Quantum Beetle

AI development company in India

AI development
company in India.

AI systems built for your problem, on your own data, on your own ground.

An AI development company in New Delhi, India: custom AI systems and products for enterprises — document and record intelligence, matching and de-duplication, classification, search and decision support — built rules-first, semantics next, AI reasoning last, with every decision explained, and deployed on the client's own infrastructure.

Talk to usWhat we build
Fig. 00Many minds, one answer

Specification first. Stages you can see. Code you own.

01What we build

Built for the people who use it.

An AI development company in New Delhi, India: custom AI systems and products for enterprises — document and record intelligence, matching and de-duplication, classification, search and decision support — built rules-first, semantics next, AI reasoning last, with every decision explained, and deployed on the client's own infrastructure.
Fig. 01Many minds, one answer

01

Document intelligence

Invoices, purchase orders, contracts, specifications, reports: read, structured into fields with a source and a confidence, and checked — so piles of text become data you can act on.

02

Matching and de-duplication

The same supplier under three names, the same part under four codes, the same payment made twice: found by comparing what records are, not how they were written.

03

Classification and extraction

Records sorted into the classes that matter to you and the critical attributes pulled out of free text, measured for precision and recall on your own labelled sample before anyone trusts it.

04

Enterprise search and knowledge

Answers from your own documents, for your own people, with the source shown and the answer never leaving your network — respecting the permissions you already have.

05

Decision support

Intelligence where decisions are made — procurement, finance, operations, master data — proposing, explaining and leaving the decision to a person.

06

AI inside products

Intelligence designed into the websites, apps and platforms we build, rather than a chatbot attached afterwards.

02The practice behind it

What the same team brings.

Each of these is a practice of its own; on your project they work as one.
Fig. 02One practice, many hands
Image · placeholderDocument Intelligence
01.d.2

Document Intelligence

Reading, structuring and reasoning over your documents at scale.

  • Reading of complex and unstructured documents
  • Structured data extracted into your systems
  • Reasoning and answers across document collections
The full story

Many organisations run on documents that were never designed to be read by a machine: scanned forms, contracts, technical drawings, reports and correspondence.

Document Intelligence reads them, turns them into structured data and reasons over what they contain. Different minds handle different kinds of document and different kinds of question, and agree on the result.

The output feeds your systems and your decisions, instead of sitting in a folder.

Image · placeholderMaster-Data Quality Layer
01.b.3

Master-Data Quality Layer

Continuous scoring and correction of master data across vendors, customers and materials.

  • Continuous quality scoring for vendor, customer and material records
  • Proposed corrections for review and approval
  • A visible measure of master-data health over time
The full story

Master data decays. Records are created in a hurry, fields are left empty, formats drift, and duplicates creep in. Every downstream process inherits the mess.

The Master-Data Quality Layer keeps a swarm working on it continuously. It scores records across vendors, customers and materials, finds what is incomplete, inconsistent or duplicated, and proposes corrections for your data owners to approve.

Quality becomes something you can see and manage, instead of something you discover when a process fails.

Image · placeholderDuplicate Payment Layer
01.b.1

Duplicate Payment Layer

Flags duplicate or suspicious payments in your existing finance system.

  • Works with your existing finance system
  • Duplicate and suspicious payments flagged with reasons
  • A clear path to the full ARGUS platform
The full story

Duplicate payments are one of the most common and most avoidable losses in finance. They happen through re-keyed invoices, small differences in reference numbers, vendors held under more than one record, and simple human error.

The Duplicate Payment Layer places a focused swarm alongside your finance system. It compares payments and invoices from several angles at once, and flags the ones that look like duplicates or look out of place, with the reasons shown.

It is the lightweight entry to ARGUS. You can begin here and extend into full transaction intelligence when it makes sense.

Image · placeholderEnterprise Search Layer
01.b.4

Enterprise Search Layer

Semantic search over your own documents and systems, inside your own walls.

  • Search by meaning, not only by keyword
  • Results linked back to the source
  • Runs on your infrastructure and respects existing permissions
The full story

Keyword search fails when people do not know the exact words used in the document they need. Answers exist, but they are hard to reach.

The Enterprise Search Layer gives your people search that understands meaning. A swarm indexes your documents and systems where they live and returns the passages that answer the question, with links back to the source.

It runs inside your walls and respects the access permissions you already have. It is the entry point to MNEMOS.

Image · placeholderCopilots & Agents
01.d.1

Copilots & Agents

Internal assistants and autonomous agents trained on your operations.

  • Internal assistants grounded in your operations
  • Agents that carry out defined tasks within set permissions
  • Explained, auditable actions
The full story

General-purpose assistants know a lot about the world and very little about your business. They cannot answer questions about your processes, your data or your rules.

We build copilots that assist your people and agents that carry out defined tasks, grounded in your operations. Behind each one is a swarm of specialised minds, each responsible for part of the work, checking one another before anything is returned or done.

They run on your infrastructure, act within the permissions you set and explain what they did.

Image · placeholderSovereign Deployments
01.d.4

Sovereign Deployments

Local models, local vector stores — your data never leaves.

  • Local models running on your infrastructure
  • Local vector stores for your own knowledge
  • No data sent to outside AI services
The full story

For many organisations, sending data to an outside AI service is not an option. Regulation, contracts or simple prudence rule it out.

Sovereign Deployments bring the whole stack inside your walls: local models, local vector stores and the tooling around them, running on infrastructure you control. The swarm works on your data without it ever leaving your ground.

We design, deploy and hand over the system so your team can run it with confidence.

Fig. 03From brief to better

03How we work

Why it is different.

01

Rules first, semantics next, reasoning last

What can be settled with certainty is settled by rules; meaning is handled by a semantic layer; language models are used only where the first two cannot decide. Cheaper, faster, and explainable.

02

Every decision explained

Each result carries a confidence and a record of which layer made the call and from what evidence, so your people — and your auditors — can check and overrule it.

03

On your infrastructure, local models by default

Deployed on-premise or in a private cloud you control. External AI services are an explicit choice, never a requirement. Your data does not leave.

04

Measured before trusted

Precision and recall on a labelled sample of your messy data, with the method explained — not a demo on clean examples.

The steps

  1. 01

    Discover

    We learn the business, the audience and what success means, before deciding what to build or run.

  2. 02

    Design

    Structure, interface and message, shaped with you and tested early.

  3. 03

    Build

    Engineering and creative produced in short, visible steps, with AI doing the heavy lifting and people doing the judging.

  4. 04

    Launch

    Shipped carefully, measured from day one.

  5. 05

    Improve

    We keep tuning what we built against real results.

04Questions

Before you ask.

Fig. 04Before you ask

Questions

What does an AI development company actually build?

Systems that read, match, classify, search and decide on a company's own data: document intelligence, de-duplication of records, classification and extraction, enterprise search, decision support, and AI designed into products. Not foundation models and not consumer chatbots — the list of AI companies in India names companies that do those.

Does our data have to go to OpenAI or another AI service?

Not with us. Our systems run on the client's own infrastructure with local language models by default; an external service is used only if you choose it, for a stated reason. Where data cannot leave the network — regulated records, material masters, payment histories — this is the only acceptable design.

How do you measure whether the AI works?

On a labelled sample of your own data, before deployment: precision and recall for matching and classification, accuracy for extraction, with the method written down. A system that is '90% sure' should be right nine times in ten on that sample, and we show you the sample.

How much does AI development cost in India?

It depends on the problem, the data and where it must run, and a figure quoted before anyone has seen your data is a sales number. We scope a measured first stage — one problem, one data set, one number — and quote against it, with the run cost after hand-over stated alongside.

What is the difference between your products and custom AI development?

MIDAS, ARGUS, ATLAS and MNEMOS are built systems for material master data, finance, procurement and knowledge on SAP and ERP data; they deploy faster because the hard parts exist. Custom development is for problems outside those four, built on the same intelligence layer and the same principles.

Can you work with a company that is not in Delhi, or not in India?

Yes. The system runs where your data is, on infrastructure you control, so where we sit matters little. Discovery and read-outs happen in person or remotely as you prefer.

Who owns the models, prompts and indexes?

You do — along with the code, the infrastructure configuration and the documentation. The intelligence stays when we leave.

Guides

Plain-language guides on the problems this work solves.

Tell us the decision you want improved.

The problem, the data it depends on, and where that data is allowed to live. A person reads it and replies honestly about whether we fit.

Hire the swarmhello@quantumbeetle.ai
Fig. 05Quantum Beetle

Contact

Hire the
swarm.

Tell us what you want the swarm to do. We'll tell you honestly whether it can — and what it would take.

hello@quantumbeetle.ai
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