The human check on healthcare AI.
Pulsemark pairs deep pricing, benefits, and compliance expertise with hands-on data science — so your AI is right where it counts: claims pricing, reimbursement logic, and plan compliance.
Trusted across the self-funded ecosystem
AI can sound confidently right about claims pricing and reimbursement — and be completely wrong.
Most teams can tell whether AI output reads well. Almost no one can tell whether the underlying numbers, logic, and compliance reasoning actually hold up in the real world. That gap is where the costly mistakes live.
Five ways we make AI trustworthy in healthcare.
From first roadmap to trained team — domain-grounded work, not generic prompt engineering.
AI strategy & roadmapping
Where AI actually moves the needle for pricing, benefits, and operations — sequenced into a roadmap your stakeholders can fund and ship.
Model development & data science
Custom models built on real claims and pricing data, with growth and benchmarking logic that reflects how the business actually works.
Regulatory & compliance
FDA, HIPAA, and self-funded plan frameworks designed in from day one — so what you build survives scrutiny, not just the demo.
Implementation & integration
Connecting employers, administrators, and providers — turning a working model into operational infrastructure people trust and use.
Staff training & change management
Your team learns to read AI the way a practitioner does — checking whether the numbers, logic, and compliance reasoning hold up, not just whether the language sounds plausible.
We evaluate AI the way Nicole built her career.
By checking whether the work holds up in the real world — not whether it reads well.
Map the real rules
Pricing models, plan language, reimbursement logic, and the regulations that govern them.
Model with intent
Develop or assess AI against benchmarked, real-world data — not plausible-sounding output.
Catch the confident errors
Stress every number and compliance claim until the logic actually holds.
Hand off with confidence
Integrate, document, and train your team to keep the bar high after we leave.
Outcomes measured in dollars and defensibility.
Representative engagements. Detail available under NDA.
overstatement in an AI pricing model — caught before it reached the plan, by checking the reimbursement logic.
from raw trend data to a benchmarked procedure-pricing model the operations team trusts.
compliance gaps after rebuilding benefit plan language with HIPAA frameworks designed in.
Nicole Austin
I specialize in healthcare pricing strategy, employer benefits design, and the regulatory frameworks that govern self-funded health plans — the domain knowledge that lets me catch when an AI model is confidently wrong about claims pricing, reimbursement logic, or plan compliance, not just whether the language sounds plausible.
Over the past several years I've built commercial and pricing infrastructure from the ground up in a healthcare operating environment: benchmarked procedure-pricing models, compliant benefit plan language, and the operational frameworks that connect employers, administrators, and providers. I pair that practitioner judgment with a sales and data-analytics background across consumer and B2B markets, where my work centered on building growth models from raw trend data.
Notes on getting AI right in healthcare.
When the model is confidently wrong about claims pricing
How to spot reimbursement logic that reads plausibly but breaks against real plan rules.
Read note →Designing HIPAA in from day one, not after the demo
A practical checklist for self-funded plans deploying AI on member data.
Read note →A roadmap your CFO will actually fund
Sequencing AI work so each phase pays for the next — starting with pricing.
Read note →Let's talk about what AI can do for you.
A working session to map where AI fits your business — and where it doesn't. We'll cover three things:
- What AI can actually do for you. The real, defensible use cases in pricing, benefits, and operations — separated from the hype.
- What I can build for you and your infrastructure. Models, benchmarks, and workflows that fit how your team already works — not a rip-and-replace.
- How AI is already affecting your pricing & benefits. Where it's quietly shaping your claims, reimbursement, and plan decisions — and how to stay ahead of it.