Context Lake unifies the capabilities of a data lake, a knowledge graph, and a novel context graph into a single governed platform. Ingest content once — structured or unstructured — and make it instantly usable across analytics, semantic search, graph reasoning, retrieval-augmented generation, and autonomous agents, with enterprise access control and compliance applied end-to-end.
One
master record per item
Many
representations, one identity
9
pluggable provider categories
6
federated query types
Every modern enterprise has invested in data platforms. Few have a platform designed for the way AI actually consumes information — across multiple representations of the same content, with governed access, with provenance, and with the contextual signal agents need to reason reliably. Context Lake is that platform.
Warehouses, lakes, vector databases, search indices, and knowledge graphs each live in their own silo. Moving a single document between them requires a dedicated pipeline and a dedicated team.
Retrieval systems are stitched together from bespoke chunkers, embedding scripts, and one-off graph extractors. Every new use case forks the pipeline and doubles the operational surface area.
PII, PCI, and regulated data leak into embeddings, graphs, and caches. Access controls stop at the database boundary, leaving AI surfaces to re-implement authorization from scratch.
Traditional platforms store what a document says. They rarely capture where it came from, who touched it, how it was transformed, or why an agent retrieved it — the context that makes AI trustworthy.
A single ingestion path produces an immutable master record and every downstream representation an analytics, search, or AI workload could want — governed by the same policies.
Structured tables, unstructured content, semantic embeddings, entity relationships, and a temporal context graph live side-by-side behind a single identity for every item.
Storage, embeddings, LLMs, agent frameworks, auth, compliance scanners, and data connectors are all swappable providers. Bring the tools you already own; swap them when you outgrow them.
RBAC and attribute-based access control translate into native enforcement at every store. PII, PCI, HIPAA, and GDPR controls run in the ingestion path, not in application code.
The outcome
One governed platform. One identity per item. Every representation, every query pattern, every AI surface — unified, compliant, and ready to use on day one.
Context Lake is designed as a set of composable architectural concepts rather than a bundle of tools. Each concept solves a specific class of problem that traditional data platforms leave to the application layer. Together they form the foundation for trustworthy, governed, AI-ready data.
The flow of content through the platform
Step 1
Ingest
Step 2
Validate
Step 3
Master Record
Step 4
Compliance Scan
Step 5
Decompose
Step 6
Enrich & Store
Step 7
Serve
Every item that enters the platform flows through the same governed path. The moment it is committed as a master record, it becomes available through every query pattern, every protocol, and every governed surface — as decomposition completes asynchronously in the background.
Every ingested item — document, row, file, event — produces a single, immutable master record that serves as the system of record. Content is hashed, deduplicated, versioned, and tied to a universal identity. Every downstream representation traces back to this anchor.
Raw content is decomposed into the representations each workload needs: full-text for keyword search, vector embeddings for semantic retrieval, entities and relationships for graph reasoning, tables for analytics, timeseries for metrics, binary objects for fidelity. All share one identity.
A knowledge graph captures what things are and how they relate semantically. A novel context graph captures everything else: where content came from, how it was transformed, which sessions touched it, which queries retrieved it, and how it has been used over time.
Storage backends, embedding models, LLM providers, agent frameworks, auth systems, compliance scanners, data connectors, search engines, and timeseries stores are all abstract interfaces with swappable providers. No lock-in to any particular technology choice.
Role-based and attribute-based access policies are defined once at the platform layer, then translated into native enforcement at every underlying store. Query rewriting, row-level security, document-level security, and object-level IAM all come from a single policy source.
Ingestion flows through a staged, distributed pipeline: validate, master record, compliance scan, decompose, enrich, store, index. Every stage is a queue-routed worker with retries, dead-lettering, and full observability. Compliance runs inline, not as an afterthought.
One query interface spans every store. Natural language, structured filters, semantic similarity, graph traversal, SQL, and hybrid queries are planned, decomposed, executed across backends, and fused into unified results — with access controls applied at every step.
Every capability is exposed through multiple protocols — REST for applications, SQL for analytics tools, a model-context protocol for AI assistants, a typed SDK for developers, and notebooks for exploration. The same governance applies regardless of how the platform is accessed.
Traces, metrics, and logs flow from every service through an open-standard telemetry pipeline. Every ingestion, query, agent invocation, and policy decision is traceable. Cost, latency, and quality are first-class signals, not afterthoughts.
Every feature below is production-minded. For each one, we describe what Context Lake gives you out of the box — and the work teams routinely sign up for when they try to assemble the same capability from piece parts.
Ingest any content once. Context Lake produces an immutable master record and automatically decomposes the content into every representation your workloads need — text, vectors, entities, tables, timeseries, and raw binary — all tied to a single identity.
With Context Lake
Without it
Build and maintain separate pipelines for each representation, reconcile identifiers across stores by hand, and accept that deleting or updating a single document requires coordinated changes across four to six systems.
Entity and relationship extraction is a first-class output of the ingestion pipeline. Entities are resolved across documents, versioned over time, and scored for confidence — with the graph queryable alongside every other representation.
With Context Lake
Without it
Stand up a separate graph database, build your own entity extraction service, maintain a reconciliation pipeline between the graph and every other store, and invent your own confidence and versioning model.
Alongside the semantic knowledge graph, Context Lake maintains a temporal context graph that captures provenance, transformations, sessions, queries, and usage. This is the missing layer that makes AI outputs explainable and data lineage actionable.
With Context Lake
Without it
Manually instrument every pipeline, retrieval, and transformation to emit provenance events, then build your own lineage store — and still face an audit without a single source of truth tying it all together.
The default search pipeline blends vector similarity, keyword relevance, and graph signals, fuses them with reciprocal rank fusion, then re-ranks with a cross-encoder. Production-grade retrieval is the starting point, not a project.
With Context Lake
Without it
Build a retrieval service, tune chunking, wire up hybrid fusion, integrate a re-ranker, and redo it every time you change embedding models — all while trying to keep authorization consistent across three retrieval backends.
Define role-based and attribute-based policies once. Context Lake translates them into native enforcement at each underlying store: query rewriting, row-level security, document-level security, field redaction, and object-level IAM — all driven from a single policy source.
With Context Lake
Without it
Implement authorization in the application layer for every new surface, accept the drift between what your UI allows and what your SQL tool allows, and rebuild the translation every time a new store is adopted.
PII, PCI, and PHI detection runs inline during ingestion through a tiered scanner pipeline. Findings drive classification, redaction, tokenization, or quarantine before content is decomposed — keeping regulated data out of your embeddings, indices, and caches.
With Context Lake
Without it
Bolt a PII scanner onto one pipeline, forget it on another, and discover sensitive fields months later inside an embedding index that must be fully rebuilt.
One query surface spans every store and every pattern: natural language, structured filters, semantic similarity, graph traversal, SQL, and hybrid. Plans are decomposed across backends, executed in parallel, and fused into a unified result envelope.
With Context Lake
Without it
Publish one API per backend, force every consumer to learn which system holds which data, and watch each team build its own cross-store join logic in the application layer.
LLM access is a governed platform service, not a library dependency. A proxy layer handles provider routing, model cascading, prompt versioning, cost tracking, and semantic caching. A pluggable agent layer exposes every platform capability to AI agents through a standardized tool protocol.
With Context Lake
Without it
Embed provider SDKs across every service, re-implement authorization inside each agent, rebuild prompt versioning in a wiki, and lose visibility into which team is burning which share of your LLM budget.
Nine categories of pluggable providers — storage, embeddings, auth, message brokers, graph, agents, compliance, search, and timeseries — all behind stable abstract interfaces. Providers are discovered at startup and configured hierarchically at platform, tenant, and user scopes.
With Context Lake
Without it
Hard-code a specific database, search engine, and LLM provider into your application, then rewrite when any of them has to change.
Applications use REST. Data and BI tools connect over standard SQL. AI assistants use a model-context protocol. Developers use a typed SDK. Data scientists use notebooks. Every surface sees the same data, the same governance, and the same capabilities.
With Context Lake
Without it
Build a separate integration for each consumer class, then keep four drivers, two SDKs, and a handful of MCP tools in sync with every schema change.
Every service emits open-standard traces, metrics, and logs. Distributed trace context flows through the ingestion pipeline, the query engine, and every agent invocation. Cost, quality, and latency are graphed by default.
With Context Lake
Without it
Instrument every service ad-hoc, maintain disconnected dashboards per system, and lose the ability to answer "why was this answer slow" across teams.
A full platform runs in a modest compute footprint in development and scales cleanly into a production orchestration layer. Convergence of relational, vector, and graph workloads onto a single operational profile dramatically cuts the number of systems to run at small scale.
With Context Lake
Without it
Run six operationally distinct database systems from day one, each with its own backup, failover, upgrade, and capacity story.
Interested in working with us or learning more about our projects? We'd love to hear from you.