Lodestone Labs provides a document grounding service that ensures AI answers are traced to specific, verifiable sources within an organization's own corpus. The platform ingests documents whole, preserves their structure, and returns every relevant passage with citations, while flagging unreadable material for human review. It integrates with existing AI models and includes a coverage report that identifies when the corpus is insufficient to answer a question.
Funding
Funding not disclosed
Founders
Product
Problem
General-purpose AI systems often generate answers from fragments of text that resemble a query, rather than from the complete set of relevant information. This leads to silent omission, where a model appears to answer correctly but misses critical qualifying clauses, exceptions, or revisions that change the meaning. In regulated or technical domains, a missed condition can be operationally significant, yet most systems cannot distinguish a complete answer from one built on a sliver of evidence.
Solution
Lodestone Labs provides a grounding service that sits underneath an organization's existing AI stack, ensuring every answer is traced to a specific document and revision. The service ingests documents whole, preserving layout, structure, tables, and cross-references, rather than splitting them into fixed-size chunks. When a question is asked, it retrieves every part of the corpus that bears on the query, including qualifying exceptions and references, and returns citations for each claim. If the corpus cannot answer, the service issues a qualified refusal and names what it would need, rather than assembling a plausible answer from unrelated content. A coverage report accompanies every answer, showing what was used, what was not, and whether the corpus was sufficient. The service runs as an isolated instance, either in the cloud or entirely within the customer's own infrastructure, and works with any model, including Gemini, Claude, GPT, or open-source alternatives.
Target Audience
Primary customers are engineering, legal, compliance, and operations teams in regulated or technical industries that need verifiable, citation-backed AI answers from their own authoritative document sets.
Features
- Whole-document ingestion that preserves layout, structure, tables, and cross-references, avoiding the meaning loss of fixed-size chunking
- Retrieval that returns every relevant part of the corpus, not just top-scoring similarity matches, with qualifying exceptions and revisions attached
- Validation stage that flags unreadable material at its exact location for human review before it becomes evidence
- Coverage report for every answer, detailing what was used, what was not, and whether the corpus was sufficient
- Qualified refusal behavior that names what information would be needed when the corpus cannot answer
- Deployment as an isolated instance in the customer's chosen region or entirely within their own infrastructure
- Model-agnostic integration that works with Gemini, Claude, GPT, or open models on the customer's own hardware