Healthcare · Risk & Compliance
Contract Review Automation for Medical Devices: Governed AI-Native
We design, build, and run AI-native contract review for medical device manufacturers, field service leaders, quality teams, and commercial operations. This page describes the engagement: scope, pricing, timeline, controls, and the KPIs we commit to.
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 contract review for medical devices is a phased engagement (Discovery 2.5 weeks → Build 7 weeks → Run continuous) that ships a production workflow on top of QMS and CRM, moves review cycle time by +38 pts against the medical devices baseline, and is operated under risk & compliance governance from day one.
Key facts
- Industry
- Medical Devices
- Use case
- Contract Review
- Intent cluster
- Risk & Compliance
- Primary KPI
- review cycle time, fallback usage, negotiation rounds, and contract leakage
- Top benchmark
- Audit-log completeness: 62% → 100% (+38 pts)
- Systems integrated
- QMS, CRM, field service platforms
- Buyer
- medical device manufacturers, field service leaders, quality teams, and commercial operations
- Risk lens
- quality management, clinical claims, product support, training accuracy, and complaint handling
- Engagement timeline
- Discovery 2.5 weeks → Build 7 weeks → Run continuous
- Team size
- 2 senior delivery (1 architect + 1 implementer)
- Discovery price
- $8k · 2-3 week sprint
- Build price
- $30k–$40k · 8-12 weeks
Primary outcome
speed up legal and commercial review while protecting standards
What we ship
clause playbook, contract review assistant, redline workflow, and fallback library
KPIs we report on
review cycle time, fallback usage, negotiation rounds, and contract leakage
Why Medical Devices teams hire us for this
Medical Devices teams operate in regulated product businesses where clinical evidence, service reliability, training, and quality documentation matter. Conventional automation usually disappoints in that setting: it moves one task into a workflow tool, but it does not understand context, does not adapt to exceptions, and does not create enough leverage for teams already under pressure. AI-native contract review is different — it treats AI as the operating layer of the workflow, not a feature.
BIS and OECD guidance on AI in regulated sectors (including medical devices) 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 contract review in medical devices-comparable contexts. Sources noted per row. Your actuals are measured against the baseline captured in Discovery.
| Metric | Industry baseline | AI-native typical | Delta |
|---|---|---|---|
Audit-log completeness Every inference call + reviewer action captured with version metadata | 62% | 100% | +38 pts |
Time-to-attestation Quarterly attestation packs assembled from audit log; reviewer signs off in hours | 21 days | 3 days | −86% |
Loss avoided / quarter (vs no AI) Conservative estimate; actuals depend on fraud volume + ticket size | $0 (no AI lift) | $280k median | Net positive |
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
Our operating model is borrowed from production engineering, not consulting. Every prompt has a version. Every output has a confidence score. Every decision has a reviewer or a logged rule. The result for contract review is a workflow that Medical Devices leaders can defend in front of a CFO, a risk officer, or an auditor — not a demo that impresses once.
What we build inside the workflow
Medical Devices workflows are bounded by the systems your team already uses. We do not propose a replacement of QMS; we build the AI-native operating layer on top of it. The Build engagement is fixed-price, scoped against the systems list captured in Discovery, and the integration footprint is part of the statement of work.
Reference architecture
4-layer AI-native workflow for risk & compliance
Source intake → AI orchestration → Action → Human review & quality.See the full architecture diagram for Risk & Compliance →
AI-native vs traditional approach
How a scoped AI-native engagement compares to the traditional alternatives for contract review in medical devices.
| Dimension | Traditional (in-house build or BPO) | AI-native engagement (us) |
|---|---|---|
| Time to production | 6-12 months | 6-10 weeks (thin slice) |
| Pricing model | FTE hourly retainer or fixed staffing | Phased fixed-price (Discovery → Build → opt Run) |
| Audit / governance | Manual logs, periodic review | Versioned prompts, audit logs, reviewer queues, attestations |
| Operator throughput lift | 1.0× (baseline) | −86% |
| Cost per unit | Industry baseline | AI-native engagements deliver thin-slice production in 6-8 weeks with measurable baseline-vs-actuals reporting. |
| Exit path | Multi-quarter notice + knowledge loss | Month-to-month Run, full handover plan in Build SoW |
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 is the only commitment to start. After Discovery, we scope Build with a fixed price. Run is opt-in, month-to-month, no lock-in.
The 4-phase delivery model
Phase 1 · Weeks 1–2
Discovery
We map the workflow, the systems, the decisions, and the baseline metrics. Output: a scoped statement of work.
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
We ship a production thin slice on real data, with versioned prompts, evaluation harness, and human review.
Phase 4 · Weeks 8+
Run
We run the workflow with you weekly, expand into adjacent work, and report against baseline.
Interactive ROI calculator
Estimate your AI-native ROI for contract review
Reference inputs below are typical for medical devices 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 medical devices is asymmetric: a single failure on quality management, clinical claims, product support, training accuracy, and complaint handling 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 review cycle time, fallback usage, negotiation rounds, and contract leakage, current service resolution time, training completion, complaint cycle time, and rep productivity); 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.
Common pitfall & mitigation
The failure mode we see most often on AI-native contract review engagements in medical devices contexts.
Regulator surprise at first attestation
Audit trail is incomplete; reviewer left a 3-week gap in week 4
Audit log designed as primary artifact (not log-as-afterthought); weekly attestation rehearsal
Build internally or work with us
Medical Devices teams that build successfully in-house tend to have an existing ML platform, a labelled data culture, and a product manager dedicated to the workflow. If any of those is missing, the project tends to stall at proof-of-concept. We replace those three dependencies with a scoped engagement and a senior delivery team.
What to ask us before signing
- Ask for a workflow map that shows intake, retrieval, generation, review, escalation, system updates, and measurement.
- Ask for an evaluation plan using real examples from medical devices, not only generic test prompts.
- Ask how we will move review cycle time, fallback usage, negotiation rounds, and contract leakage within the first 30 to 60 days.
- Ask which parts of the process remain human-owned and why.
- Ask for our exit plan: what stays with you if the engagement ends.
Recommended first project
The best first project for AI-native contract review in medical devices is a contained workflow with enough volume to matter and enough structure to evaluate. Avoid the most politically sensitive process first. Avoid a workflow with no measurable baseline. Choose a process where we can ship a production-grade thin slice, prove adoption, and then extend the same architecture to neighboring work.
A practical target is a 30-day build followed by a 60-day operating period. In the first 30 days, we map the work, connect the minimum data sources, build the assistant, and create the review process. In the next 60 days, the system handles real volume, the team measures outcomes, and we improve the workflow weekly. By day 90, leadership knows whether to expand into adjacent work.
Frequently asked questions
How do you automate contract review in medical devices with AI?+
We map the existing contract review workflow inside medical devices, 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 QMS, CRM, field service platforms, runs against a labelled test set, and ships behind a reviewer queue before it sees production traffic. We then operate it, measure review cycle time, fallback usage, negotiation rounds, and contract leakage, and improve it weekly.
What does it cost to automate contract review for a medical devices company?+
Three phases, billed separately. Discovery sprint: $8k (2-3 week sprint). Build engagement: $30k–$40k (8-12 weeks). Run retainer: $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.
What is the best AI agent for contract review in medical devices?+
There is no single "best" off-the-shelf agent for contract review in medical devices — the right architecture depends on your QMS setup, your data, and your risk profile. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for QMS and CRM integrations, and a reviewer queue. We benchmark candidate models against a labelled test set during Discovery and pick the one with the best accuracy/cost ratio for your workflow.
How long does it take to deploy AI contract review for medical devices?+
A thin-slice deployment in 2-3 week sprint after Discovery, with real medical devices data and real reviewers. The full Build phase runs 8-12 weeks. By day 90, review cycle time, fallback usage, negotiation rounds, and contract leakage is instrumented, the team has a baseline, and leadership has the data needed to decide on expansion into adjacent medical devices workflows.
What do we own, and what do you own?+
We own the workflow design, the prompts, the retrieval architecture, the evaluation harness, and weekly improvement. Your medical device manufacturers, field service leaders, quality teams, and commercial operations team owns data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.
How do you handle risk and audit for AI contract review in medical devices?+
Every output is grounded in approved sources, every prompt is versioned, and every reviewer action is logged. We provide a control map covering quality management, clinical claims, product support, training accuracy, and complaint handling, plus quarterly attestations on request.
Sources we reference
The following sources inform the architecture, governance, and benchmarks we apply on medical devices engagements. Cited here so you can verify and dig deeper.
- FDA Digital Health
- Generative AI in the Enterprise — Deloitte AI Institute
- Worldwide AI and Generative AI Spending Guide — IDC
- Principles for the Sound Management of AI Risks — BIS Financial Stability Institute
- AI/ML Software as a Medical Device Action Plan — U.S. FDA
- 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 →Start the engagement
Book a discovery call for Medical Devices
Tell us about your workflow, the systems involved, and the KPI you want to move. We'll send a scoped statement of work within 5 business days.