MTekLabsAI clinicians trust.
Auditors can verify.
For health systems, clinics and health plans. We find where AI gives clinicians time back and speeds up claims, prove it in one clinic or one queue, then scale it safely.
For providers and payers. Patient data protected by design.
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Sources readClinical notesCoverage policyMember history
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AI recommendationMeets coverage criteriaCites the policy section and the chart notes
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Human decisionNurse reviewer approvesNamed reviewer · time · reason recorded
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RecordEvidence pack ready for auditEvery source and step kept
The mandate is clear.
The path isn't.
Health systems and health plans are under pressure to “do AI.” Four problems keep getting in the way:
Pilots stuck in one clinic
The ambient-scribe demo impressed the medical staff. Two years on it lives in one clinic, because EHR integration, clinical governance and costs were never planned.
Patient data in public chatbots
Clinicians paste notes into public tools; staff draft appeals with them. Patient data leaves the building, and every paste is a compliance question.
No one owns the risk
Boards, compliance officers and regulators ask where AI touches clinical and coverage decisions. No one has the answer, or the evidence.
Data that can't carry the load
EHR here, claims platform there, faxes in between. Projects get funded before anyone checks the data can support them.
Getting this wrong costs more than a failed pilot. It costs clinician trust, and a compliance finding you didn't need.
Less time on paperwork.
Faster, defensible decisions.
Four areas where AI moves real healthcare numbers: clinician time, claim cycle time, the cost of compliance and patient experience.
Clinical operations
Give clinicians time back for patients.
For example: ambient documentation, guided intake and triage, and decision support that cites the chart and the evidence.
Revenue cycle & claims
Decide claims faster, with fewer denials and appeals.
For example: faster claim review, prior-authorization support, coding and billing help, and denial and appeal insights.
Quality, safety & compliance
Be ready for any audit, without the scramble.
For example: sorting deviations and complaints, finding audit documents fast, policy answers with citations, and quality-measure reporting.
Patient & member experience
Answer patients and members faster.
For example: navigation and benefits assistants, letter drafting, follow-up after discharge, and call-center support.
Your shortlist will be different. Finding it, and ranking it honestly, is what the Readiness Sprint is for.
Clinicians stay in charge.
Every answer shows its work.
In healthcare, patients, clinicians and regulators all have to believe the answer. We build three things into every recommendation, design and pilot.
People make the consequential calls
A clinician or reviewer signs off on diagnoses, denials and prior authorizations. We test for bias across patient populations and map policies to HIPAA, 42 CFR Part 2 and state privacy law.
AI speeds up your clinicians. Accountability stays with them.
Every answer shows its sources
Each answer cites the clinical evidence, the policy and the chart it used. No black-box recommendations in clinical or coverage decisions.
No determination you can't defend to a patient, a plan or a regulator.
Evidence ready for any audit
Every prompt, source and decision is kept in an append-only log, per user and per patient. Evidence packs for CMS audits, OIG inquiries and accreditation surveys.
When the auditor asks why, you have the receipts.
Patient data stays protected.
AI opens new paths to patient data: new identities, and new data flows across EHRs, clearinghouses and partners. Security is the first gate every recommendation must clear, not a review at the end.
- Find unapproved AI useSpot it in clinics and back offices, and move it onto protected tools.
- Protect patient data everywhereDetect and de-identify PHI, share only what is needed, and encrypt throughout.
- An identity for every person and agentScoped access, and every action traced to who or what took it.
- Defend against attacks on AIGuard against prompt injection and misuse, and filter what goes in and out.
- Your environment, your keysRun in your cloud or on-premises, using only BAA-covered services.
- Continuous testingAccuracy checks, red-teaming and drift monitoring as a routine, not a one-off.
The result: an AI setup your CISO and your compliance officer can both sign, designed to pass BAA, privacy and audit review the first time.
Start with one clinic.
Or one claims queue.
A fixed-fee Readiness Sprint gives your board a defensible starting point in two to three weeks. If you're not ready, the readout says so, and says what to fix first.
- Q1Where does AI relieve clinicians or cut cycle time first, and what is it worth?
- Q2Is our clinical and claims data ready? If not, what exactly is the fix?
- Q3Can we defend AI-assisted clinical and coverage decisions to patients, plans and regulators?
- Q4What will it cost, and when does it pay back?
- Q5Who owns AI risk, clinical and corporate, and how is it monitored?
Typical arc: ranked use cases in about six weeks, a first pilot decision within a quarter, and a first workload in production within two.
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Step 1 · 2–3 weeks · fixed feeReadiness SprintClinical and claims data, EHR and privacy posture, scored
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Step 2 · 4–6 weeksRoadmapUse cases ranked by value, clinical risk and data readiness
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Step 3 · 8–12 weeksPilot-to-ProductionOne clinic, service line or claims queue, connected to your EHR and claims systems
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Step 4 · OngoingEmbedded AdvisoryScale service line by service line, with your team
Advice from people
who ship healthcare AI.
Most AI advice comes from people who have never run a model in production, or sat through a utilization-review meeting. Ours comes from doing both.
- Healthcare fluencyWe frame AI around clinician time, cycle time, denials, quality measures and total cost of care.
- Vendor-neutralBedrock, Vertex, Azure OpenAI or on-premises. We work for your outcome, not a reseller margin.
- Built to hand overYour people and your operating model get stronger at every step, and can run on their own at the end.
- Proven in live useWe built Clairant and Praman & Anvesh for providers and payers. Both run with HIPAA-grade controls.
Claims decided faster, with people approving.
AI agents gather the evidence, deterministic rules decide, and a person approves the outcome or sends it back.
Clinical AI built around evidence.
Surfaces and grades clinical evidence for clinicians, who make the call. Emergency routing follows fixed rules no model can override.
Explore Praman & AnveshIllustrative. These figures come from MTekLabs platform benchmarks and early pilots. They are not a guarantee of results in your environment. We check every target against your own data during the Readiness Sprint, and set a baseline before any number is claimed.
One clinic.
One queue.
One measurable win.
Bring us the workflow that costs your clinicians or your claims team the most. We'll show you what AI can safely do with it.
From first call to a clear decision.
- 1A 30-minute callWe pick one clinic, service line or claims queue.
- 2A fixed-fee sprintScope, team and dates agreed up front.
- 3Your readout in 2–3 weeksAn honest clinical and claims scorecard, and a board-ready readout. You decide what comes next.