Structure-first AI
that reasons over your knowledge

Knows your business like your best engineerKnows your numbers like your CFOKnows your protocols like your clinicianKnows your compliance like your regulatorKnows your inventory like your operations leadKnows your customers like your account manager

Vedana turns your documents into structured data
and lets AI explore it with tools

Book a call See the demo →
Open-core engine Managed cloud Enterprise support

Your company knowledge
lives in many places at once

PDFs · Specs · Excel sheets · Shared drives · Emails · Wikis · ERP exports

AI can read text. But your knowledge isn’t text – it’s a fragmented system.

PDF
DOCX
XLSX

Vedana builds a data model of your business knowledge

It turns your documents into structured knowledge:
Anchors (entities) · Attributes (facts) · Links (relations)

AI gets a model of how your world works.

product_spec.docx certificate_scan.pdf supplier_contract.docx Extracted entities Product Certificate Supplier DATA MODEL Product Supplier Certificate supplied by certified by

AI explores your structured knowledge with tools

It queries the model, compares options, and applies constraints step by step.
So answers follow your rules and you can trace how they were reached.

User query Which suppliers provide certified batteries for the EU market? AI reasoning 1. Query Product entities 2. Filter EU certificates 3. Match suppliers Structured answer 3 compliant EU suppliers: ACME Power Ltd EuroBattery GmbH GreenVolt

Why similarity-based RAG fails for business questions

Business Scenario
Classic RAG Similarity-based
Semantic RAG (Vedana) Rule-based
Complete list with filters
Which products can be sold in the EU?
Classic RAG
✕
Returns a few similar products. May miss others that also qualify.
Semantic RAG (Vedana)
✓
Returns the full validated list.
Checks all required conditions before answering.
Exact product codes / SKUs
Show details for SKU B49-6
Classic RAG
✕
May surface similar-looking codes (e.g., B49-8) because they appear in related documents.
Semantic RAG (Vedana)
✓
Exact identifier match.
Only the requested SKU is evaluated and returned.
Business rules & constraints
Can this battery be shipped by air?
Classic RAG
✕
Finds documents mentioning air shipment, but does not verify weight or hazard class.
Semantic RAG (Vedana)
✓
Clear Yes/No answer.
Shipment rules are verified before answering.
Compatibility logic
Which chargers work with Model X?
Classic RAG
✕
Returns documents where both are mentioned. May include incompatible options.
Semantic RAG (Vedana)
✓
Only compatible options are returned.
Technical compatibility is checked first.
Regulatory compliance
Is this device compliant in Germany?
Classic RAG
✕
Finds documents mentioning compliance, but does not confirm certificate validity.
Semantic RAG (Vedana)
✓
Compliance is verified.
Certification status is checked before answering.

Which response do you prefer?

Pick a query and compare outputs.

See industry-specific examples

Automotive

Compliance, homologation, and structured product validation for regulated automotive environments.
Explore Automotive

Development

Validate investigation scopes, check regulatory requirements, and estimate engineering work for development projects using structured project and regulatory data.
Explore Development

HoReCa

Deterministic reasoning over regulations, contracts, and structured legal knowledge.
Explore HoReCa

Legal

Deterministic reasoning over regulations, contracts, and structured legal knowledge.
Explore Legal

MODELING FOUNDATION

Vedana is built on Minimal Modeling

Minimal Modeling is a data modeling discipline created by Alexey Makhotkin, author of the Database Design Book.

Instead of treating knowledge as raw text, Minimal Modeling represents it as a structured system of anchors, attributes, and links.

  • Anchors represent entities — products, suppliers, regulations, ingredients.
  • Attributes describe properties of those entities.
  • Links define relationships between them.

This structure turns documents into a navigable knowledge graph that an AI assistant can reason over.

If the assistant struggles with certain questions, you don’t rewrite prompts. You simply lift that concept into the data model — as a new anchor, attribute, or relationship.

Vedana doesn’t rely on prompts alone. It reasons on top of an explicit data structure.

Implementation timeline

  1. 1

    Week 1: Discovery & Data Mapping

    We meet with your experts to identify recurring questions and key data sources – your products, services, pricing, operating procedures and other important knowledge sources.

    Outcome: Your domain map + list of structured and unstructured sources.

  2. 2

    Week 2: Domain Modeling

    We build your knowledge graph (entities, attributes, links) and define how VEDANA should reason via Playbook logic.

    Outcome: Custom data model + reasoning framework for your use cases.

  3. 3

    Week 3: Data Loading

    We ingest and normalize your data using the data model.

    Outcome: Live knowledge graph populated with your real operational data.

  4. 4

    Week 4: Testing & Launch

    We test the system on your golden dataset, fine-tune the Playbook reasoning rules, and launch an internal demo of your working assistant.

    Outcome: AI Assistant that answers your team’s real questions with structured, constraint-aware reasoning.

Frequently Asked Questions

Is Vedana just another RAG chatbot? +

No. Vedana is graph-first reasoning infrastructure. It does not rely on similarity search alone. It builds and queries structured knowledge graphs to answer questions under explicit constraints.

How is this different from vector search? +

Vector search retrieves similar text fragments. Vedana evaluates entities, attributes, and relationships before answering. This reduces hallucinations and ensures answers follow business rules.

Do I need a knowledge graph beforehand? +

No. Vedana extracts entities and relations from your documents, normalizes them, and builds a structured graph layer on top of your existing data sources.

What kind of companies is Vedana built for? +

Enterprises where answers must follow constraints: healthcare compliance, product eligibility, logistics rules, regulatory environments, complex catalogs, and operational playbooks.

How long does a pilot take? +

A structured pilot can be delivered in 4 weeks. It includes data extraction, graph modeling, reasoning setup, and measurable validation.

Is this SaaS or on-premise? +

Both. Vedana can run in a managed cloud environment or be deployed inside your infrastructure, depending on security and compliance requirements."

Try Vedana your way

Start with the open-core docs, or work with us to design your data model and deployment.