The access layer for local clinical guidance.
GuidelinesIQ turns available guidelines into a source-grounded access layer for clinicians, trainees, and the care team, with page-level citations, figure-aware retrieval, and a workflow posture built for trust.
Live product pattern
Source-grounded retrieval for local guidance
Why this matters
The answer surface stays inspectable instead of asking users to trust a black box.
Institution validation
The ingestion pipeline does more than index files. It tests retrieval and grounded answer behavior against each institution's uploaded guideline set before activation.
Each guideline set goes through ingestion, retrieval testing, and readiness review before it becomes the live local corpus. That keeps deployment tied to institution-specific performance rather than a generic benchmark alone.
What’s lacking in clinical AI right now
The problem is not that hospitals lack guidance. The problem is that the guidance is too slow to retrieve, too hard to inspect, and too easy for generic AI to distort.
Clinical guidance exists, but access is broken.
The right answer is often buried in a PDF, image-heavy pathway, or scanned protocol at the exact moment speed matters most.
Generic LLMs are the wrong tool.
They answer from broad priors, not your local standard of care. That creates fake certainty without a usable trail back to policy.
National guidance is not local workflow.
Reference tools and national-scale medical LLMs can be useful, but they do not understand your local protocols, escalation pathways, consult expectations, or operational constraints. Clinical work still happens in the local setting.
Why GuidelinesIQ is different
This is not a chatbot wrapped around a PDF. GuidelinesIQ is built around local guidance retrieval, visible evidence, and workflows that fit the way care teams already work.
Every answer points back to the local source.
GuidelinesIQ responds in a retrieval-first pattern with source chips, page references, and support for highlighted excerpts.
Pathways stay visual.
Figure-aware retrieval keeps the actual algorithm, table, or pathway visible alongside the answer instead of reducing it to a simplified summary.
Built for real clinical roles.
The same underlying corpus can support bedside lookup, trainee teaching, and audit review without asking clinicians to change the way they already work.
See GuidelinesIQ in action.
Explore the product, request a pilot, or contact our team.