Uttarakhand GenAI: cited investor access to government orders, policies, and gazettes.

Project facts & technologies

A citation-friendly summary of the Uttarakhand GenAI Investor Facilitation Platform — client, scope, technology, and headline outcomes.

Client

Government of Uttarakhand — investor facilitation

Industry segment

Government, Public Sector, Investment Promotion

Engagement type

GenAI chatbot platform — design, build, and deployment

Query resolution rate

94% — investor queries fully resolved by the chatbot

Processing time reduction

60% reduction in time-to-answer for investor queries

Document corpus

Government orders, policies, gazettes, investment schemes, approval procedures, department FAQs

Source document state

Mostly scanned PDFs and non-searchable images — handled via OCR

Document ingestion

OCR + layout parsing + chunking + metadata tagging + embedding generation

Retrieval foundation

Vector store with semantic embeddings and structured metadata filters

RAG orchestration

LangChain — query understanding, retrieval, generation, citation

Large language model

GPT-4 — grounded in retrieved government context

Citation approach

Every answer references the originating government document

Guardrails

Refusal on out-of-scope queries · hallucination prevention · source verification

Channels

Investor portal, web widget, mobile, department admin console

Cloud platform

AWS — document storage, OCR pipeline, vector store, LLM orchestration

Why is government investor information so hard to navigate?

Every state government in India runs an investment promotion mandate. Schemes are announced, sectoral policies are issued, gazette notifications go out, and procedural orders flow continuously from departments to the field. The information itself is public — government orders, policies, and gazettes are official publications — but accessing the right piece of information at the right moment, when an investor is trying to decide whether and how to set up business, is a different challenge entirely.

Most government documents predate digital workflows. They live as scanned PDFs and non-searchable images, organised by date and department rather than by what an investor would actually want to ask. Even when the document exists, finding it requires knowing which department issued it, what the order number was, what year it was issued in, and whether it has been superseded by a later notification. For an investor evaluating a sector for the first time, that knowledge does not exist — and acquiring it consumes time that translates directly into delayed investment decisions.

What problem does the investor chatbot solve?

Investors struggled to navigate complex government policies, orders, and gazettes, creating barriers to investment and slowing the business facilitation process. The platform needed to address four specific failure modes that no traditional portal could close on its own.

Key challenges

• Government documents were not searchable — most of the corpus existed as scanned PDFs and image-based files where keyword search could not see inside the content.
• Navigation depended on institutional knowledge — finding the right policy required knowing which department had issued it, which year, and whether it had been superseded.
• Investor queries piled up at the facilitation desk — routine questions about subsidies, approvals, and procedures consumed capacity that should have gone to case-specific facilitation.
• Answers required citation, not just retrieval — investors making material decisions needed the underlying government document to verify the information and attach as due-diligence evidence.

How does the investor facilitation chatbot work?

AiSPRY built the Uttarakhand GenAI Investor Facilitation Platform as a six-layer system: a government document corpus, an OCR pipeline that converts legacy scanned material into structured searchable text, a vector store with rich metadata, a LangChain-orchestrated RAG flow on GPT-4 with four cooperating components (query understanding, document retrieval, GPT-4 generation, and citation/verification), an investor-facing chatbot, and the channels through which investors reach it. Every answer must be grounded in a specific, citable government document — not the model's general knowledge.

See the investor chatbot in action

A walkthrough of the Uttarakhand GenAI chatbot — an investor asks about sectoral subsidies and approval procedures, the platform retrieves and grounds its answer in the relevant government order, and every response carries the originating document citation.

Uttarakhand GenAI — cited, grounded investor facilitation in action

Click to play · OCR-fed RAG with GPT-4 and source-document citations

• Natural-language investor queries — subsidies, sectoral policies, approval procedures, gazette references
• OCR-driven document access — chatbot retrieves from scanned PDFs the platform has structured
• Active-document priority — metadata filter avoids superseded policies
• Cited answers — every response references the originating government document for verification

What is the architecture of the Uttarakhand GenAI platform?

The architecture is organised as six layers: the government document corpus, the OCR and document ingestion pipeline, the vector store with structured metadata, the LangChain RAG orchestration with GPT-4 (further decomposed into query understanding, retrieval, generation, and citation/verification), the investor-facing chatbot, and the channels and governance layer. Raw scanned documents never reach the chatbot without OCR and structuring; the vector store is the only place where retrieval is grounded; GPT-4 generates only against retrieved context; and every answer carries a traceable citation back to its source document.

How is the platform engineered for government information accuracy?

Government information is exactly the wrong domain for a model to answer from its training data. The platform's design choices ensure every answer is anchored to a specific, current, cited government document.

What measurable results did the platform deliver?

The platform was evaluated against the operational pain points it was built to address — query resolution, time-to-answer, citation quality, and the broader effect on investor facilitation capacity.

Uttarakhand GenAI — frequently asked questions