Nexorc
SData engineering and analytics

Your data out of PDFs, registers and spreadsheets, into one place, with reports that add up.

Getting the data a business already has — in PDFs, paper registers, spreadsheets and old systems — into one place it can be searched and counted, kept current by pipelines that run on their own, with reports generated from it rather than rebuilt by hand.

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01What it covers

What’s included.

  • 01

    Document and PDF extraction

    Structured documents read with their sections and tables intact, ready to search or to feed an assistant.

  • 02

    Data pipelines

    Data gathered from where it lives into one store, on a schedule, with duplicates caught and every run logged.

  • 03

    Reports and dashboards

    Daily reports, cash books and statements generated from the records, so they always add up.

  • 04

    Search over your data

    Search that understands both meaning and exact terms, so a precise clause or part number is never lost.

  • 05

    Machine-learning models, kept running

    Models trained on your data where they genuinely help, and watched after launch so they keep working.

02How it runs

What an engagement looks like

We find where each number really comes from and who trusts it. Pipelines are built to be rerun safely, with duplicates detected and every source recorded, so a figure in a report can be traced back to the document it came from.

You end up withOne source for every number, and reports that foot.

Technologies we use for this

  • Python
  • PostgreSQL
  • Qdrant
  • pgvector
  • Celery
  • Redis
  • SQLAlchemy
Everything we work with
  1. 01

    Listen

    Every business has its own dialect: its documents, its shorthand, the way work passes between people. We learn yours before we build anything.

  2. 02

    Learn

    We build systems that remember what your business knows and answer from it, show the source, and say “I don’t know” when the answer isn’t there.

  3. 03

    Build

    The AI, the software and the systems around them hand work to each other cleanly, each part with one clear job, like a pod.

  4. 04

    Hand off

    We teach your team to run what we built, and we hand it over, so the knowledge lives with your people and not with us.

03The work

Work behind it

Case study
Case study · Delivered

A policy assistant that explains cover from the wording, and never advises

An assistant for business-pack insurance that answers only from the approved policy document, shows the clause it relied on, refuses to recommend, and keeps a record of every answer for audit.

04Questions

Questions people ask.

Our data is messy. Is that a problem?

It is the usual starting point. Part of the work is finding where each number really comes from and cleaning it on the way in, with the original kept.

Can you work with scanned documents?

Yes, with text recognition where the document is a scan. We check what comes out against the original before it is relied on.

Who can see the data?

Only the people and systems you decide. Access is set by role, and personal data is handled under India's DPDP Act.

06Contact

Tell us what you need.

An AI agent, a system, a process to automate, or something you can't name yet. You'll get a real reply from the person who'd do the work, not a sales sequence.