ClinicalRate / runtime APINEWLive healthcare pay context for LLMs, agents, and pricing models →Live pay context for AI systems →<100ms p95 latency

For ML, agent & platform teams

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Stop your model from drifting into the wrong rate.

Q: What does the API actually return?

P25 / P50 / P75 rate bands, variance flag, confidence score, refresh timestamp, and source data context — by role, MSA, shift, and contract type. JSON in, JSON out, OpenAPI 3 schema.

Q: How does this work with LLMs and agents?

We expose a tool-call schema and an MCP server. Drop into Claude, GPT, Gemini, or any agent framework. The model can call our context endpoint as a tool — no wrapper code needed.

Q: What’s the latency and refresh cadence?

Sub-100ms p95 latency at the context endpoint. Underlying benchmarks refresh daily from millions of healthcare postings. Cache TTLs configurable per integration.

Q: How do we cite or explain model outputs?

Every response is timestamped and references the underlying market data window. When stakeholders ask ‘why this rate?’, you can point to a specific request, refresh date, and sample density.

Q: Can we plug this into our existing model serving stack?

Yes. REST API works anywhere. Python and TypeScript SDKs available. Webhooks for event-driven flows. We’ve been deployed inside SageMaker, Vertex, Modal, Replicate, and custom infra.

Q: What about data residency and compliance?

SOC 2 audit-ready, OAuth 2.0, role-based access, audit logs on every request. Region-aware deployment available. Data residency controls for regulated environments.

A healthcare pay-data API for pricing engines, agentic workflows, and LLM applications. Live benchmarks and variance signals injected at every inference — so your model is current, defensible, and stops drifting silently.

View the API

Live healthcare pay context for the AI infrastructure you already build on

◣ The runtime context endpoint

One call. Inference -time pay context.

Models stop drifting because the context arrives at every request — not at training time. Benchmark, variance, and guardrails delivered in <100ms p95. Cite the result back to your stakeholders.

<100ms

p95 latency at inference

Daily

Refresh cadence on benchmarks

412

Healthcare specialties normalized

14k+

MSAs covered, daily

pricing_agent.py · inference loopResolved in 84ms

# At inference time, on every request:

ctx = hwiq.context(

role="ICU_RN",

msa="12060",

shift="night",

contract_weeks=13,

)

output = model.predict(input, ctx=ctx)

validated = hwiq.validate(output, ctx)

# ✓ Live benchmark

# ✓ Drift detection

# ✓ Dynamic guardrails

# ✓ Audit-trail attached

◣ Live response · 84ms

{

"role": "ICU_RN",

"msa": "12060",

"market": {

"p25": 2380,

"p50": 2520,

"p75": 2710

"variance": "in_band",

"confidence": 0.94,

"refreshed_at": "2026-04-17T08:00Z"

}

Same call works for every model. Pricing engine, recommendation system, agent workflow, LLM RAG context — same API, same payload shape, same SLA.

◣ The thesis

Models don't
drift. They silently
misprice
the market.

A healthcare pricing model trained six months ago doesn't fail loudly — it produces high-confidence outputs against yesterday's market. Confidence stays high. Dashboards stay green. Contracts lock in 12% below market.

◣ The problem

Stale thresholds.
Blind confidence.
Silent failure.

Without an inference-time market signal, every model degrades. Pricing engines, recommenders, agents, LLM copilots — all confidently wrong, all explaining a number no one can defend in a Q3 review.

12%

below market: typical drift after 6 months without context

94%

model confidence on outputs 20% off market reality

Silent

the failure mode for static-context healthcare pricing

when most teams discover it — at quarterly review

◣ Who calls this in

Three system shapes. Same runtime dependency.

Pricing & optimization engines

Stop pricing yesterday’s market.

Live benchmark for every prediction
Variance & confidence signals exposed
Drift caught at request, not at quarter close

▶ Outputs you can defend in front of a CFO

LLM apps & agentic workflows

Healthcare pay context for every reasoning step.

Tool-call ready — JSON in, JSON out
RAG context that’s current, not 12 months old
MCP server available for agent frameworks

▶ Stop hallucinating rates the market doesn’t support

Platform & ML infrastructure

One dependency. Predictable SLA. Audit-ready.

REST API, SDKs, MCP, webhooks
<100ms p95, daily refresh cadence
SOC 2, RBAC, full request audit

▶ Drop-in for any model serving stack

◣ Six capabilities

Everything an AI system needs to price healthcare labor without drifting.

Runtime

Live benchmark per request

P25 / P50 / P75 + confidence by role, MSA, and shift. Refreshed daily, returned in under 100ms p95.

Validation

Drift & variance detection

Compare any model output to the live market. Get back variance, confidence, and an in-band flag — every time.

Guardrails

Dynamic thresholds

Bands move when the market moves. No hard-coded rules to maintain. No cron jobs to retrain on quarterly snapshots.

Agents

MCP server + tool-call schema

Drop into Claude, GPT, Gemini, or any agent framework. Pre-built tool definitions and JSON schemas included.

Auditability

Citable, traceable outputs

Every response is timestamped and citeable. When the CFO asks ‘why this number?’ — point to the request.

Infra

Production-grade SLA

REST API, SDKs (Python / TS), webhooks, OAuth 2.0, SOC 2, role-based access, region-aware deployment.

◣ Inside the inference loop

Four stages. Same call. Every request.

Drop the API into any inference path — model, agent, RAG pipeline, automation. Same shape across every product surface.

Request arrives

Your system receives a pricing, recommendation, or reasoning request involving a healthcare role.

Context fetched

Single call to /v1/context — live benchmark, variance, confidence returned in <100ms.

Model runs with ctx

Inject context into prompt, model input, or post-hoc validator. Output is current by construction.

Output validated & logged

Validator confirms in-band. Result and reference data logged for audit and downstream review.

◣ Static training data

Trained six months ago. Acting confidently today.

Hardcoded benchmarks
Bands baked in at training time. The market moved. The model didn’t know.
Confidence ≠ accuracy
94% confidence on rates 20% off market. The model is sure. The market disagrees.
No variance signal
No way to know if the segment is volatile, calm, or shifting. Every output looks the same.
Unexplainable outputs
Stakeholder asks ‘why this rate?’ The answer is ‘the model said so.’ That doesn’t fly.

◣ ClinicalRate runtime context

Live context at every request. Defensible by default.

Benchmarks refreshed daily, delivered per request.
Variance and confidence signals attached to every response.
Dynamic guardrails — your validator stays correct as the market moves.
Citable, timestamped outputs — defensible at executive review.

Models don't need to be retrained quarterly. They need a live signal at inference. That's what we are.

◣ Outcomes after integrating

What changes inside the model serving path.

<100ms

p95 inference-time latency

One round trip per request. Same SLA whether you call it from a model server, an agent, or a RAG pipeline.

Drift between retrains

Benchmarks update daily. Your model stays current without quarterly retrain cycles or manual threshold tuning.

100%

Outputs citable & auditable

Every response timestamped and traceable. When the CFO asks ‘why this rate?’ — you point to a request, not a vibe.

“Our pricing model was drifting and we didn’t know it. By the time we caught it in Q3 review, we’d locked in contracts 12% below market for three months. We added the ClinicalRate context call inside the inference loop. The drift just stopped.”

ML Engineering Lead

Healthcare workforce platform

“The MCP server made it trivial to plug into our agent stack. Our pricing copilot stopped hallucinating rates the market doesn’t support — and started citing a live benchmark on every recommendation.”

Head of AI

Healthcare staffing technology

◣ Keep going

AI systems run on the same canonical data layer that powers our other features and use cases.

Use case

Integrations→

Wire the same canonical layer into your VMS, ERP, and BI stack.

Use case

MSPs & VMS programs→

Build branded AI experiences for healthcare staffing buyers on top of our API.

Feature

Rate intelligence→

The benchmarking layer your agents call as a tool.

Product

Loop AI→

Our reference assistant — built on the same MCP server you can call.

◣ Frequently asked

For ML, agent, and platform engineering teams.

Q01

What does the API actually return?

P25 / P50 / P75 rate bands, variance flag, confidence score, refresh timestamp, and source data context — by role, MSA, shift, and contract type. JSON in, JSON out, OpenAPI 3 schema.

Q02

How does this work with LLMs and agents?

We expose a tool-call schema and an MCP server. Drop into Claude, GPT, Gemini, or any agent framework. The model can call our context endpoint as a tool — no wrapper code needed.

Q03

What’s the latency and refresh cadence?