Enterprise AI InfrastructureSpecialized ModelsPrivate Data IntelligenceMade in Maharashtra

Build AI that learns from your organization, not just the internet.

Tegacy transforms messy proprietary enterprise data into structured intelligence for specialized AI models, retrieval systems, evaluation datasets, and continuous feedback loops.

// tegacy.pipeline — live data intelligence flow

PDFsDatabasesLogsSpreadsheetsExpert DecisionsWorkflowsChats→ Structured Intelligence
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The Problem

Enterprises generate knowledge.
AI systems cannot access it.

The gap between your organization's institutional intelligence and what your AI can actually use is the defining productivity challenge of the next decade.

Proprietary Data Is a Mess

Enterprises sit on vast troves of high-value data locked in PDFs, databases, logs, spreadsheets, and expert decisions — completely inaccessible to AI systems.

Generic Models Miss Context

Off-the-shelf LLMs trained on public internet data have zero knowledge of your processes, terminology, domain nuances, and institutional intelligence.

Fine-tuning Requires Clean Data

Building specialized AI models demands structured, curated, evaluation-ready datasets. Most enterprises cannot produce this at scale without specialized infrastructure.

AI Without Feedback Degrades

AI systems deployed without continuous evaluation and feedback loops become stale. Your enterprise AI needs to improve with every interaction and decision.

80%+

of enterprise AI projects fail due to poor data quality, inaccessible domain knowledge, and the absence of structured fine-tuning pipelines. Tegacy solves all three.

Platform Pillars

Prepare. Specialize. Improve.

Three integrated pillars that transform raw enterprise data into a perpetual intelligence engine.

Prepare

From chaos to structure.

Ingest any enterprise data format — PDFs, logs, spreadsheets, databases, chat exports, expert documents. Tegacy cleans, normalizes, and structures it into AI-ready knowledge.

Multi-format ingestion — PDF, DOCX, CSV, JSON, SQL

Metadata extraction and entity recognition

Semantic chunking and knowledge graph construction

Interactive Product Preview
Prototype Workflow Simulation — Not a live product

See how Tegacy turns raw enterprise data into specialized AI intelligence.

tegacy.dashboard — pipeline.simulate
Ready

Prototype Workflow Simulation

Simulate the full Tegacy data intelligence pipeline

Step 1Ingesting messy enterprise data...
Step 2Extracting metadata and cleaning documents...
Step 3Structuring data into AI-ready knowledge chunks...
Step 4Preparing retrieval and fine-tuning datasets...
Step 5Creating evaluation and feedback loop...
How It Works

Five stages. One intelligent pipeline.

From raw enterprise data to continuously improving specialized AI — a closed loop infrastructure designed for real enterprise complexity.

01

Ingest

Connect to your data sources. Tegacy ingests PDFs, Word documents, databases, logs, spreadsheets, API outputs, email threads, and chat exports — any format, any location.

  • Native connectors for 20+ data formats
  • On-premise and cloud data sources
  • Incremental and real-time ingestion
02

Structure

Raw data is cleaned, parsed, and transformed into structured knowledge. Entities are extracted, metadata is enriched, and semantic chunking creates optimal retrieval units.

  • Entity and relationship extraction
  • Semantic chunking with overlap control
  • Quality scoring and deduplication
03

Retrieve / Train

Deploy your structured knowledge as a RAG retrieval corpus or export fine-tuning datasets for model training. Tegacy generates the exact data format your AI stack requires.

  • Vector embedding with multiple model support
  • Fine-tuning dataset export (JSONL, Parquet)
  • Prompt-completion pair generation
04

Evaluate

Automatically generate evaluation datasets from your domain knowledge. Benchmark your AI models against your ground truth — not generic public benchmarks.

  • Auto-generated QA evaluation sets
  • Domain-specific benchmark construction
  • LLM-as-judge evaluation pipelines
05

Improve

Capture AI inference feedback, detect model drift, and automatically generate new training signals. Your enterprise AI compounds in quality over time.

  • Inference feedback capture and labeling
  • Model performance monitoring
  • Automated retraining trigger generation
Why Now

The window for AI infrastructure leadership is open.

Four converging forces make this the right time to build enterprise AI data infrastructure — and Maharashtra is the right place to build it.

Enterprise AI Spending Surges

Global enterprise AI infrastructure investment is projected to exceed $200B by 2027. The bottleneck is not compute — it is data readiness and model specialization.

Private Data Is the Last Moat

As foundation models commoditize, proprietary domain data becomes the only sustainable competitive advantage for enterprises building specialized AI capabilities.

India's AI Infrastructure Moment

India's digital economy generates vast enterprise data across BFSI, healthcare, manufacturing, and government — yet lacks the infrastructure layer to make it AI-ready.

Fine-tuning Is Now Viable at Scale

Advances in PEFT, LoRA, and quantization have made domain-specific model fine-tuning accessible at a fraction of prior cost — but structured training data is still the missing piece.

Use Cases

Built for every sector with complex knowledge.

Any industry with proprietary data, domain expertise, and operational complexity is a fit for Tegacy infrastructure.

Financial Services
Regulatory intelligence at scale.

Transform compliance manuals, audit reports, risk frameworks, and transaction logs into retrieval-ready knowledge. Build specialized AI for regulatory Q&A, fraud pattern detection, and automated reporting.

Key Capabilities

  • Regulatory document Q&A systems
  • Audit trail intelligence
  • Risk model fine-tuning datasets
  • Compliance evaluation benchmarks
About Tegacy

Why Tegacy.

A focused team with deep conviction about the enterprise AI data problem — and the technical depth to build the infrastructure layer that solves it.

T
Tegacy
Enterprise AI Data Infrastructure

Tegacy is an early-stage enterprise AI infrastructure startup founded in Mumbai, Maharashtra. We are building the data intelligence layer that enterprises need to train, deploy, and continuously improve specialized AI models on their proprietary data.

Our thesis: the bottleneck to enterprise AI is not foundation model capability — it is the infrastructure for transforming proprietary enterprise data into AI-ready structured intelligence. We are building that infrastructure.

Mumbai, Maharashtra, India
|
Enterprise AI Infrastructure
|
Early Stage
Stage
Early-Stage Startup
Sector
Enterprise AI / Deeptech
Location
Mumbai, Maharashtra
Focus
Data Infrastructure for AI

Technical First-Principles

Tegacy is built by engineers who understand the full AI stack — from raw data formats to model training pipelines. Every design decision is grounded in real enterprise AI implementation challenges.

Domain-Informed Architecture

Our platform architecture is informed by direct observation of how enterprises in BFSI, healthcare, and manufacturing actually fail to deploy AI — not theoretical use cases.

Infrastructure Mindset

We are building infrastructure, not an application. Tegacy is designed to be the data intelligence layer that other AI systems and products are built on top of.

Deep Local Roots

Based in Mumbai, Maharashtra — with direct access to enterprise decision-makers, government institutions, and the growing Indian startup ecosystem.

Get in Touch

Ready to build AI that knows your domain?

Request a private demo, explore a partnership, or reach out about enterprise pilots, investment, or strategic collaboration.

Location
Mumbai, Maharashtra, India

We welcome conversations about:

  • Enterprise pilot programs
  • Strategic partnerships
  • Investment and funding
  • Research collaborations