AI & ML · Three Index
AI features that survive contact with real users.
We add AI where it changes a workflow — retrieval, classification, assistants, document intelligence — with evaluation, guardrails and a path to production.
- Team
- 50+ IT professionals
- Delivery
- Design, build, release, support
- Engagement
- Project or dedicated team
What this covers
Concrete work we take on under ai & ml — not a catalogue of buzzwords.
Document extraction and classification pipelines
RAG over your private knowledge base
Model evaluation, prompt versioning and cost control
Problems we take on
If one of these sounds familiar, we have likely shipped something close — and we will say so if we have not.
A demo works in a notebook but fails on real customer data.
You need AI inside an existing product, not a separate experiment.
Compliance and auditability matter as much as accuracy.
Who this is for
This is for teams that already have a product and a workflow where an assistant, a classifier, or document extraction would change the work. The buyer has usually seen a demo and needs the same behaviour on real records, with a review path when the model is wrong.
It is a poor fit for a standalone experiment that will never be wired into the product people already use. We will say so if the useful move is a better form, a better search index, or a human step.
How an engagement starts
Written scope
We name the task, the documents or records it runs on, who is allowed to see the output, and what “good enough” means on a sample set. That sample set is part of the scope. A notebook that works on five happy files is not the acceptance test.
First increment
The first increment runs inside the existing product on staging: one workflow, an evaluation set, and a way to see what the model returned and why a human would override it. Prompt and model versions are recorded so a later change is comparable.
Decisions we make with you
A hosted foundation model plus retrieval is the faster path when your documents and permissions can be grounded and the volume fits an API budget.
Training or fine-tuning is the path when you have labelled data the foundation model keeps missing and the cost of those misses is high enough to justify the pipeline.
Human review stays in the loop where a wrong answer has a clinical, financial, or customer consequence. Fully automatic output is reserved for low-stakes classification where you can measure the error rate on an ongoing sample.
What you own
You own the application code, the prompts and evaluation sets in the repository, the vector index if there is one, and the cloud account the feature runs in. Model provider keys live in your account.
- You own
- Application code
- You own
- Prompts and evaluation sets in the repository
- You own
- Vector index if there is one
- You own
- Cloud account the feature runs in
Where this shows up
The clearest fit in our delivery is work that already depends on reading records carefully. Chart review is that pattern: documents, a structured task, and a person who has to stay accountable for the result.
Other products get the same treatment when a workflow inside them needs retrieval or classification, and when auditability is part of the requirement.
Further reading
Adding AI features to existing products without the hype
Putting a feature into a live product, including evaluation and cost.
ReadRAG and GenAI inside business software
Retrieval over private material.
ReadTechnologies
Stacks we use when they fit the product and your team — not a mandatory toolkit for every engagement.
- Stack
- Python
- Stack
- OpenAI / Gemini APIs
- Stack
- LangChain
- Stack
- Vector DBs
- Stack
- FastAPI
- Stack
- AWS/GCP ML
AI development company
Looking for the company-level AI landing — agents, RAG, copilots and how we ship production AI? See AI Development Company.
How we work
Scope first, named engineers, biweekly demos, and you keep the repo.
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Scope before code
We write what is being built, what is excluded, integrations, dates and cost basis before the build starts. Changes get quoted before they get coded.
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Talk to the people doing the work
You meet named engineers and designers. There is no account manager translating technical questions in both directions.
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Working software every two weeks
Increments land on staging you can open and use. Progress you can click on beats progress in a status report.
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You own everything
Repository, cloud account, domains and credentials stay yours. We work in your accounts wherever possible.
FAQ
Answers specific to ai & ml — stacks, engagement and what we actually ship.
Do you train custom models?
When the data and problem justify it. Many products ship faster and safer on well-tuned foundation models plus retrieval.
How do you handle hallucinations?
Grounding, evaluation sets, human review loops where stakes are high, and clear UI that shows uncertainty.
Can you add AI to an existing product?
Yes — assistants, document intelligence and retrieval features inside your current stack, not a separate experiment that never ships.
How do you control cost and quality?
Prompt and model versioning, evaluation sets, usage limits and monitoring so accuracy and spend stay visible after launch.
What about private or sensitive data?
We design for your data residency and access rules — private retrieval, audit trails and human review where the stakes require it.
Related
Services that often sit beside ai & ml on the same roadmap.
Why Three Index
AI-first software development company in Ahmedabad. Founded 2020. Fifty-plus IT professionals. Five hundred-plus projects shipped.
We are a software engineering company: large enough to staff serious products and small enough that the people who wrote a module can still explain it. See how we operate, our AI development work, browse case studies, read the FAQ, or join the team.
Tell us what you are trying to build.
Send a short description of the project. You will get a reply from someone technical — with questions worth answering, not a brochure.