AI that does real work inside your business.

We design, build and run practical AI systems: document extraction, LLM assistants, and the data pipelines behind them.

Start a project See what we build
Document → dataSample invoice

TAX INVOICE

Sharma Textiles Pvt Ltd
GSTIN 07AAACS1234F1Z5
Invoice No. ST/26-27/0193
Date 14-10-2026

Taxable value ₹1,24,500.00
CGST 9% ₹11,205.00 · SGST 9% ₹11,205.00
Total ₹1,46,910.00

{
  "vendor": "Sharma Textiles",
  "gstin": "07AAACS1234F1Z5",
  "invoice_no": "ST/26-27/0193",
  "date": "2026-10-14",
  "taxable": 124500.00,
  "gst_total": 22410.00,
  "total": 146910.00,
  "checks": "passed"
}
Hover a field to see where it came from. Totals are cross-checked before anything reaches your system.

What we build

We take one workflow that eats your team's time and make AI handle it reliably, end to end, connected to the systems you already use.

  • Document intelligence

    Pull fields, tables and line items out of PDFs, scans, emails and web pages. Every value comes with a confidence score, and uncertain ones go to a person for review.

    InvoicesContractsKYC formsFinancial filings

  • LLM assistants and agents

    Chat, search and task assistants grounded in your own documents and tools, so answers cite their sources and actions stay within limits you set.

    RAGTool useInternal knowledge search

  • Data pipelines

    Ingestion, cleaning and storage that keep models supplied with fresh, trustworthy data, built in Python and deployed on the cloud you prefer.

    ETLCloud deploymentScheduling

  • Evaluation and guardrails

    Test sets, accuracy metrics and monitoring, so you know how well the AI performs before launch and notice quickly when something drifts.

    Eval suitesMonitoringHuman review loops

  • AI strategy and prototypes

    Find the use cases that are worth doing, estimate the payoff, and prove the idea with a working prototype on your real data.

    Use-case reviewProof of concept

How a project runs

Short, visible steps. You see a working version early and decide whether to go further.

  1. 1

    Discover

    About 1 week

    We map the workflow, look at sample data and agree on what success means in numbers.

  2. 2

    Prototype

    2–3 weeks

    A working prototype on your own documents and data, measured against those numbers.

  3. 3

    Build

    Scoped per project

    Production version with integrations, security, evaluation and a review screen for your team.

  4. 4

    Run

    Ongoing

    We monitor accuracy, handle new document types and keep improving the system.

Our own products are on the way.

Alongside client work, we're building AI products of our own from what we learn on real projects. We'll share details here soon.

Get product updates

Tell us what you want to automate.

[email protected]
The taskWhat your team does by hand today and how often.
The dataA few sample documents or a description of your data.
The goalTime saved, accuracy needed, or the decision it should support.