Applied AI, data and automation for enterprises and governments.
Mindrops designs and engineers AI for production use — agents, generative AI, document intelligence and automation — with the guardrails, evaluation and governance that enterprise and government deployments require.
Mindrops is an AI development company that builds production-grade AI — agents and agentic workflows, generative AI and LLM applications, chatbots, intelligent automation, and the data, MLOps and governance behind them. We apply AI to real business problems, with human-in-the-loop review and guardrails.
AI, data and automation services
Ten specialised services across the AI lifecycle: what the AI does, how it is governed and operated, and the data and devices it depends on.
Software Mindrops has designed and built for clients in this area.
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.
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.
Mindrops built four production ML products in Python: predictive maintenance, computer-vision body measurement, multi-channel attribution and deep-learning manpower forecasting.
25% lower maintenance cost, 20% fewer sales returns, 15% more sales and 90% better forecasting accuracy for the respective clients.
A read-only validation platform that runs 30+ checks across seven detection modules — including eight statistical anomaly methods — with one governed review workspace.
Every client close is now checked the same way and tracked from one dashboard, with 100% of findings carrying an audit trail for sign-off.
A mobile-first platform with a web admin portal where OCR and AI read six fields from each invoice and a person confirms every record before it is saved.
100% of invoices are confirmed by a person before saving, producing a governed, searchable record from the very first upload.
Invoices, statements, receipts and forms turned into structured, validated records — the capability behind Caelum AI and our invoice-automation builds.
02
Grounded assistants & search
Answers over your own catalogue, policies and knowledge base, with retrieval, citations and a rule that the model names only what a lookup returned.
03
Triage, routing & decisioning
High-volume requests and cases classified, prioritised and routed by type, urgency and sentiment, with people making the final call where it matters.
04
Forecasting & prediction
Demand, manpower and maintenance forecasts from classical and deep-learning models, trained on the history you already hold.
05
Anomaly & error detection
Statistical and ML checks that surface duplicates, outliers and gaps in financial or operational data before they reach a report.
06
Computer vision
Image-based measurement and inspection models, such as the body-measurement tool we built for a fashion size-recommendation platform.
Why Mindrops for AI development
AI systems in production
Our AI already runs real workloads: Caelum AI reads documents from 25+ UK banks at 99% accuracy, and the India Post grievance platform triages thousands of citizen complaints a day.
LLM calls kept for open-ended work
We send the predictable path through rules, search and databases and reserve LLM calls for open-ended work — the design that keeps most BeanBot conversations fast, cheap and model-free.
Guardrails, review and audit trails
Guardrails, human-in-the-loop review where the stakes are high, evaluation and monitoring in production, and audit trails that make decisions explainable — aligned to the EU AI Act and ISO/IEC 42001.
One team across the stack
The same engineers build the data pipelines, the model layer, the web and mobile apps and the cloud they run on, so AI arrives integrated and secured under a CMMI Level 3, secure-SDLC process.
Technologies we use
Models & orchestration
OpenAI / LLMs · Anthropic · RAG · Function calling
Data & retrieval
Vector DBs · Embeddings · PostgreSQL · OCR
Build & serve
Python / FastAPI · PyTorch · Docker · Serverless
Ops & safety
Tracing · Evals · Guardrails · SSO / JWT
Cloud
AWS · Azure · GCP
Governance
EU AI Act · ISO/IEC 42001 · NIST AI RMF · GDPR
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.”
We assess the process, the data available and the risk level, turn “good” into measurable acceptance criteria, and return a scoped estimate.
02 — Proof of concept
A PoC scored by evals
A working slice on your real data, measured against an evaluation set, so the go/no-go decision rests on measured results.
03 — Production
Harden & integrate
We add guardrails, access control, human review steps, integrations and load handling, then release in increments.
04 — Monitoring
Operate & improve
Tracing, quality and drift monitoring, cost tracking and scheduled re-evaluation keep the system accurate as data and models change.
Frequently asked questions
Talk to our team
Message, call or email us about your project. We'll go through your objectives, suggest the most cost-effective approach and give you a rough timeline and cost.
Reach out today. We'll get back to you as quickly as possible.
How do I choose between your AI services?
Choose by the outcome you need. If AI should act across your systems, start with AI agents; if it should answer people, AI chatbots; if it should read documents, document intelligence; if it should generate content, generative AI; if it should run a rules-heavy process end to end, intelligent automation. Data engineering, BI, LLMOps, governance and IoT supply the foundations and controls, and discovery confirms the fit.
What does an AI engagement usually include?
Typically discovery, data preparation, a proof of concept measured against an evaluation set, the production build with integrations and guardrails, and monitoring after launch. Governance documentation and MLOps pipelines are added when the use case calls for them.
How is an AI project priced?
Against a scoped estimate produced in discovery. Well-defined builds can run as fixed scope, proofs of concept and exploratory work usually suit time & materials, and ongoing AI products often use a dedicated team — see engagement models. Expected model and hosting costs are estimated alongside, so you see the running cost before you commit.
How long does it take to get AI into production?
It depends on data readiness, the number of integrations and the risk level of the use case, so timelines are agreed in discovery, once those factors are known. Work ships in increments — a proof of concept first, then production releases hardened with LLMOps & MLOps — so quality is visible early.
Which models do you use, and can they use our private data?
We are model-agnostic and choose per use case — including OpenAI and Anthropic — balancing quality, latency, cost and data residency. Models are grounded in your data through retrieval (RAG) with access control, and can be self-hosted where residency rules require it.
How do you keep AI safe and compliant?
With human-in-the-loop review, guardrails, evaluation, monitoring and audit trails, aligned to the EU AI Act and ISO/IEC 42001. See AI governance.
What support do you provide after launch?
Monitoring, re-evaluation, maintenance and enhancements, under a managed support or AMC arrangement if you want one. AI systems need this ongoing work: data drifts, models are updated and prompts age, so we keep testing quality against the evaluation set and fix regressions.
Talk to our team
Get a quick view of the approach, timeline and cost for your idea.