Ask your unstructured data anything with LAKEer.Get the most trustworthy answers in seconds.

Plug in a cartridge that encodes your industry's ontology, terminology, and relationships so LAKEer interprets documents against a structured domain model rather than an open-ended guess.

Cartridges available today from DaaX include General Purpose, OSDU Ontology (Oil & Gas), and Good Relations (Agentic Commerce). Cartridge from DaaX Partners available soon. Want to create your own Cartridge? Yes, you can do that! Contact us for details.

Every company has its own language — product names, internal acronyms, industry shorthand. Company Language Import lets you teach that language to LAKEer once, then package it as a reusable Cartridge that travels with your data.

And because it's built on open standards, your vocabulary is never locked into LAKEer. Company Language Import uses SKOS (Simple Knowledge Organization System), the W3C standard for controlled vocabularies.

Aliases, preferred terms, and broader/narrower relationships all follow the same model used across the semantic web — so the vocabulary you build is portable and interoperable, usable in any SKOS-aware system, not just DaaX.

Think of your company's vocabulary like a dictionary that's constantly being edited by different people. Left unmanaged, dictionaries like that get messy fast: someone adds a new entry for a word that already exists under a slightly different spelling, someone else defines a word in a way that contradicts an existing entry three pages over, and pretty soon nobody's sure which definition is the "real" one.

That slow decay is what we mean by schema drift — the vocabulary technically still works, but it's quietly stopped being trustworthy.

LAKEer prevents the structural version of this problem automatically. Every time a new term gets added, the system checks it against the rules of your existing vocabulary before it's allowed in: does this term already exist under this exact name? Does the relationship it's claiming (e.g., "this is a type of that") actually make sense, or does it contradict something already on record? Is it properly connected to the rest of the vocabulary, or is it just floating on its own with no way to find it later?

A term that fails any of those checks gets rejected before it ever reaches your knowledge graph — so the vocabulary can keep growing for years without turning into a mess.

LAKEer doesn't just chunk and embed. Content is extracted from your documents and organized into a knowledge graph guided by your active cartridge — so relationships between vendors, contracts, obligations, and terms are captured as structure, not just similar-looking text.

This is a meaningfully different approach from open graph-extraction tools like Microsoft's GraphRAG, which let an AI model freely decide what counts as an entity and how things relate, with no predefined structure to check against. That's flexible, but it means the same real-world thing can end up represented two different ways in the graph — a "sensor" in one document and a "detector" in another, with nothing telling the system they're the same kind of object — because there's no schema constraining what gets extracted or how.

LAKEer's cartridges provide that schema: every extraction is checked against your ontology's defined types and relationships as it happens, so "vendor," "contract," and "obligation" mean the same specific thing everywhere they show up in your knowledge graph, not just wherever the text happened to look similar.

LAKEer can recognize when the same entity shows up in more than one document and bring what each one says together into a single answer.

If Document A establishes that John is a strong math student, and Document B separately notes that John is a good basketball player, LAKEer resolves both mentions to the same John and can answer with both facts together — not just whichever document it happened to retrieve first.

LAKEer doesn't trust its own first draft answer. ClaimGuard^TM, LAKEer's answer-verification pipeline, decomposes every generated answer into individual claims and independently checks each one against the retrieved evidence before the answer reaches the user.

Claims that fail verification are corrected or regenerated — not passed through. So, when the evidence isn't there, LAKEer says so — instead of returning a plausible-sounding guess.

Every answer ships with a ClaimGuard verification log showing which claims are supported by your sources, with estimated (non-authoritative) values explicitly flagged. Your team always knows the difference between a sourced fact and an inference.

ClaimGuard doesn't only check claims against retrieved text. For cartridges that define their own domain-specific value patterns — for example, a valid price binding in the Agentic Commerce cartridge — ClaimGuard also checks that a claim's entity-value binding matches those patterns before the answer renders, catching a number that's present in a document but attributed to the wrong entity. As cartridges add their own patterns, this check gets stronger without any change to the underlying verification pipeline.

Read the ClaimGuard blog for a full breakdown.

Every query can run in one of two modes. Grounded mode, the default, applies a strict constraint: every claim must trace directly to a specific retrieved passage, with no inference or extrapolation — well suited to specification lookups, compliance checks, and regulatory questions where precision matters more than completeness.

Knowledge mode synthesizes a comprehensive answer across all retrieved evidence — well suited to open-ended or comparative questions.

Both run through the same retrieval and verification pipeline; you can choose the mode per query if you want.