Reliable AI for important decisions
Ensures your AI answers from fact, and tells you what it needs when it can't.
We ground AI in the right facts, making it compact, efficient, and domain-specific.
More is not better. Better is better.
Grounding is the foundation of reliable AI.
How it works
AI that answers from fact and shows its work
01
Ingest
Your documents go in whole. Layout, structure, tables, and cross references are preserved rather than chunked. Anything the system cannot read cleanly is marked for review at the exact place it occurs, and a person decides before it is trusted.
02
Retrieve
A question returns every relevant part of the corpus, with the exceptions, references, and revisions that qualify it still attached. Not the passages that most resemble the question.
03
Ground the answer
Every claim carries the document and the revision it came from. Where the corpus cannot answer, the service says so and names what it would need.
Every answer ships with a coverage report: what was used, what was not, and whether the corpus was sufficient to answer at all.
Runs as an isolated instance on the cloud and in the region you choose. Your documents stay inside your environment.
Not a better RAG. A different guarantee.
Traditional RAG
Easy to stand up if you are technical. Low latency. Reliability that depends on how your documents happen to chunk.
Advanced and strategic RAG
GraphRAG, corrective RAG, agentic retrieval. Higher reliability, bought with high complexity and high latency.
Deterministic grounding
No tuning surface to get wrong. Reliability enforced as a release gate rather than measured as a benchmark score. Low latency, because the work happens at ingestion.
Real questions, from real domains
Grounded in your own knowledge, not the open web. Each one carries the limit it will not answer past.
Energy & manufacturing
"This compressor keeps tripping on high vibration at part load. What is the likely cause, and the fix, given this unit's own service history?"
Ranks the likely causes against this unit's own service records and the OEM manuals, and cites every source.
Will not confirm a root cause without the live vibration trace from the last trip.
Engineering & legal
"Across thousands of internal documents, what do our own standards actually require here, and where do they contradict each other?"
Pulls the governing clauses, surfaces where they conflict, and cites every document.
Flags any requirement it could not trace to a controlling document, instead of guessing.
Robotics
"The robot has to complete this task in an environment it has not seen before. What does it actually know that applies here, and what is it missing?"
Separates what the machine knows and can cite from what it can't, and flags the gap.
Marks the gap it cannot close from memory, so the robot asks instead of acting blind.
Medicine & life sciences
"We need to neutralize this pathogen. What approaches are viable across the published research and our own proprietary studies and assay results?"
Lays out the viable paths across the public literature and your own studies, each one cited.
Separates what the evidence already supports from what would need a new experiment to confirm.
Industries
Where a dropped condition changes the answer
Energy
Operational records, technical manuals, and subsurface data, where the condition that qualifies an answer is often pages away from the answer itself.
Manufacturing
Specs, procedures, and quality records, reasoned over without losing fidelity.
Construction
Drawings, codes, and archives where traceability to source is non-negotiable.
Aerospace
Certification records, maintenance corpora, and airworthiness data where traceability is a regulatory requirement and a safety imperative.
Robotics
Machines that act on what they know. Grounding is the line between autonomy and guesswork.
Legal tech
Where a fabricated citation is a failure, not a footnote.
Medicine
Clinical and research corpora where provenance decides whether an answer can be trusted.
Education
Curriculum, research archives, and institutional knowledge where a confident wrong answer teaches the wrong lesson.
Gaming & Digital Twins
Live models that answer inside a frame budget, where a character or a twin has to stay consistent with the world it is modeling.
Don't see your industry?
The list above is where we have done the most thinking, and it is not a boundary. Tell us what documentation you are working with and what your current tools get wrong, and we will give you a straight read on whether grounding helps.
From AI that talks to AI that acts.
The grounded brains of the autonomous world, where machines act because people trust what they know.
Get in touch
Bring us the questions your tools get wrong.
A slice of real documents and a list of questions is enough to start. We will tell you when grounding is not the answer.