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Enterprise AI, LLM & RAG solutions

Enterprise RAG.Your knowledge,put to work.

Connect AI and LLMs to your approved knowledge with RAG. Help teams find answers with source references, with data security and integrity built into the scope.

Request a discovery call Try the RAG example

30-minute conversation · One team, one knowledge use case

Your sourcesRetrieveAnswerVerify

Clients & past engagements

Organizations we’ve worked with.

View all clients & past work
  • Sentient
  • Trelleborg
  • Jio
  • Flipkart
  • Viacom18
  • BHEL
  • IIT Madras

Use case 01 / Enterprise RAG

Your knowledge.
Answers with RAG.

Build AI assistants with retrieval-augmented generation (RAG). Connect an LLM to approved SOPs, manuals and internal documents so teams can find answers with references to the source.

Explore enterprise RAG Discuss a RAG use case

Find the right information

Scope knowledge search for operations, support or internal teams across your agreed document sources.

Keep the evidence in view

Design answers with source references and a path to staff review when the available evidence is insufficient.

Build in data security & integrity

Define access permissions, document updates and retrieval evaluation before rollout. Test answers against representative questions.

Use case 02 / Document processing

AI document processing.
From intake to review.

Help warehouse and 3PL teams check delivery documents, review discrepancies and prepare receiving records. Start with a paid assessment to decide whether a focused pilot makes sense.

Start with delivery documents

Evaluate your delivery notes, challans or packing lists and the fields your receiving team needs.

Make discrepancies visible

Define quantity and completeness checks, with source references and a clear route to staff review.

Prepare the receiving record

Scope an approved export or update to one agreed WMS, ERP or existing tool.

See the assessment and pilot scope
  • Warehouse & 3PL operators
  • Manufacturing & distribution
  • WMS / ERP implementation partners

Validation component demo

A small discrepancy.
A clear next step.

Try four synthetic examples. The same validation rules calculate the result in your browser.

Fictional delivery note

DEMO–014

Example Supplier → Example Warehouse

Original structured sample values, preserved while review values are edited
ItemSource quantity
Product A60 cartons
Product B40 cartons
Source printed total98 cartons

Structured synthetic values · no document uploaded

02 / CHECK & REVIEW

Review required

Reviewed line-item total
100 cartons
Reviewed printed total
98 cartons
Line total minus printed total
2 cartons
  • Line quantities total 100 cartons, but the printed total is 98. A reviewer must resolve the 2-carton difference.

Staff approval stays in the workflow.
Try reviewer corrections and a local sample export in the full demo.

Working validation component · synthetic data only. This demo runs deterministic checks in your browser. It does not perform AI extraction, verify document authenticity, authenticate a reviewer, or connect to a warehouse system. No sample values are uploaded. Try the editable demo · See the reproducible validation evidence.

For your next decision

Evidence you can inspect.
Assumptions you can change.

01

What do the checks catch?

Review the synthetic cases, measured results and limits of the validation component.

Inspect the evidence02

How would this connect?

See the path from document intake to review and an agreed export or integration.

Understand system fit03

What could it be worth?

Explore handling effort and costs using your own assumptions. Keep the worksheet for your team.

Use the value worksheet

Use case 03 / Voice-to-Form Automation

Speak naturally.
Build a complete form.

Turn a conversation into a structured report with voice AI and LLMs. Ask for missing details, handle corrections and let staff review the form before an approved submission.

For field-service reports, inspections and operational requests. Start with one team, one form and one agreed language.

Explore voice-to-form Try the guided example
  1. 01 / CAPTURE

    Describe the work

    Speak in your own words. Map the conversation to your agreed form fields.

  2. 02 / CLARIFY

    Fill the gaps together

    Follow-up questions and corrections help complete the draft. Keep typing available too.

  3. 03 / REVIEW

    Check before submission

    Validate required fields and confirm the final record. Scope data security, integrity and system access before rollout.

Explore our broader engineering services

AI & LLM applications, RAG knowledge assistants, voice AI, AR/VR simulations, web and mobile software, and games.

Explore more services

AI & LLM assistance

Use artificial intelligence and large language models to extract information and assist with agreed tasks. Evaluate outputs against representative samples and keep staff approval in the workflow.

Data security

Agree data access, hosting, retention, and model-provider handling before implementation. Define who can view documents and approve actions within the scoped workflow.

Data integrity

Keep source references, check missing or inconsistent fields, and review exceptions before updates. Define reconciliation steps for failed or incomplete transfers.

Start with one workflow.

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