Multi-Modal Document Parsing
Automated semantic chunking for PDF technical briefs, PostgreSQL schema catalogs, JSON REST logs, and enterprise codebases.
Air-gapped, high-throughput Retrieval-Augmented Generation (RAG) engines. Transform internal document lakes, CRM data, and enterprise codebases into real-time semantic intelligence without training third-party public models.
Strict DPDP Act 2023 and enterprise security compliance built into every vector query.
Automated semantic chunking for PDF technical briefs, PostgreSQL schema catalogs, JSON REST logs, and enterprise codebases.
Combines dense vector embeddings (pgvector / Qdrant) with sparse BM25 keyword matching for 99.4% context retrieval precision.
Self-hosted model endpoints using vLLM and Ollama. Client data never leaves your VPC or crosses foreign jurisdiction boundaries.
Our architects build production prototypes connected to your staging data within 10 business days.
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