The Problem
Your Agents Are Reasoning Over Context-Stripped Data.
Traditional ETL pipelines extract, transform, and load data — but strip the business context in the process. Your agents hallucinate not because the model is bad, but because the data they receive has lost the context that makes it meaningful.
Context Loss
ETL systems flatten relationships, discard metadata, and strip semantic meaning. Agents get clean records but no understanding of what they mean.
Entity Resolution
"R. Sharma", "Rajesh Sharma", "Sharma, R." — the same person across three systems. Traditional joins fail. Agents get confused by duplicates and mismatches.
Infrastructure Sprawl
Vector databases for embeddings. Graph databases for relationships. Time-series for events. Three systems, three APIs, three maintenance burdens.
Indian Language Data
Customer records in Hindi, Tamil, Telugu. Address formats that don't match Western schemas. Transliteration variations that break exact-match systems.
Capabilities
ETL-C: Extract, Transform, Load — with Context
Context Engine extends traditional ETL with a contextual intelligence layer. Your agents don't just get data — they get data with the relationships, semantics, and business meaning preserved.
Contextual Join Engine
Semantic matching across datasets using embeddings and metadata. Resolves entity variations with confidence scores and audit trails — including Indian name and address formats.
Adaptive Pipeline Orchestration
Dynamically adjusts data processing based on context signals. Tailored enrichment per data type and business event. Pipelines that understand what the data means, not just what it looks like.
Managed Context Store
Unified semantic context storage combining vector DBs (embeddings), graph DBs (relationships), and time-series (events) — accessed through a single API.
AI-Ready Data APIs
Semantic query interface optimised for RAG applications. Natural language queries with contextual result streaming. Your agents get answers, not just rows.
Indic Language Understanding
Native handling of Indian language data — Hindi, Tamil, Telugu, Bengali, and 18 more. Transliteration-aware matching. Multi-script entity resolution.
Provenance & Lineage
Every contextual enrichment is tracked. Know exactly where your agent's context came from, how it was joined, and what confidence level it carries.
How It Works
From Raw Data to Rich Context
Ingest
Connect your data sources — databases, APIs, documents, and streaming feeds
Contextualise
Semantic enrichment, entity resolution, and relationship mapping across sources
Store
Unified context store — vectors, graphs, and time-series in one managed system
Serve
AI-ready APIs deliver contextually-rich data to your agents in real time
Traditional ETL
- Strips business context during transformation
- Exact-match joins fail on messy data
- Three separate database systems to manage
- English-only entity resolution
- No provenance for enriched data
Context Engine
- Preserves and enriches context through the pipeline
- Semantic matching with confidence scores
- One unified context store, one API
- 22 Indian languages with transliteration
- Full lineage and audit trail for every enrichment
Integration
Feeds Your Entire Agent Stack
- Data Sources: PostgreSQL, MongoDB, S3, Kafka, REST APIs
- Agent Frameworks: LangChain, LlamaIndex, custom RAG pipelines
- Vector Stores: Built-in, or sync to Pinecone, Weaviate, Qdrant
- Graph: Built-in, or sync to Neo4j, Neptune
India Deployment
Data Localisation: All context processing within Indian data boundaries.
Language: Native Indic language handling — no transliteration preprocessing required.
Compliance: DPDP Act ready. Full data lineage for regulatory audits.
Agents don't hallucinate because models are bad.
They hallucinate because the context is missing.
