Hallucination from messy RAG
Throw raw documents into a vector database and your agent will confidently invent answers. Without structured context, retrieval is a coin flip.
Structured Data for AI · On-Premise · Zero Cloud
Your Unstructured Data →
Agent-Ready. On Your Premises.
We turn enterprise documents, PDFs, emails, and legacy databases into structured data your AI agents and LLMs can actually use. Ontologies, schemas, action contracts — built on your infrastructure. Your data never leaves your building.
- 90%
- of enterprise data is unstructured
- 100%
- on-premise processing
- 4–8 wks
- to production
- Zero
- data exfiltration
02 — Why AI Projects Stall
Your AI Agents Are Only as Good as Your Data Layer.
Most enterprises have spent years collecting data. Almost none of it is ready for AI. Documents are scanned, schemas are inconsistent, knowledge lives in PDFs, and critical context sits in someone's email thread. When you point an LLM at this, three things happen — all of them bad.
Agents that can’t act
Conversational AI without action schemas is a chatbot. Real agents need typed tool contracts, entity relationships, and business constraints — none of which exist in your raw data.
No audit trail, no deployment
Regulated industries (healthcare, BFSI, government) can’t deploy black-box AI. Without lineage, schema enforcement, and audit logs, your AI never leaves the POC.
03 — The Three-Layer Stack
Built for Agents. Owned by You.
We don't sell data cleanup. We architect the foundation your AI agents run on — three connected layers, deployed entirely on your infrastructure.
Ingestion & Extraction
Documents, scanned PDFs, emails, contracts, legacy databases, ERP exports → structured records. Powered by ATC Manthan, our on-premise document AI engine. OCR, table extraction, entity recognition, multi-modal parsing.
Outcome: Clean, queryable, machine-readable data.
Ontology & Action Schema
This is where most vendors stop and most agents fail. We design the entity model, define relationships, build MCP-compatible tool contracts, and encode business constraints. Healthcare codes, financial taxonomies, legal clauses, manufacturing BOMs — domain-specific, vertical-aware.
Outcome: Agents that act, not just answer.
Continuous Sync & Governance
Production data moves. Schemas drift. Compliance demands lineage. We build change data capture, drift detection, audit logging, and validation pipelines — so your structured data stays structured, and every agent action is traceable.
Outcome: Production-grade, audit-ready, compliant.
04 — Industries We Structure Data For
Vertical-Aware Data Engineering.
Generic data structuring fails in regulated industries because the ontologies are domain-specific. We've built for these verticals.
Healthcare
FHIR R4, ICD-10, SNOMED CT, LOINC. Patient records, clinical notes, discharge summaries, lab reports. ABDM-aligned.
Explore →Financial Services
Transaction data, KYC documents, loan files, compliance reports. RBI-aligned schemas.
Explore →Legal
Contracts, case law, regulatory filings, clause-level extraction. Citation-ready.
Manufacturing
BOMs, SOPs, equipment manuals, sensor logs, quality reports. ISA-95 compatible.
Explore →Government
Citizen records, regulatory filings, policy documents, scheme data. DPDP Act compliant.
Explore →Education
Curriculum content, assessment data, learner records, NEP 2020 frameworks.
Explore →05 — Why On-Premise Matters Here
Your Data Doesn't Leave. Period.
Most data structuring vendors are cloud-first. They send your sensitive data to third-party labeling services, cloud OCR APIs, and external AI models. For regulated industries, that's not an option — it's a liability.
Air-gapped processing
Every step — extraction, structuring, validation — runs on your infrastructure. No third-party data labelers. No cloud APIs.
DPDP Act compliance by design
Data residency, purpose limitation, audit trails. Built in, not bolted on. HIPAA-ready, RBI-compliant where applicable.
You own the pipeline
Source code, schemas, ontologies, fine-tuned models, infra-as-code. Yours. Forever. No vendor lock-in.
06 — How We Engage
From Audit to Production in 4–8 Weeks.
- Week 1–2
Data Readiness Audit
We map your data sources, assess structure, identify gaps, and design the target ontology. You get a written readiness report with scope and timeline.
- Week 3–6
Pipeline + Schema Build
We deploy ATC Manthan, build extraction pipelines, design schemas, and connect to your existing systems. Daily standups, weekly demos.
- Week 7–8
Validation + Handoff
Quality validation, accuracy benchmarks, audit logging setup. Full source code, schemas, and documentation transferred to your team.
- Ongoing
Continuous Sync & Governance
Optional managed service. Schema drift detection, audit reviews, ontology updates as your business changes.
07 — Pricing
Fixed Cost. Unlimited Usage. No Per-Document Fees.
Pricing in USD; INR equivalents on quote.
Foundation
For teams getting started with AI agents.
- ✓Single domain
- ✓Up to 100K documents
- ✓1 ontology
- ✓Standard schemas
- ✓4-week delivery
From $18,000
Get Quote →Most Popular
Department
For organisations deploying AI across multiple workflows.
- ✓3 domains
- ✓Up to 1M documents
- ✓Multi-schema architecture
- ✓Custom ontology
- ✓MCP tool contracts
- ✓Continuous sync (3 mo)
- ✓6-week delivery
From $45,000
Get Quote →Enterprise
For org-wide AI infrastructure.
- ✓Unlimited domains
- ✓Unlimited scale
- ✓Custom ontology engineering
- ✓Action schema layer
- ✓Continuous sync + governance
- ✓Dedicated team
- ✓8-week delivery
Custom Pricing
Get Quote →08 — Frequently Asked Questions
Common questions about structured data for AI
What is "structured data for AI"?
Structured data for AI is enterprise data that has been extracted, normalised, schema-validated, and enriched with relationships and constraints so AI agents and LLMs can query, reason over, and act on it reliably. It is the difference between dumping PDFs into a vector database (which produces hallucinations) and giving an agent typed entities, ontologies, and tool contracts (which produces dependable behaviour).
What does "agent-ready" actually mean?
Agent-ready data has three properties classic data warehouses lack: (1) explicit ontology — entities and relationships are typed; (2) action schemas — every operation an agent can take is described as a typed tool contract, often via MCP; (3) provenance and constraints — every value carries lineage and business-rule validation so agent actions are auditable and safe.
How is this different from data cleanup or ETL?
ETL moves data between systems; data cleanup fixes nulls and dedupes rows. Neither produces an ontology, action schemas, or MCP tool contracts. Our work begins where ETL ends — we treat the LLM/agent as a first-class consumer of the data layer, not an afterthought.
Why on-premise instead of cloud labeling services?
Most cloud labelling and OCR services route your sensitive data to third-party servers. For Indian healthcare, BFSI, and government workloads, that breaches DPDP Act, RBI guidelines, or sectoral mandates. We run every step — OCR, extraction, structuring, validation — on your hardware. No third-party data labelers, no cloud APIs.
Do you support FHIR, RBI, ISA-95, NEP 2020, and other domain ontologies?
Yes. Healthcare deployments use FHIR R4, ICD-10, SNOMED CT, LOINC, and ABDM mappings. BFSI deployments align to RBI schemas. Manufacturing uses ISA-95. Education uses NEP 2020 frameworks. We bring vertical-aware ontologies; we don’t make you build them.
What is an MCP-compatible tool contract?
MCP (Model Context Protocol) is an open standard for exposing tools and data to LLM clients. An MCP-compatible tool contract is a typed, documented description of an action your agents can perform — generated from the same ontology that defines your structured data, so agents can plug into Claude, Cursor, on-prem agents, or any MCP client without rewriting integrations.
How long until production?
4 weeks for the Foundation tier (single domain, up to 100K documents). 6 weeks for Department (3 domains, up to 1M documents, MCP tool contracts). 8 weeks for Enterprise (unlimited scale, dedicated team, governance layer).
How is pricing structured?
Fixed-price by tier, not per-document or per-token. You pay once for the engagement; ongoing usage is unlimited on your hardware. Optional managed continuous sync and governance is billed monthly. Foundation starts at $18,000; Department at $45,000; Enterprise is custom.
Who owns the pipeline after delivery?
You do. Source code, schemas, ontologies, fine-tuned models, and infra-as-code transfer to your team. Open-source dependencies retain their original licenses. No vendor lock-in.
Free Audit · No Sales Pitch
Book a Data Readiness Audit.
30 minutes with our data architects. We'll assess your current data layer, identify what's standing between you and production AI agents, and give you a concrete path forward — with timelines and costs.
Intelligence, Built In.