LANGUAGE INTELLIGENCE

Your institutional knowledge,
finally accessible to everyone.

Large language models trained on your documents, policies, and data — deployed entirely on your infrastructure. No cloud. No data leakage. No hallucinations you can't explain.

Start your POC → See case studies
reimbursement
circular 2019/14
OPD claims
retrieve
vSense · llm
0.94
0.88
0.81

The Problem

Your best knowledge
is trapped.

Every organisation has years of institutional knowledge locked inside PDFs, policy documents, past reports, and the heads of senior staff. Junior employees waste hours searching. Decisions get made without the right context. And when senior staff leave, the knowledge walks out with them.

Generic AI tools don't know your organisation. They hallucinate. They send your data to third-party servers. They can't be audited.

Knowledge silos
Critical information spread across SharePoint, email, legacy databases, and paper files — inaccessible at the moment of need.
Generic LLMs fail at specificity
Off-the-shelf AI doesn't know your procurement rules, your policy from 2019, or your compliance definitions.
Data sovereignty non-negotiable
For regulated enterprises, sending documents to any external API is simply not permitted.

What We Build

Six capabilities.
One coherent system.

CAPABILITY 01
Private AI Assistant
Conversational interface trained on your internal documents. Cites sources. Never guesses.
CAPABILITY 02
Retrieval-Augmented Generation
Your documents become a searchable knowledge base — grounding every answer in actual content.
CAPABILITY 03
Multi-Document Q&A
Upload a tender, policy, audit. Ask cross-document questions. Get referenced answers in seconds.
CAPABILITY 04
Semantic Search
Beyond keyword matching — understand intent, context, meaning across thousands of documents.
CAPABILITY 05
Domain Fine-Tuning
We fine-tune Llama, Mistral on your domain data. The model learns your terminology and formats.
CAPABILITY 06
Multilingual Support
English, Hindi, Arabic, French, Spanish via language-specific models and Bhashini integration.

Under the Hood

Technically sound.
Practically simple.

STEP 01
Ingest
Your documents — PDFs, Word files, scanned records, database exports — are ingested, OCR-processed, chunked, and hashed for integrity.
STEP 02
Embed
Text chunks are encoded into vector embeddings using a locally-deployed embedding model and stored in a private vector database on your server.
STEP 03
Retrieve & Reason
When a user asks, the system retrieves the most semantically relevant chunks, passes them as context to the LLM, and generates a grounded, cited response.
STEP 04
Audit
Every query, retrieved chunk, and generated response is logged with full traceability — back to source document and page.

Use Cases

Deployed across sectors.
Delivering daily.

Government & PSU
Policy & Circular Intelligence
Staff query complex procurement rules. System retrieves the exact clause, cites document and page.
Hours → SecondsOutcome
Legal & Compliance
Case Precedent Research
Lawyer uploads case facts. AI searches 150M indexed judgements for semantically relevant precedents.
4× faster researchOutcome
Healthcare
Claims Policy Assistant
Claims officer queries reimbursement policy docs. System answers instantly with clause, limits, exceptions.
60% fewer escalationsOutcome
5×
Faster knowledge retrieval vs manual
4×
Reduction in research and lookup time
100%
Data remains on your infrastructure

Ready to unlock your institutional knowledge?

POC in 7 days — trained on your actual documents, deployed on your infrastructure.

Book a Language AI POC →