • Marketing & Advertising

Case study Relax Agro · India

Relax Agro: one app for egg orders, stock and deliveries

Relax Agro, an egg distribution business in India, moved orders, stock and deliveries from Excel and paper into one mobile and web app built by KaliIO.

  • Agriculture & AgriTech
  • Logistics & Supply Chain
See all case studies

Tech stack

Proven tools, no lock-in

We pick the model and stack that fit your data, budget and team, and build so you can switch later.

  • Anthropic Claude
  • AWS
  • Azure
  • Django
  • Docker
  • FastAPI
  • Flutter
  • Google Cloud
  • Google Gemini
  • Hugging Face
  • Kubernetes
  • LangChain
  • LangGraph
  • Llama
  • LlamaIndex
  • MongoDB
  • n8n
  • Next.js
  • Node.js
  • OpenAI
  • pgvector
  • Pinecone
  • PostgreSQL
  • Python
  • PyTorch
  • React
  • React Native
  • Redis
  • TensorFlow
  • TypeScript

FAQ

Questions we often get

How much does an AI project cost?

It depends on scope, not on a rate card. The main cost drivers are how many workflows the system covers, how ready your data is, which systems it must connect to, the model and its usage costs, and your security or compliance needs. On a discovery call we go through them with you and agree the scope before building.

How long does it take to build an AI product?

It depends on scope and on how ready your data is. Every project moves through the same steps: discovery, prototype, pilot, production, then ongoing improvement. The prototype comes first, so you see the idea working on your own data early, and the timeline for each later step is agreed before that step starts.

What is the difference between a prototype and a pilot?

A prototype shows the idea working on a sample of your data, usually without production integrations. A pilot puts a working version in front of a small group of real users, inside their actual workflow, and measures it against the success metrics agreed in discovery. The pilot result decides whether, and how, to move to production.

What do you need from us to get started?

A short description of the problem, an idea of what success looks like, and sample data or documents if you have them. It also helps to have one person who knows the workflow well and can answer questions during discovery. Book a discovery call and we will take it from there.

Related services

Dedicated AI Team

A full team of AI engineers, developers and a delivery lead working as part of your company.

Hire AI Developers

AI/ML and LLM engineers you interview and choose, who join your team, tools and stand-ups.

Support & Maintenance

Monitoring, fixes and improvements after launch, under a clear service agreement.

Start a project

Tell us what you want to build

A few lines are enough: the problem, the data you have and where it should run. We reply with next steps.