Copilots, RAG search and document AI grounded in your own data.
Mindrops designs and engineers LLM applications — copilots, retrieval (RAG) and document intelligence — grounded in your data, with guardrails, evaluation and monitoring in production.
Software Mindrops has designed and built for clients in this area.
Total post offices1,65,000The largest postal network in the world
Government · GovTech
India Post — Grievance Redressal
An AI-automated platform on Mindrops’ LazyMonkey foundation that categorises, prioritises, routes and auto-responds to every grievance, with one SLA-tracked record for central and regional teams.
Awarded via GeM (Nov 2025), it auto-triages thousands of grievances a day for a network of 1.5 lakh+ post offices.
Cafés in India50+outlets across India, served by the systems we builtBrewing since 1963
AI · Food & Beverage
BeanBot — The Coffee Bean & Tea Leaf
An embeddable AI chat assistant grounded in live catalogue and store data, with an “Order Now” action on every product and a no-code admin console for content and pricing.
Live across 50+ outlets in India, handling about 10,000 conversations a month and around 50 orders a day.
Extraction accuracy99%with human-in-the-loop review, on tens of thousands of documents a monthUK accountancy
AI · Accounting
Caelum AI — Document Processing
A Mindrops document-intelligence product that extracts transactions, line items, totals and dates to Excel, CSV, JSON or XML through a REST API, with human-in-the-loop review.
Delivers 99% extraction accuracy on supported documents across bank statements, invoices and receipts.
Mindrops designs and engineers applications powered by large language models — copilots, chat, retrieval-augmented generation (RAG) and document intelligence — for production. Each is grounded in a client’s own data with citations, guardrails, evaluation and monitoring.
01
LLM apps & copilots
Assistants and copilots embedded in a client’s products and workflows.
02
Retrieval (RAG)
Answers grounded in a client’s documents and data, with citations and up-to-date sources.
03
Document AI & OCR
Extract and structure data from invoices, statements and forms — proven at 99% on 25+ UK bank formats in Caelum AI.
04
Prompt & model tuning
Prompt engineering, evaluation and fine-tuning to hit quality, latency and cost targets.
05
Guardrails & evals
Validation, permissioning, human-in-the-loop and automated evals so quality is measured and safe.
06
Integration
LLM features wired into a client’s apps, data and tools with secure, typed interfaces.
Who we build generative AI for
Finance & operations teams buried in documents
Invoices, statements and forms are keyed in by hand. LLM and OCR extraction turns them into structured records that a person confirms.
Organisations with knowledge nobody can find
Policies, manuals and product data sit in documents that search handles badly. RAG answers questions from them, with a citation to the source.
Product teams adding AI features
Customers expect summaries, drafting or natural-language search inside the product. We build these as native features of your app.
Teams with a stalled GenAI pilot
The prototype answered well in testing but hallucinates or costs too much at volume. We add grounding, evals and routing to make it production-ready.
Why Mindrops for generative AI
We run our own GenAI product
Caelum AI, our document-intelligence engine, extracts data from receipts, invoices and statements across 25+ UK banks at 99% accuracy. Client systems get the same engineering.
Answers grounded in retrieved data
Retrieval over pgvector, citations, and a rule that the model names only what a lookup returned — the anti-hallucination design we shipped in BeanBot for a café chain’s live menu.
A person confirms what matters
In our invoice builds, AI reads the document and a person commits the record, so nothing becomes data unchecked. We design that checkpoint into any workflow where an error has a cost.
Quality measured on every release
Every release is scored against an evaluation set built from your real inputs, so a prompt or model change ships with evidence of its effect on accuracy.
Technologies we use
Models & orchestration
OpenAI / LLMs · Anthropic · RAG · Function calling
Data & retrieval
Vector DBs · Embeddings · PostgreSQL · OCR
Backend
Python / FastAPI · Node.js · Queues
Ops & safety
Tracing · Evals · Guardrails · SSO / JWT
Client reviews
AI vehicle monitoring
Computer vision at a mine gate records number plates, vehicle movements and trip counts for daily reporting.
From the client’s review
“Daily reports are ready on their own, so nobody has to sit and prepare them at the end of the shift.”
A mobile app, website and admin dashboard connecting patients in rural and semi-urban India with doctors, with video consultations, digital prescriptions and patient registration.
From the client’s review
“The UI/UX design process was collaborative and they kept the end user in mind throughout, which mattered a lot for a platform targeting rural users.”
It is the building of applications powered by large language models — copilots, chat, retrieval-augmented generation (RAG) and document intelligence. Mindrops builds these for production, grounded in a client’s own data with citations, guardrails and evaluation.
What can be built with generative AI?
Copilots and assistants, retrieval-augmented (RAG) search over a client’s data, document intelligence and OCR, content generation, and LLM features embedded in products.
How are hallucinations prevented?
Mindrops grounds models in a client’s own data with retrieval and citations, adds guardrails and validation, and runs automated evals so quality is measured before and after release.
Can it use private data securely?
Yes. Mindrops uses retrieval and access control so models draw on a client’s knowledge without exposing it; deployment can be managed, self-hosted or hybrid for data residency.
Does Mindrops have real generative AI in production?
Yes — Caelum AI reads receipts, invoices and statements from 25+ UK banks at 99% accuracy, and Mindrops has built AI invoice capture with OpenAI extraction and human-in-the-loop.
Do you fine-tune models or use them off the shelf?
Usually retrieval and prompt engineering come first, because they are cheaper to maintain and keep answers tied to your current data; fine-tuning is used when evals show a clear quality, latency or cost gain. Mindrops is model-agnostic, including OpenAI and Anthropic.