Every output traced to its clause and evidence

Your engineers should be designing products. Not assembling evidence.

ForgeComply is the AI execution layer for compliance in regulated hardware. Replace 6-figure consultancy bills with software output in days.

Book a demo See it read a product

30 minutes, no pricing pitch. We open a handful of design-partner seats each quarter.

ForgeComply portfolio workspace — active projects with AI-derived risk states, an AI insight on IEC 60601-1 impact, and live standards monitoring (demo data).

Hero dashboard capture · dark theme · demo data

ForgeComply is an AI compliance-execution layer for regulated-hardware engineers. It maps applicable standards clauses (FDA 21 CFR 820, ISO 13485, IEC 62304, ISO 14971, EU MDR) to your product context, drafts evidence, and flags gaps — turning submission prep from quarters into days. It’s your Quality team’s co-pilot, not a replacement.

The problem

Compliance is a velocity tax on your engineering org.

Your best engineers lose 20–30% of their capacity to evidence assembly. Chasing documents. Re-tracing requirements. Reformatting the same test report for a third regulator. None of it is engineering.

Each submission burns $200–400K and 6–18 months — most of it spent on analysis a machine can now do in hours.

And nothing you own fixes it. Your QMS stores files; it can’t reason over them. Point solutions nibble at the edges — one manufacturer spent $2M on a labelling fix that failed. So the work lands where it always lands: on engineering.

“Our sustaining team only manages regulation fixes and updates. The higher-value work never happens.”

— Director of Systems Engineering, multi-site medical device manufacturer

“Six figures to consultants on every submission — just to understand the requirements and analyse our own evidence.”

— Compliance lead, regulated hardware company

The stakes

The cost of doing nothing.

20–30%

of engineering capacity lost to compliance work

$200–400K

per submission in consultancy and internal cost

6–18 months

per submission timeline, much of it analysis wait

From discovery interviews with engineering and quality leaders at regulated-hardware companies, 2025–26.

ForgeComply turns that into software output in days.

Day one

A filing date, not a blank workspace.

Forge reads the product, not the files. Connect what you already have — certifications, standards, evidence, product documents. Forge builds a live graph and runs a day-one health check across it. Then it does what no consultant will on day one: it commits to a date.

162 standards in scope, day one
12,847 evidence → clause → cert links walked
12 at-risk certs flagged — before you asked
11 months projected to file
The Day-1 Readiness Report — standards in scope, evidence-to-clause links walked, at-risk certifications flagged, and a projected filing date (demo data).

Day-1 Readiness Report · dark theme · demo data

Day-1 Readiness Report — live ForgeComply workspace (demo data).

Every day

AI does the reading. Your team keeps every verdict.

The Decision Queue — typed AI findings awaiting a human verdict, sorted by urgency; every action attributable and audit-logged (demo data).

The Decision Queue · dark theme · demo data

The Decision Queue — every AI action waits for a human verdict (demo data).

Forge reviews evidence, drafts gap closures, maps clauses, and watches your portfolio around the clock. Nothing changes state on its own. Everything the AI wants to do lands in a Decision Queue — one-click verdicts, every action attributable and audit-logged. Your Quality discipline, at machine speed.

  • Seven kinds of typed AI finding
  • PR-style sign-off before evidence finalises
  • 21 CFR Part 11 audit trail + e-signatures

How every output is verified

Checked by an agent. Signed off by a human.

Every output is built to move through the same three stages before it reaches you — each stage logged and attributable.

  1. 1

    Forge generates

    The AI drafts the evidence, mapping, or gap analysis from your product context.

  2. 2

    A checking agent is built to verify

    An independent agent is built to review every output against the source evidence and the applicable standard, flagging anything unsupported before it reaches you.

  3. 3

    Your team signs off

    A qualified person reviews and approves. Nothing finalizes without human sign-off.

Nothing reaches you unchecked. Nothing ships without sign-off.

The honest comparison

Why not just use your QMS — or ChatGPT?

Legacy QMS Generic AI ForgeComply
Knows your product Stores your files — can’t reason over them No product context at all Reasons across your live product graph
Standards analysis Templates and checklists you fill in Plausible text; hallucinated clauses Clause-by-clause mapping, traced to evidence
Drafts evidence No Unsourced drafts you can’t submit Drafts linked to requirements and tests, reviewed by your team
Where it fits System of record — and it stays that A chat window beside the work, not inside it The reasoning layer between your context and your submissions
Audit trail For documents None Every AI action logged and attributable

All three will live in your stack. Your QMS stays the system of record; ForgeComply is the layer that reasons.

See this comparison run live. 30 minutes on a real product context — no pricing pitch. Book a demo

When the ground moves

A new edition drops. Forge has already walked your exposure.

Horizon scanning watches the regulatory sources continuously. When a standard changes, Forge walks the dependency graph — standard → evidence → certification — and triages every affected cert: still valid, retest, notify, or resubmit. Reasoning shown, down to the clause and the exact test it invalidates.

14 min from detection to triage
6 triage outcomes per certification
1 click to start the update workflow
Impact analysis — dependency graph from a changed standard through evidence to affected certifications, each triaged into an outcome (demo data).

Impact analysis · dark theme · demo scenario

Impact analysis, 14 minutes after detection (demo scenario: a future IEC 60601-1 edition).

Prior approvals

What did regulators clear before you?

Similar Approvals — match-scored cleared devices drawn from the public regulatory record (demo data).

Similar Approvals · dark theme · demo data

Similar Approvals — match-scored cleared devices from the public record (demo data).

Devices like yours have already been cleared — the pathway they took, the standards they cited, where they ran into trouble. Forge scans the public record (FDA 510(k), EUDAMED and more) and matches the closest cleared devices, so your pathway is based on real regulatory outcomes, not guesswork.

One medtech engineer described the job as reviewing past FDA assessments to decide how to bring a product to market. Forge does that reading for you — and keeps doing it as the record grows.

And the machine around it.

One product graph underneath — requirements, risks, tests, suppliers, standards — with the operational depth your Quality team expects on top:

  • Enterprise QMS surface — CAPA, management review, change control, and training with Part 11 audit trail (demo data).

    Enterprise QMS · dark theme · demo data

    Enterprise QMS

    CAPA, management review, change control, training — Part 11 audit trail throughout.

  • Per-clause checklists surface — AI-generated evidence-linked gap analysis with assigned owners (demo data).

    Per-clause checklists · dark theme · demo data

    Per-clause checklists

    AI-generated, evidence-linked, owner-assigned gap analysis.

  • Labelling requirements surface — market-by-market labelling matrix traced to evidence (demo data).

    Labelling · dark theme · demo data

    Labelling

    Market-by-market labelling requirements traced to evidence.

  • Regulatory pathways surface — route-to-market analysis per target market, locked and tracked (demo data).

    Regulatory pathways · dark theme · demo data

    Regulatory pathways

    Route-to-market analysis per target market, locked and tracked.

Before you book

Questions engineers ask before a demo.

Is the FDA accepting AI-assisted documentation?
Submissions are judged on their content, not their authoring tools. ForgeComply drafts; your team reviews and owns every artifact — nothing ships without human sign-off, and every AI contribution is logged and attributable.
Does this coexist with our QMS and PLM?
Yes. ForgeComply is a reasoning layer, not a system of record. Your QMS and PLM stay authoritative — Teamcenter is the common PLM case. Forge reads context from where it lives, drafts evidence and gap analyses, and hands the output back into your existing systems.
What’s your validation posture?
Every AI action carries a complete, attributable audit trail, and nothing finalises without human sign-off — that’s the architecture, built to GAMP 5 and 21 CFR Part 11 principles. SOC 2 Type I is in progress; validation documentation is available under NDA.
How is our data isolated?
Tenant isolation per customer. Your product context is never used to train shared models. Role-based access control and complete audit logging are on by default.
Who’s using it today?
A hand-picked cohort of teams in medtech and adjacent regulated hardware. We keep it small on purpose — every partner gets founder-level attention and shapes the roadmap. Ask about the next seat.
What actually happens in a demo?
30 minutes: we walk a live product context — or yours, under NDA — run the gap analysis in front of you, and you judge the output. No pricing pitch on the first call.

Going deeper

Compliance, automated. Engineering, freed.

30 minutes to see whether ForgeComply fits your stack and your submission timeline.

Optional: tell us about your product and timeline — . We’ll only use this to arrange the demo.