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AI & ML Engineering

LLM Fine-Tuning & Optimization

Fine-tune, distill and quantize models for accuracy, speed and cost, in the cloud or on your own servers.

Book a discovery callHow we build

In short

When should you fine-tune an LLM?

Fine-tune an LLM when prompting and retrieval are not enough: when you need a consistent format or tone, a narrow task done cheaply at high volume, or a model that runs on your own servers. KaliIO tests a prompt-and-retrieval baseline first, then fine-tunes, distills or quantizes only if the numbers show it is worth it.

What's included

Fine-tuning when the numbers say so

Fine-tuning costs data, compute and upkeep, so we prove the gain on your own evaluation set before and after.

  • A prompting and retrieval baseline, scored
  • Training data collected, cleaned and reviewed
  • Fine-tuning of open-source or hosted models
  • Distillation and quantization for speed and cost
  • Before-and-after evaluation on held-out cases
  • Deployment in your cloud or on your own servers
scope/llm-fine-tuning.yaml
baseline:  prompt + retrieval, scored
data:      reviewed examples, held-out set
method:    lora fine-tune, open model
shrink:    quantize to fit one gpu
compare:   quality, latency, cost
host:      your vpc or on premises

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

Where a tuned model beats a bigger one

  • Consistent structured output

    Reports, summaries or records that must follow your format every time.

  • High-volume, low-cost tasks

    Classification or extraction at volumes where a large model’s per-call cost adds up.

  • Private deployments

    Models that run inside your network because the data can’t leave it.

  • Brand voice and domain terms

    Replies and content in your tone, using your product and industry vocabulary.

  • Indian-language tasks

    Better results in Hindi or other languages where general models are weaker.

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

Generative AI Development

Custom generative AI features and products, from content and code generation to copilots that work on your own data.

LLM Application Development

Chat, search and document tools built on GPT, Claude, Gemini or open-source models, with evaluation built in from day one.

RAG & Knowledge Assistants

Assistants that answer from your documents and systems, with sources, permissions and measurable accuracy.

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.