AI systems fail not because models are weak — but because the context they reason over is unstructured, incomplete, and epistemically unmarked. Context Layer is infrastructure that assembles minimum-sufficient, optimally structured context packages so AI reasoning is trustworthy where it matters most.
Request Early Access →Every high-stakes AI deployment — legal, consulting, clinical, M&A — shares the same failure mode: the model receives a poorly assembled context window and hallucinates, misses key evidence, or reasons from stale information. Prompt engineering treats symptoms. Context Layer treats the root cause.
RAG pipelines return semantically similar text, not epistemically relevant context. The model gets noise, not signal.
Retrieved spans carry no provenance, confidence, or temporal metadata. The model reasons as if all context is equally trustworthy.
Naive retrieval fills the context window with redundant information. Reasoning quality degrades with irrelevant token pressure.
Context Layer processes raw source material — meeting transcripts, documents, communications — through a structured pipeline that extracts entities, builds a typed semantic graph, and assembles evidence packs: ranked, deduplicated, epistemically marked context packages ready for AI reasoning.
Typed edges (supports, refers_to_entity, belongs_to_topic) between extracted nodes. Graph traversal surfaces non-obvious evidence relationships BM25 alone misses.
BM25 full-text search combined with graph expansion. Edge-type priors control expansion weight. Abstention logic prevents weak evidence from polluting the context pack.
Native support for mixed-language content including Hinglish and Devanagari. Domain glossary injection at ingest time preserves acronyms and technical signal across translation.
Output is not a list of documents. It is a ranked, deduplicated, source-attributed evidence pack with topic trees and confidence scores — ready for AI reasoning.
Our beachhead is management consulting — a domain defined by large transcript corpora, high-stakes decisions, and tolerance for premium tooling. Our design partner is a senior business consultant processing real meeting transcripts. The system handles multi-speaker, multilingual content and answers natural language queries across thousands of turns.
We are building the infrastructure layer that sits between raw enterprise data and AI reasoning — assembling the minimum-sufficient, optimally structured, epistemically marked context that makes AI trustworthy in consequential domains. Starting with consulting. Expanding to legal, investment banking, and clinical decision support.
Founded in India. Building for the world.