Generic answers
The model knows the internet, not your business, so answers are vague.
What we doConnect it to your documents and systems with retrieval, so answers use your facts.
Bridging the technology gap
We build applications on large language models that answer from your documents and systems: retrieval pipelines, fine-tuning where it helps, prompt engineering, and the guardrails to run it safely.
Applications. Assistants, search, summaries and document tools that people use every day
MOUNTSMY builds generative AI and LLM applications from Ahmedabad, India: retrieval-augmented generation (RAG) over your documents, fine-tuning, prompt engineering, multi-modal apps and the evaluation and guardrails that make them safe to run. We work with GPT, Claude, Gemini and open-source models, and the data stays in your own cloud accounts.
The difference between a demo and a tool people trust is what the model is allowed to say, and where it gets its facts.
An example path, drawn by us. Your discovery call shows yours.
Most pilots impress in a demo and then struggle in production. These are the usual causes.
The model knows the internet, not your business, so answers are vague.
The model sounds sure and is sometimes wrong.
Nobody wants company data sent to an unknown service.
A pile of ad hoc prompts that break when anything changes.
Usage grows and so does the bill.
It works on a laptop, and nobody owns the path to production.
The benefits depend on the use case, so we agree how to measure them before we build.
Staff and customers find what is in your documents without reading all of it.
Drafting, summarising and sorting handled by the model, with a person approving.
Your information stays in your accounts, under your access rules.
Sources shown with each answer, so people can check.
Right-sized models, caching and budgets from the start.
Evaluation, deployment and monitoring planned from day one.
Most projects start with the simplest option that works and move up only when needed.
| Technique | What it does | Often used when |
|---|---|---|
| Prompt engineering | Gives the model clear instructions and examples | The task is general and needs no private knowledge |
| RAG (retrieval-augmented generation) | Looks up relevant passages from your data and gives them to the model | Answers must come from your documents and stay up to date |
| Fine-tuning | Trains a model further on your examples | You need a specific style, format or behaviour that prompts cannot give |
| Agents and tools | Lets the model call your systems to take actions | The job involves doing things, not just answering |
We use the models and platforms that fit your data and budget.
Make your documents and data searchable by a language model.
We prove value on a small slice first, then widen.
Agree the problem, the users and how success is measured.
Share example questions and documents.
No build starts until you approve the plan.
Get your documents and data ready for the model.
Confirm which sources are allowed.
Sensitive content is excluded unless you approve it.
Assemble the pipeline and the first version of the app.
Try it and give feedback.
Built in your accounts, with your data only.
Measure quality before real users see it.
Review the results and agree the launch bar.
A release goes out only when it meets the bar you agreed.
Release it and keep it healthy.
Keep it yourselves, or keep us on monthly support.
You own the code, prompts and accounts.
We build what works for your data and budget, not what is fashionable.
Language models fail in predictable ways. We plan for each.
We start small, measure, and widen only when the numbers hold up.
Most clients begin with a short discovery.
A short review of your use case and data, with a feasibility note and a written plan. Yours to keep.
Good for: Knowing what is realisticA clear scope, timeline and price for a defined piece of work.
Good for: A specific goalFixes, audits and short tasks, billed by the hour.
Good for: Small or urgent jobsPart-time or full-time engineers working inside your tools, by the month.
Good for: Ongoing work and supportThe right use case depends on the documents you hold and the people who need answers.
It sits on top of good data and good delivery.
Something else on your mind? Ask us directly.
Ask a questionBuilding applications on models that generate text, images or code, such as GPT, Claude and Gemini, and connecting them to your data and systems so they are useful and safe.
Retrieval-augmented generation. The system first looks up relevant passages in your own documents, then gives them to the model to answer from, so answers use your facts and can show their sources.
Often not. Prompting and RAG cover most needs. Fine-tuning helps when you need a specific style, format or behaviour that prompts cannot give.
It depends on quality needs, privacy and budget. We compare options on your own test questions and recommend one, including open-source models where privacy matters.
We build in your own cloud accounts with access rules on every source. Where needed, we use private or open-source models so data does not leave your control.
By grounding answers in retrieved sources, showing citations, testing on known questions and refusing out-of-scope requests. No system removes the risk entirely, so high-stakes answers keep a human in the loop.
A focused first version can take a few weeks. The plan gives you a timeline before you commit.
It depends on the use case, the data and the model. There is no rate card: you get a written, itemised quote after a free call.
Yes. We expose it through APIs or embed it in your product or workflow.
You do. The code, prompts, pipelines and accounts are yours.
Book a free 30-minute call with an engineer. We’ll send a written plan and quote after.