Healthcare · Risk & Compliance
Cut Compliance Operations Review Time 70% in Healthcare Providers (Audit-Ready)
hospital systems, clinics, care operations leaders, and patient access teams usually arrive here with two questions: what does AI-native compliance operations actually ship, and what does it cost. Both are answered below, alongside the operating posture and the governance frame.
Projects from $15k · Refundable 7 days · Kickoff within 5 days
Early access: we work with a small first cohort. Engagements are scoped, priced, and shipped end-to-end by our team — not referred to third parties.
In one sentence
AI-native compliance operations for healthcare providers — From Discovery baseline to production traffic in 8-12 weeks, with the operating model — eval harness, reviewer UI, audit log, calibration cadence — handed over as part of Build, not deferred to Run. Expected delta on audit readiness: Net positive.
Key facts
- Industry
- Healthcare Providers
- Use case
- Compliance Operations
- Intent cluster
- Risk & Compliance
- Primary KPI
- audit readiness, control failure rate, review cycle time, and remediation backlog
- Top benchmark
- Loss avoided / quarter (vs no AI): $0 (no AI lift) → $280k median (Net positive)
- Systems integrated
- EHR, RCM, patient portals
- Buyer
- hospital systems, clinics, care operations leaders, and patient access teams
- Risk lens
- patient safety, clinical validation, privacy, consent, and equity
- Engagement timeline
- Discovery 2 weeks → Build 6 weeks → Run continuous
- Team size
- 1 senior delivery + founder oversight
- Discovery price
- $8k · 2-3 week sprint
- Build price
- $30k–$40k · 8-12 weeks
Primary outcome
turn regulatory work into a traceable operating system
What we ship
policy assistant, evidence tracker, control library, and review workflow
KPIs we report on
audit readiness, control failure rate, review cycle time, and remediation backlog
Why Healthcare Providers teams hire us for this
In healthcare providers, turn regulatory work into a traceable operating system is constrained by the speed at which experienced operators can review context, weigh tradeoffs, and act. AI-native compliance operations unblocks the throughput ceiling without removing the operator from the loop — the system handles intake, retrieval, drafting, and first-pass review; the operator owns judgment, exception handling, and final approval.
BIS and OECD guidance on AI in regulated sectors (including healthcare providers) converges on a common requirement: explainable decisions, traceable inputs, versioned models. Our control stack is built against that requirement, not retrofitted.
Industry context: Mid-market and enterprise operators face the same fundamental tradeoff: AI must compress operational cycle time while remaining auditable and integrable with existing systems of record.
Benchmarks we hit
Reference benchmarks from production deployments of compliance operations in healthcare providers-comparable contexts. Sources noted per row. Your actuals are measured against the baseline captured in Discovery.
| Metric | Industry baseline | AI-native typical | Delta |
|---|---|---|---|
Loss avoided / quarter (vs no AI) Conservative estimate; actuals depend on fraud volume + ticket size | $0 (no AI lift) | $280k median | Net positive |
Review backlog clearance False-positive triage automated; reviewers see only the cases that need them | 14 days | 1.8 days | −87% |
False-positive rate (initial alerts) Lift from grounded context + multi-step reasoning before alert escalation | 78% | 31% | −60% |
Benchmarks are reference values from comparable engagements and authoritative sector benchmarks. Your engagement's baseline is captured during Discovery and actuals are reported weekly during Run against that baseline.
How we operate the workflow
Three commitments anchor how we run compliance operations in production for healthcare providers: every output is grounded in an approved source, every action is logged with the prompt and model version that produced it, every reviewer decision feeds the next iteration. Drop any one of the three and the workflow degrades within weeks — we have seen it happen, so we ship all three from week one.
What we build inside the workflow
For healthcare providers workflows, the design choice that matters most is where to draw the boundary between automation and human judgment. On compliance operations, we draw three lines: full automation (high-confidence, low-stakes, reversible actions), assisted review (drafts with reviewer one-click approval), full human ownership (policy edits, escalations, exceptions). The lines are documented, instrumented, and revisited quarterly as confidence calibration improves.
Reference architecture
4-layer AI-native workflow for risk & compliance
The reference architecture treats prompts and retrieval as code: version-controlled, evaluated on every change, deployed through CI. That posture is what makes compliance operations legible to engineering audit twelve months in.See the full architecture diagram for Risk & Compliance →
AI-native vs traditional approach
How a scoped AI-native engagement compares to the alternatives for compliance operations in healthcare providers: in-house build, BPO retainer, generic SaaS subscription, traditional consulting engagement.
| Dimension | Traditional (in-house build or BPO) | AI-native engagement (us) |
|---|---|---|
| Lead time to live deployment | 6-12 months | 6-10 weeks (thin slice) |
| Engagement billing | Time-and-materials or annual contract | Phased fixed-price (Discovery → Build → opt Run) |
| Audit posture | Manual logs, periodic review | Versioned prompts, audit logs, reviewer queues, attestations |
| Per-operator capacity | 1.0× (baseline) | −87% |
| Per-case cost | Industry baseline | Sub-dollar marginal cost on routine envelope |
| Exit path | Knowledge transfer takes 6+ months | Documented exit at every phase; artefacts in your repo |
Traditional process automation projects cost $80-200k+ with 6-12 month payback; AI-native engagements deliver thin-slice production in 6-8 weeks with measurable baseline-vs-actuals reporting.
Engagement scope & pricing
We run this as a fixed-scope engagement with a clear commercial envelope, not an open-ended retainer.
Governed engagement
Three phases, billed separately. You commit one phase at a time.
Phase 1 · Discovery
$8k
2-3 week sprint
Phase 2 · Build
$30k–$40k
8-12 weeks
Phase 3 · Run
$4k–$6k / mo
optional, quarterly attestations available
~$52k–$90k typical year 1 (~80% take the run option, regulated workflows need ongoing controls)
Controls, audit logs, reviewer queues, versioned prompts, and quarterly risk attestations.
Discovery contains its own value (the workflow map, the baseline, the SoW). You can stop after Discovery and still own the artefacts. If you proceed, Build is fixed-scope and fixed-price.
The 4-phase delivery model
Phase 1 · Weeks 1–2
Discovery
We sit with the operator team running the workflow today, watch a working day end-to-end, and produce the baseline that Build will be measured against. Two-week sprint, fixed price.
Phase 2 · Weeks 2–4
Design
We design the operating model: data access, retrieval, prompts, review queues, controls, and the KPI dashboard.
Phase 3 · Weeks 4–8
Build
6-10 week sprint that ships the thin-slice production workflow on top of your existing systems. Eval harness gating every prompt change. Reviewer queue staffed. Audit log queryable. Dashboard live.
Phase 4 · Weeks 8+
Run
Optional Run phase, month-to-month, no lock-in. Weekly performance review against the Discovery baseline. Quarterly architecture retrospective. The cadence is documented; your team can absorb it any time.
Interactive ROI calculator
Estimate your AI-native ROI for compliance operations
Reference inputs below are typical for healthcare providers teams in the risk compliance cluster. Adjust them to match your situation.
Projected
Current monthly cost
$57,000
AI-native monthly cost
$20,070
Annual savings
$443,160
65% cost reduction · ~656 operator-hours freed / month
Governance and risk controls
The cost of getting governance wrong in healthcare providers is asymmetric: a single failure on patient safety, clinical validation, privacy, consent, and equity can cost more than the entire AI engagement saved. We treat governance as the first design constraint, not the last documentation pass. The architecture decisions in Build are made against the risk map captured in Discovery, not retrofitted at the end.
How we report ROI
We commit to a baseline-vs-actuals report every week of Run. The baseline is captured in Discovery (current audit readiness, control failure rate, review cycle time, and remediation backlog, current patient access time, denial rate, clinician documentation burden, and care gap closure); the actuals come from the workflow itself. ROI is not modelled — it is measured and signed off by a named owner on your team. The first 30-day report is the gate to expansion.
Selected portfolio
Real builds — compliance operations in healthcare providers and adjacent sectors
Below are engagements drawn from our active portfolio where the workflow rhymed with compliance operations in healthcare providers or in adjacent contexts. Scope and stack are accurate; client identities are withheld under engagement NDAs.
Q3 2025
Radiology workflow application — case handling and reporting
Medical imaging operator · Europe
Application supporting radiology workflow: case intake, structured reporting, document handling, and quality-assurance loop. Designed for regulated medical-imaging context with audit trail and role-based access.
- Web app + secure storage
- Structured reporting
- Audit-trail compliance
Q2 2026
Authenticated remote voting platform — AGM resolutions, audit trail, EN/AR bilingual
Mid-market property operator · GCC region
Purpose-built e-voting system: per-unit cryptographic authentication, AGM resolution console for admins, real-time tally, full per-vote audit log. Federated identity with the OA management platform so owners use one login. Bilingual EN/AR from day one.
- Next.js + tRPC
- Per-unit auth + audit trail
- Bilingual EN/AR (next-intl)
Q4 2025 → Q1 2026
Owners-association management SaaS — 55+ screens, 47 normalized tables
Mid-market property operator · GCC region
Full operational backbone for a property operator running multiple owners associations: properties, units, owners, accounting, service charges, budgets, maintenance, violations, and a resident-facing community portal — replacing a patchwork of spreadsheets and disconnected accounting tools.
- Next.js + tRPC
- PostgreSQL · Drizzle ORM
- JWT federated identity
Client identities withheld under engagement NDAs. Sector, geography, and scope are accurate. Full case studies on request.
Common pitfall & mitigation
The failure mode we see most often on AI-native compliance operations engagements in healthcare providers contexts.
Reviewer queue overflow
Volume spikes during incident windows; reviewers can't keep SLA, escalations stack
Confidence threshold raised dynamically during volume spikes; secondary reviewer pool on retainer
How the regulatory frame shapes the architecture
Three regulatory pressures shape every healthcare providers engagement we run on compliance operations. The first is explainability — the regulator's right to receive a coherent rationale for any decision the workflow produced, in language a senior examiner understands. The second is replayability — the ability to reconstruct the inputs, model versions, and reasoning chain that led to that decision, six months or two years later. The third is segregation of duties — the line between automated action, drafted-with-review, and reserved-to-human steps, with no operator able to silently widen the automation envelope.
We address all three at the architecture level rather than as policy overlays. Explainability is wired into the prompt pipeline: every customer-facing output ships with the supporting source citations, the confidence band, and the policy clauses the model applied. Replayability is wired into the audit log: every inference call is stored with its full input context, model fingerprint, retrieval bundle, and downstream effects, with a retention policy aligned to the regulator's longest plausible review window. Segregation is wired into the reviewer UI: each step has a typed permission, each escalation has a named owner, each policy-edit action requires a second pair of eyes from a different team.
The practical effect for healthcare providers leadership is that examinations stop feeling like archaeological digs. The supervisory question — "show me how this decision was made on date X" — becomes a one-query lookup in the audit log, returning the policy clauses, the source citations, the model version, the reviewer trail, and the downstream actions. The traditional posture would assemble that record over weeks; the AI-native posture assembles it on demand. That is the operational difference between a controlled AI workflow and a research prototype dressed in compliance language.
Data residency and sovereignty constraints in healthcare providers are easier to honor when designed into the architecture than when bolted on later. The retrieval index lives in your cloud region; the model provider is selected to align with your data-residency expectations; the audit log retention follows your jurisdiction's longest plausible review window. These are Discovery-phase decisions, not late-Build pivots, because reversing them costs months.
The concrete first-30-day delivery plan
Week 1 — Discovery handover and labelled test set capture. We sit with the operator team running compliance operations today, watch a working day end to end, and capture 200+ real cases as the labelled test set. By Friday we have the workflow map, the system inventory (EHR, RCM, and adjacent), the risk register, and the success metrics aligned with your KPI of audit readiness.
Week 2 — Architecture and integration scoping. We design the four-layer workflow (intake, context, action, review), confirm the retrieval shape, lock the prompt strategy direction, and produce the integration plan against EHR. The output is the Build statement of work with a fixed price and a named deliverable per phase.
Week 3-4 — Build sprint 1: retrieval and intake. We stand up the retrieval index against your approved sources, build the intake classifier, instrument the audit log, and run the first eval cycle against the labelled test set. The thin slice is functional but not production-deployed.
Week 5-6 — Build sprint 2: action and review. We ship the action layer, build the reviewer queue UI, calibrate the confidence thresholds against the labelled test set, and onboard the first reviewer cohort. By end of week 6 the workflow is processing low-stakes production traffic with full audit logging.
The rest of the Build phase widens the production envelope case-by-case based on the reviewer feedback loop. By the end of Build, compliance operations for healthcare providers is running on real traffic with the operating cadence already established.
Closest precedent in our portfolio
The recent build in our portfolio that maps cleanest to compliance operations in healthcare providers is summarised below. Identity withheld under engagement NDA; sector and stack are accurate.
Radiology workflow application — case handling and reporting. Application supporting radiology workflow: case intake, structured reporting, document handling, and quality-assurance loop. Designed for regulated medical-imaging context with audit trail and role-based access. (Medical imaging operator · Europe, Q3 2025.)
The architectural choices that worked there translate to healthcare providers compliance operations with two adjustments: the data-source mix shifts to match your operating systems (EHR, RCM, and adjacent), and the reviewer SLAs adjust to your team's operating cadence. The four-layer pattern (intake, context, action, review), the evaluation discipline, and the audit posture are portable.
For US buyers
US compliance scaffolding for compliance operations in healthcare providers (HIPAA, PHI, NIST AI RMF)
Healthcare Providers engagements touching US clients on compliance operations ship with the regulatory scaffolding your procurement, compliance, and legal teams expect. The framework that matters most for healthcare providers is Health Insurance Portability and Accountability Act (HIPAA) — addressed below alongside the adjacent frames we encounter.
HIPAA
Health Insurance Portability and Accountability Act
Authority: U.S. Department of Health and Human Services / OCR
- Scope
- Protected Health Information (PHI) handling, security safeguards, breach notification, business associate accountability.
- How we ship inside it
- We sign a Business Associate Agreement (BAA) on healthcare engagements that touch PHI. The architecture supports BAA-covered model providers (Anthropic BAA, Azure OpenAI BAA, AWS Bedrock BAA). Audit log retention defaults to 6 years (HIPAA minimum). PHI handling follows minimum-necessary principle at the prompt and retrieval layers.
PHI
Protected Health Information
Authority: HIPAA Privacy Rule
- Scope
- Any health information that can identify an individual.
- How we ship inside it
- PHI is redacted before transmission to non-BAA model providers; retention follows BAA terms; access is logged at the user level. Workflows touching PHI are deployed to BAA-covered infrastructure only.
NIST AI RMF
NIST AI Risk Management Framework (AI 100-1)
Authority: U.S. National Institute of Standards and Technology
- Scope
- Voluntary framework: Govern, Map, Measure, Manage functions for AI system risk.
- How we ship inside it
- Every engagement maps to NIST AI RMF during Discovery. The control map produced becomes the artefact your internal audit and security teams use to defend the workflow.
Premium engagement page · hand-edited
The bespoke playbook for this combination
HIPAA compliance ops automation — risk assessments, BAA tracking, breach notification readiness, audit trail.
Architecture, end-to-end
Compliance operations AI for healthcare providers — annual risk assessment workflows, BAA inventory, breach response coordination, OCR audit-ready evidence pack. Runs entirely under BAA-covered infrastructure.
Source-of-truth ingestion (your existing GRC tool or shared drives) → risk-assessment workflow with HIPAA Security Rule mapping → BAA tracker with expiry alerts + flow-down enforcement → incident triage with breach-determination decision tree → audit log retained per OCR requirements (6 years minimum). All inference happens on BAA-covered providers.
Specific risks we engineer against
The four to six failure modes we have actually encountered on engagements that look like yours. Each has a documented mitigation in the Build SOW.
RiskBreach determination errs and triggers wrongful notification
MitigationAI surfaces analysis; final breach determination is a named Privacy Officer decision. Decision tree logged for audit.
RiskBAA expiry overlooked
MitigationMulti-channel expiry alerting (60/30/7 days), with automated escalation if not acknowledged.
RiskOCR audit gap
MitigationQuarterly audit-ready pack auto-generated; reviewed by Privacy Officer before quarter close.
Reference deltas on healthcare compliance ops
| Metric | Before | After | Window |
|---|---|---|---|
| Annual HIPAA risk assessment prep | 120–200 hours | 30–50 hours | first cycle |
| BAA review cycle time | 2–4 weeks | 3–5 days | 60 days |
| Breach-determination decision time | 5–10 business days | 1–2 business days | 90 days |
| OCR audit pack prep | 60–90 hours | Auto-generated | first cycle |
Reference values from provider compliance team engagements.
Objections we hear most often
We have a GRC tool already (LogicGate, ServiceNow GRC)+
We sit on top, not in place. The workflow augments your GRC with AI-driven evidence collection, risk scoring, and audit pack generation.
Mini SOW
What the Build SOW looks like
Total fee
$28,000 Discovery + Build
Duration
10 weeks to thin-slice production
Week 1–2
Discovery: HIPAA control map, BAA inventory, GRC integration scope.
Week 3–5
Risk assessment workflow + BAA tracker; BAA-covered inference layer.
Week 6–8
Breach response workflow with decision tree; incident audit log.
Week 9–10
Quarterly audit pack generator; Privacy Officer dashboard.
Procurement FAQ
Do we need a BAA?+
Yes, executed with MSA. Our model providers are BAA-covered.
Where does PHI sit?+
Inside your cloud region, BAA-covered.
Real shipped systems
What our clients say
Below: attributions from active clients. Client identities are withheld in public form pending written approval; live references available to qualified procurement contacts on discovery call.
AI SaaS · DACH region
“They shipped the production version of our pricing brain in 6 weeks, including the billing layer and the onboarding flow. We had been bouncing between contractors for 4 months before.”
Founder, AI Pricing SaaS
Outcome: From 0 to live SaaS with paying customers in 6 weeks. Production billing live, AI onboarding flow shipped, 2 pricing tiers active.
Government-licensed legal services platform · GCC region
“A complete bilingual platform compliant with regulator requirements. Technical quality and delivery speed are outstanding.”
Founding team, regulated legal marketplace
Outcome: Ministry-of-Justice-licensed national legal marketplace, EN/AR bilingual, in 16 weeks. Directory + bookings + legal tools + emergency contacts.
Property management operator · GCC region
“We replaced spreadsheets and 4 disconnected tools with a single OA platform. 55 screens, 47 tables, a voting platform, and an internal portal — all on the same identity layer.”
CTO, multi-region property operator
Outcome: Centralised property operations across multiple owners associations. 14-week first release; 8-week follow-on for the staff portal; 6-week follow-on for e-voting.
Before / after
Concrete deltas from shipped engagements
Owners-association management workflows
Property management operator · GCC
Operator was scaling association count and could not maintain manual coordination. Replaced 4 fragmented tools with a single AI-augmented operational backbone.
Metric
Operational surface area
Before
Fragmented across spreadsheets + email + 4 SaaS tools
After (14 weeks Build phase)
Unified SaaS with 55 screens / 47 normalized tables / cross-app identity
Pricing strategy SaaS onboarding
AI pricing SaaS · DACH
Founder shipping AI-native pricing platform for early-stage SaaS. Discovery + Build delivered a working SaaS with subscription billing and an AI brain that learns from each customer.
Metric
Time-to-pricing for a new founder
Before
3–4 weeks of consultant time + spreadsheets
After (6 weeks total Build)
9-step structured AI workflow, completed in 30–45 minutes
Lawyer discovery and appointment booking
National legal marketplace · GCC
Regulated entity needed to launch the national reference platform for legal services. Delivered a Next.js 16 monorepo with bilingual content layer, PDF generation, and police directory.
Metric
Citizen access to certified legal services
Before
Fragmented across social media, no central directory, phone-only booking
After (16 weeks Discovery + Build)
Ministry-licensed bilingual EN/AR marketplace; multi-channel booking; legal tools; emergency hotline
Marketing site + booking funnel
Premium vehicle care specialist · DACH
Niche detailing workshop needed to project premium positioning matching their workmanship. AI-assisted copywriting + image art-direction compressed launch time.
Metric
Brand perception alignment
Before
Generic web presence — did not match workmanship quality
After (3 weeks concept-to-live (AI-augmented build))
Premium responsive site, German-market SEO foundation, appointment-oriented CTAs
For US companies
Start a US-friendly engagement
Discovery from $8,500–$12,000, Build from $35,000–$75,000, optional Run from $5k/mo. Fixed-price, milestone-billed, you own every artefact. Send a short brief and we reply within 5 business days. 11am–4pm ET overlap for live syncs.
USD pricing
Discovery $8,500–$12,000 · Build $35,000–$75,000
US-style commercial
MSA / SOW / mutual NDA standard. DPA with SCCs included.
Limited capacity
We onboard 3–5 new clients per quarter to protect delivery quality.
Build internally or work with us
The opportunity cost of building first in healthcare providers is often invisible: 6-9 months spent hiring, tooling, and converging on a reference architecture is 6-9 months of competitors shipping. The engagement model we propose front-loads the reference architecture and the senior delivery team, then transitions the operation to your team once the pattern is proven.
What to ask us before signing
- Ask for a 30/60/90-day plan with named deliverables, not a vague phase description.
- Ask how we handle the long tail of edge cases the operator team has never encoded — escalation, calibration, capture.
- Ask for the model and provider strategy — single-model, multi-model, fallback paths, cost forecasting.
- Ask how the reviewer queue UX is designed and whether your operator team can shape it during Build.
- Ask for references from healthcare providers-adjacent engagements — sector, scope, and outcome dimensions.
Recommended first project
The first project we recommend for healthcare providers on compliance operations is rarely the one leadership names in the initial conversation. The named project is usually the most politically visible — which is also the riskiest place to ship a first AI-native workflow. We typically recommend the adjacent subflow with the cleanest baseline, the smallest blast radius, and the most repetitive operator work. That first project produces three artefacts that the visible project needs: a labelled test set the operator team has signed off on, a reference architecture against EHR, and a credibility track record with the internal stakeholders who will be asked to support the second engagement. By the time we propose the second workflow — the visible one — the organisational gravity is on our side.
Frequently asked questions
How does AI compliance automation work for healthcare providers?+
Inside a HIPAA-covered envelope: we deploy on BAA-covered model infrastructure, and the AI handles the documentation-heavy layer of compliance — mapping obligations to your policies, drafting control evidence, assembling audit-readiness packets, and flagging missing documentation before an auditor does. Compliance officers interpret rules and approve everything consequential; the AI never makes a compliance judgment on its own. Every output is grounded in your approved policy corpus with citations, and every reviewer action lands in a tamper-evident audit log.
What healthcare compliance tasks are safe to automate first?+
Evidence assembly and documentation drafting — the work that consumes compliance-team hours without requiring regulatory judgment. Concretely: collecting access-review evidence, drafting incident documentation from structured inputs, summarizing regulatory changes against your current policy set, and tracking remediation items. Rule interpretation, regulator interactions, and final attestations stay human-owned. Discovery ($5-8k) maps your control library and locks the PHI envelope before any model touches data.
How do you automate compliance operations in healthcare providers with AI?+
We map the existing compliance operations workflow inside healthcare providers, identify the high-volume, high-structure tasks, and build an AI agent that handles those tasks while routing low-confidence cases to a human reviewer. The build connects to your EHR, RCM, patient portals, runs against a labelled test set, and ships behind a reviewer queue before it sees production traffic. We then operate it, measure audit readiness, control failure rate, review cycle time, and remediation backlog, and improve it weekly.
What does it cost to automate compliance operations for healthcare providers teams?+
~$52k–$90k typical year 1 (~80% take the run option, regulated workflows need ongoing controls). The structure: $8k Discovery (2-3 week sprint) → $30k–$40k Build (8-12 weeks) → optional $4k–$6k / mo Run. Controls, audit logs, reviewer queues, versioned prompts, and quarterly risk attestations.
What is the best AI agent for compliance operations in healthcare providers?+
Model selection on compliance operations for healthcare providers happens against five criteria: quality on your labelled test set, cost per inference at your projected volume, latency budget for the user-facing path, provider reliability over 12-18 months, contractual data-handling posture. We bring the comparative methodology from prior engagements and run it during Build; the winning model is the one that survives all five, not the one that wins the demo.
How long does it take to deploy AI compliance operations for healthcare providers?+
A thin-slice deployment in 2-3 week sprint after Discovery, with real healthcare providers data and real reviewers. The full Build phase runs 8-12 weeks. By day 90, audit readiness, control failure rate, review cycle time, and remediation backlog is instrumented, the team has a baseline, and leadership has the data needed to decide on expansion into adjacent healthcare providers workflows.
What do we own, and what do you own?+
What we ship as code lives in your repository under your IAM. The prompts, the evaluation harness, the integration code, the reviewer UI, the infrastructure-as-code — all in your Git, not in our SaaS. We bring the engineering, the operating discipline, and the cadence; you bring the data, the policy, and the operator team. The handover is documented from day one of Build, not deferred to the end.
What's the auditor's experience of this AI workflow?+
The audit log is queryable on every dimension — input context, model version, retrieval bundle, output, reviewer disposition, downstream action. Pulling the evidence for a randomly-sampled case is a one-query operation. The control map ties each guardrail to a line of code that implements it and a named human owner.
Do you train models on our data?+
No. We do not train any model on client data. Anthropic Zero-Data-Retention is enabled by default; OpenAI default-no-training is honoured. Prompts, retrieval indexes, audit logs, and integration data live in your cloud account under your IAM. At engagement end, every artefact transfers to your repository.
What if we want to exit the engagement?+
Discovery and Build are fixed-scope, so there is no mid-engagement exit cost. Run is month-to-month with 30-day notice. Every artefact (prompts, eval harness, integration code, dashboards, runbooks) is in your repository throughout the engagement, not behind our SaaS. There is no lock-in.
What does success look like 90 days after Build closes?+
audit readiness, control failure rate, review cycle time, and remediation backlog measurably improved against the Discovery baseline. Your team is operating the workflow with the cadence we shipped during Build. The audit log is queryable. The reviewer queue is calibrated. The next workflow scope is informed by real production evidence rather than initial assumptions.
What support is included after the engagement ends?+
Optional Run retainer covers weekly cadence, prompt refresh, retrieval index updates, and reviewer-queue calibration. Architecture-level questions and breaking-change support are billed hourly outside of Run. Most engagements transition Run in-house at month 6-12; we stay available for architecture decisions for 12 months at no extra charge.
How does this integrate with EHR and our existing stack?+
Discovery scopes the integration footprint explicitly. We integrate at the API layer; no replatforming required. The Build statement of work names exactly which systems are connected, which data flows are bidirectional, and what authentication patterns we use (SSO, service accounts, OAuth scopes). The integration code lives in your repository.
What does your team look like during an engagement?+
Discovery: 1 senior delivery lead + 1 PM, ~30 hours/week. Build: 1 senior delivery lead + 2-3 senior AI engineers, ~50-80 hours/week across the team. Run: 1 delivery owner + 1 engineer on weekly cadence. We do not use offshore staff augmentation. Every engineer touching your engagement is senior-level.
Sources we reference
The following sources inform the architecture, governance, and benchmarks we apply on healthcare providers engagements. Cited here so you can verify and dig deeper.
- WHO Artificial Intelligence for Health
- Responsible Scaling Policy — Anthropic
- AI Index Report — Stanford HAI
- AI/ML Software as a Medical Device Action Plan — U.S. FDA
- Generative AI: Charting a Path to Responsibility — OECD.AI
- Google Search Central: helpful, reliable, people-first content
- Google Search Central: URL structure best practices
Concepts on this page:
AI governance·NIST AI RMF·Audit log·Grounding·Guardrails·Model cardFull glossary →High-intent reads
Start the engagement
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