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Abstract Purple Grid

AI workers, built for how your experts already work.

 Our AI agents focus on scaling your volume and streamlining output, allowing your team to maintain essential human oversight without the manual grind.

STEP 1:  AGGREGATE

Pull every relevant document, record, and data point into one place, no matter how messy or scattered the source.

Every 2Q AI worker follows the same 4-step discipline. Tuned to your environment. 

STEP 2:  STANDARDIZE

Normalize it into structured, comparable, machine-readable form.

STEP 3:  APPLY EXPERT JUDGEMENT

Evaluate it against your policies, protocols, and criteria — not a generic industry standard.

STEP 4:  PROVIDE STRUCTURED OUTPUT

Deliver a decision, a draft, or a flag, with the evidence and citations to back it up. Before anything reaches a reviewer,

it passes a final deterministic check — fixed, explicit rules, not the model's own probability-based judgment. 

Our Healthcare Workforce - QClinical

Our deepest workforce, with AI workers in production today.  The QClinical  framework can be composed into various solutions for today's most pressing healthcare challenges.

CLINICAL DATA EXTRACTION &
NORMALIZATION

This is the extraction discipline underneath every workflow above: messy clinical documentation converted into structured, citation-backed facts, with full source provenance on every field. It's what makes every worker's output defensible in front of a payer, a regulator, or an auditor.

CLINICIAL DOCUMENTATION &
CDI INTELLIGENCE

AI CDI analysts read every note, consult, lab, and medication record in the encounter overnight, before the chart closes. They surface documentation gaps against the draft discharge summary, generate ranked query drafts with source citations, and produce pre-populated discharge summary drafts. Query logic is grounded in your facility's documentation standards and payer mix.

CARE GAP MANAGEMENT &
POPULATION HEALTH

AI workers identify, prioritize, and close open care gaps across your attributed population at a scale no manual outreach team can match — running multi-channel outreach and tracking closure against your HEDIS measure targets and STAR rating goals.

PRIOR AUTHORIZATION &
CLINICAL CRITERIA INTELLIGENCE

AI workers read payer policy documents and patient clinical records side by side, mapping every requirement against the chart before the PA packet is even assembled — surfacing what's met, what's missing, and what's genuinely ambiguous. Requirements are flagged at the point of ordering, not after a denial. Letters of medical necessity are drafted directly from cited clinical evidence, and pending requests are triaged by denial risk before they leave your building. Criteria are mapped to your specific payer contracts and protocols, not generic coverage guidelines.

Our EDGE AI Workforce           DISCONNECTED BY DESIGN

Mission data has to be

exploited where it's

collected.      

Our workers run on small, SWaP constrained hardware - disconnected, air-gapped, denied environments included. Long-running agents with persistent, structure memory let compact models reason far bigger ones. No cloud reach-back required. 

AIR-GAPPED
DDIL-READY
SMALL HARDWARE
FULL AUDIT TRAIL

What makes an AI worker, not just an AI tool.

Human authority, not human-in-the-loop as an afterthought.

 

Deterministic checks, not probabilistic ones. 

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Every output traceable, every decision auditable.

 

Your ground truth, compounding. 

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Knowledge that stays, and belongs to you. 

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No silent changes.

The power behind your AI workforce.

Every custom solution we deliver runs on QNya, our proprietary governed multi-agent AI platform. Your data is always yours — protected, private, never leveraged beyond your engagement.

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What you're really getting is managed care: we handle the infrastructure, governance, updates, and monitoring that keep your AI workers running — so you don't have to.

 

And because it's managed, growth doesn't mean starting over. New capabilities mean configuring what's already there.

See a digital worker on
your own workflow.

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