Software Mindrops has designed and built for clients in this area.
SaaS · Influencer Marketing
Activate by Bloglovin'
A dedicated onshore and offshore Mindrops team built, upgraded and maintained the platform with Agile delivery — campaign matching, messaging and real-time reporting.
Maintained for 4+ years, scaling to 130,000+ influencers and 100,000+ brands on a network serving 16M+ users a month.
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 builds the pipelines, storage and governance that make an organisation’s data reliable and usable — batch and streaming pipelines, warehouses and lakehouses, data quality and governance, and the AI-ready data (features and vectors) behind dependable analytics and machine learning.
01
Pipelines & ETL/ELT
Reliable batch and streaming pipelines that move and transform data at scale.
02
Warehouse & lakehouse
Modern data platforms that give every team one trustworthy source of truth.
03
Data quality
Validation, testing and observability so decisions rest on clean data.
04
Real-time & streaming
Event streams and CDC for up-to-the-second data where it matters.
05
Data for ML & LLMs
Feature and vector pipelines that make an organisation’s data usable by ML and LLMs.
06
Governance
Lineage, access control and cataloguing for trust and compliance.
Who we build data platforms for
Companies starting an AI programme
Models are only as good as the data behind them. We assess readiness and build the pipelines and vector stores the first use case needs.
Teams with data in silos
Sales, finance and operations each keep their own numbers. A warehouse or lakehouse brings them into one governed source.
Analysts fighting broken reports
Pipelines fail silently and dashboards repeat yesterday’s mistakes. Tests, validation and observability catch issues upstream.
Products that need live data
Stock, pricing or status must update within seconds. Streaming and change-data-capture pipelines keep downstream systems current.
Why Mindrops for data engineering
Data layers behind live AI
We built the vector and fuzzy-search layer in PostgreSQL behind BeanBot and the extraction pipelines behind Caelum AI — the data engineering that working AI depends on.
Systems of record left untouched
Our bookkeeping validation platform reads a live ledger and writes findings to a separate store, never altering the source — the principle we apply to every integration with core systems.
Experience at real scale
Activate by Bloglovin’, an influencer-marketing SaaS we built and maintained for 4+ years, serves 16M+ users and 130k+ influencers; data at that volume has to be modelled and moved reliably.
The simplest platform for the use case
We recommend the leanest warehouse, lakehouse or database setup that meets the use case, so you do not pay to run infrastructure you do not need.
Technologies we use
Storage & compute
PostgreSQL · MySQL · MSSQL · PySpark
Pipelines
ETL / ELT · CDC · Streaming · Airflow-style
AI data
Vector DBs · Embeddings · Feature stores
Cloud
AWS · Azure · GCP · Docker
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 Mindrops team working alongside the client’s own engineers on software development and cloud-based projects, brought in to add reliable delivery capacity.
From the client’s review
“Mindrops handled complex requirements with ease and added value throughout the project.”
Building the pipelines, storage, quality and governance that make data reliable — and specifically structuring it (features, vectors) so analytics, ML and LLMs can use it.
Warehouse or lakehouse — which is needed?
It depends on the data and use cases. Mindrops assesses and recommends the simplest platform that meets analytics and AI needs without over-engineering.
Can Mindrops handle real-time data?
Yes. Mindrops builds streaming and change-data-capture pipelines for up-to-the-second data where the use case needs it.
How is data quality ensured?
Validation, automated testing and observability across pipelines, so issues are caught before they reach dashboards or models.
How does this support AI?
Reliable, well-structured data is the prerequisite for useful AI — Mindrops builds the feature and vector pipelines that its AI work depends on.
Contact us
Tell us where your data lives and what it needs to power. We’ll recommend the simplest platform that fits, and reply within one business day.