Turn documents and records into datasets you can actually analyse
Most organisations do not have a data problem, they have a format problem — the answers are locked inside scanned PDFs, registers and spreadsheets that disagree with each other. We build the pipeline that turns those into one clean dataset, then the analysis on top of it.
For: Government departments, researchers and universities, publishers, cooperatives, and any business sitting on records nobody can query
The problem we usually walk into
The data exists. It is in a stack of scanned PDFs, three spreadsheets and a register, the same person is spelled four different ways, and nobody can answer a simple question like how many, where, or has this changed. So decisions get made on a sample somebody counted by hand.
See what this costs →What's included
Documents to datasets
Scanned PDFs, registers and forms in Indian languages converted into structured CSV, JSON or a database — the same pipeline behind our 70,000-PDF digitisation project.
Name matching and entity resolution
Deciding when two records are the same person or place despite spelling, transliteration, initials and name-order differences. This is the hardest part of Indian datasets, and we have done it at lakhs of records.
Deduplication and reconciliation
One row per real entity, with totals that tie back to the source so you can prove nothing was invented or lost.
Aggregate analysis
Counts, distributions, geography and change over time — summarised at ward, village, district or state level.
Reports and dashboards
A readable PDF or a live dashboard, in English or your regional language, built for the person who has to act on it.
Quality you can audit
Validation rules, confidence scores and an accuracy report per batch, with low-confidence records flagged for human review rather than silently guessed.
How we work
In this order, because each step depends on the one before it.
- Step 1
Define the question first
What decision does this data need to support? That determines what we extract and what we can safely ignore.
- Step 2
Sample and prove
We run a representative slice end to end and show you measured accuracy before scoping the full job.
- Step 3
Build the pipeline
Extraction, cleaning, matching and validation — repeatable, so next year's data takes hours rather than weeks.
- Step 4
Reconcile and deliver
Totals checked against the source, then datasets, reports and documentation handed over. You own all of it.
Questions we get asked
- How large a job can you handle?
- We have processed 70,000 scanned PDFs into more than 10 lakh structured records on a single project. Throughput scales with machines rather than people, so volume is rarely the constraint. Scan quality usually is.
- What makes Indian datasets harder than Western ones?
- Name matching. One person appears as a Telugu spelling, an English transliteration, an initial plus surname, and a surname-first variant across four documents. Off-the-shelf matching tools are built for Latin script and consistent name order, and they fail quietly here rather than loudly.
- Do you work with personal data?
- Sometimes, and we treat it as a liability rather than an asset. We work only from a lawful source, process on our own servers in India, extract only the fields the question actually needs, report aggregates rather than individuals, and delete raw data on an agreed schedule. We decline work whose output would profile identifiable individuals.
- What do we get at the end?
- The clean dataset, the analysis, the code that produced it and an accuracy report. Nothing is a black box, and you can re-run it yourself next year.
Tell us what you need
A 20-minute call, then a written fixed-price scope. No obligation.