AI agents that complete multi-step tasks in your systems.
Mindrops designs and engineers AI agents and agentic workflows that act inside your systems through typed tools, with retrieval, human-in-the-loop review, guardrails and tracing on every step.
A selection of the software Mindrops has designed and engineered for clients.
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.
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.
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 builds production AI agents and agentic workflows that plan and take actions — calling tools, retrieving data and completing multi-step tasks across an organisation’s systems. Each is engineered with retrieval, human-in-the-loop checkpoints, guardrails and observability, integrated with existing systems.
01
Agentic workflows
Multi-step agents that plan, call tools and complete real tasks across an organisation’s systems and APIs.
02
Retrieval (RAG)
Answers grounded in the organisation’s own documents and data, with citations and up-to-date sources.
03
Tool & API use
Agents that act — creating records, triggering workflows and orchestrating services through safe, typed tools.
04
Human-in-the-loop
Review and approval checkpoints where the stakes are high, so automation never runs unchecked.
05
Guardrails & safety
Input/output validation, permissioning, rate limits and policy controls that keep agents inside the lines.
06
Observability & evals
Tracing, evaluation and monitoring that measure quality and catch regressions in production.
Who we build AI agents for
Operations teams buried in triage
Requests arrive faster than people can read, classify and route them. An agent sorts, prioritises and drafts the response, escalating anything uncertain.
Back offices re-keying between systems
Staff copy data from one application to the next to finish a task. An agent calls the same APIs through typed tools and leaves an auditable record.
Product teams adding “do it for me”
Users want the product to complete tasks on their behalf. Agentic features plan the steps and act inside your app with the user’s permissions.
Leaders burned by a fragile demo
A prototype agent worked on stage and failed on real inputs. We rebuild it with evals, guardrails and tracing so it holds up in production.
Why Mindrops for AI agents
Planning kept apart from answering
In BeanBot, a planner model only decides which lookups to run and never writes the reply; a separate step answers from the results. We use the same split so agents cannot invent actions or data.
Facts rebuilt from source data
Prices, records and links are rebuilt from authoritative data after the model responds, so a missed field never reaches a user. Agent actions get the same backstop.
Escalation designed in
The India Post platform auto-responds to routine grievances and escalates complex cases to a central team for the final reply — the pattern we use to decide which agent steps need a person.
Every step traced
Each tool call, retrieval and decision is logged, so you can see why an agent acted and run regression tests on its behaviour before every release.
Technologies we use
Models & orchestration
OpenAI / LLMs · Anthropic · RAG · Function calling
Data & retrieval
Vector DBs · PostgreSQL · Embeddings · OCR
Backend
Python / FastAPI · Node.js · Queues · Webhooks
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.”
An AI agent uses a large language model to plan and take actions — calling tools, retrieving data and completing multi-step tasks. Mindrops builds agents that act safely inside an organisation’s systems.
How does Mindrops stop agents from making mistakes?
With guardrails: input/output validation, permissioning, human-in-the-loop checkpoints for high-stakes steps, and observability with evaluations so quality is measured and regressions are caught.
Can agents use internal data and tools?
Yes. Mindrops grounds agents in an organisation’s documents and data with retrieval (RAG) and gives them safe, typed tools to act on its systems and APIs.
How is this different from a chatbot?
A chatbot answers; an agent acts. Agentic workflows plan across multiple steps and take real actions. For conversational interfaces specifically, see the AI-powered chatbots service.
Which models power the agents?
It depends on the step: planning needs strong reasoning, while routine lookups can run on smaller, faster models or no model at all. Mindrops is model-agnostic — including OpenAI and Anthropic — and chooses per step for quality, latency, cost and data residency.