Quick Answer: This is not software against consultants. The question is which parts of a 510(k) should be done by a person, and which should be done by FDA regulatory AI software before a person ever looks at it.

The work splits cleanly. Research and drafting are high-volume and database-driven: finding a defensible predicate, checking adverse event history, identifying applicable consensus standards, writing first drafts. Judgment is different: deciding whether a borderline predicate survives review, reading a deficiency letter correctly, knowing when an intended use statement is too broad. Software does the first faster. It does not do the second at all.

Published industry rates for 510(k) support in 2026 run $150 to $500 per hour, or fixed fees from $15,000 to $80,000 [1, 2]. Much of that time goes into the first category.

Nothing makes FDA review faster. FDA's clock is FDA's. What you control is how quickly you reach a filed submission and how few rounds of questions come back.

What regulatory AI software actually changes

Start with what a 510(k) consultant actually does. The work splits into two very different categories.

Research and drafting. Finding a defensible predicate. Comparing indications for use. Checking MAUDE for adverse events on that predicate. Identifying which consensus standards apply. Writing the device description, the substantial equivalence argument, and the labeling sections. Assembling the eSTAR.

Judgment. Deciding whether a borderline predicate will survive review. Reading a deficiency letter and knowing which FDA concern is really being raised. Calling whether your intended use statement is defensible or needs narrowing.

The first category is high-volume, repetitive, and database-driven. It is also where most billable hours go. The second is genuinely hard, genuinely valuable, and not something software should be trusted to do alone.

Complizen automates the first and keeps a human on the second. Step four of Submission Builder is regulatory expert review, and it is not optional. The 510(k) submission service is delivered by regulatory professionals with direct FDA submission experience.

So the honest framing is not that you stop needing expertise. The research and drafting simply stop being the expensive part, which leaves the expert time for the judgment calls that actually decide whether a submission clears.

The questions people ask after searching for a 510(k) consultant

Cost is the first follow-up question, and it is harder to answer than it looks, because the headline number is rarely the number you pay. Five questions surface most of the difference between quotes.

Is the price fixed, or does scope change trigger hourly billing?

Published industry rates run $150 to $500 per hour, with fixed fees from $15,000 to $80,000 or more for a complete submission [1, 2]. Hybrid arrangements are common: a fixed fee for the submission itself, hourly billing for anything outside the original scope. Ask which parts are fixed and what specifically counts as a scope change.

Is the FDA Additional Information response included?

This is the single most consequential line in a 510(k) quote. One widely cited industry model is a fixed submission fee plus hourly billing for FDA response preparation [1]. That is the moment your budget is least predictable, because your review clock is on hold and the work is open-ended. Get the answer in writing before signing.

Are FDA's own fees passed through at cost?

They should be. FDA's fees are paid to FDA. In FY2027 a standard 510(k) user fee is $28,653, or $7,163 with small business status, a roughly 9.9% increase on FY2026 6]. Every MDUFA rate for the current year is listed on our [FDA user fees page. A quote that bundles FDA's fee into the total makes the service portion look smaller than it is.

What does it cost to find out whether you need a submission at all?

Some providers charge for scoping work, some do not. A written assessment covering your product code and class, likely pathway, a candidate predicate, which testing you already hold versus what is missing, the applicable FDA fee, and a realistic timeline is a reasonable thing to expect before committing to a full engagement. Complizen's Gap Assessment is free and covers those points, including a named reviewer.

Who actually reviews the submission, and what are their credentials?

Ask for names and FDA submission history rather than firm credentials. This matters equally for software providers: a platform that produces a draft and leaves you to validate it is a different product from one where regulatory professionals review the output before filing.

Why not just use ChatGPT, Copilot, or Gemini?

This is the most common question, and it deserves a real answer rather than a dismissal. General-purpose models are genuinely useful for regulatory work. They explain unfamiliar concepts well, summarise documents you paste in, tighten prose, and help you think through an argument. Many regulatory professionals use them daily for exactly that.

The problem is narrow and specific: they fabricate citations, and regulatory work runs on citations.

The evidence on fabricated references

Researchers at Stanford and Yale studied this systematically in a legal context, asking models specific, verifiable questions about random federal court cases. Hallucination rates ran from 58% with ChatGPT 4 to 88% with Llama 2 [7]. On questions about a court's core ruling, reporting on the study put the rate at a minimum of 75% [8].

Citation fabrication has been measured directly too. Across 42 topics, 55% of GPT-3.5's references and 18% of GPT-4's were entirely invented, and among the references that were real, 43% and 24% respectively contained substantive errors [9]. The fabrications are hard to spot because they typically pair real author names with non-existent titles [9].

Translate that into a 510(k). A fabricated K-number, a predicate device that does not exist, or a consensus standard misattributed to the wrong revision is not a rounding error. FDA reviewers check these against their own records.

Three problems specific to regulatory work

Training cutoffs mean recent clearances are invisible. A general model knows what was in its training data. A device cleared last quarter, a guidance reissued in January, or a predicate recalled last month may not exist to it. Stanford's researchers noted the same pattern in law: model performance appears to lag several years behind current doctrine [8].

Sycophancy makes bad premises worse. The same study found that models often fail to correct a user's incorrect assumptions, instead building on them [7]. Later work found the effect strengthens when a prompt contains a false premise [10]. In predicate selection, where the whole exercise is testing whether an assumption survives scrutiny, that failure mode is expensive.

Chat output is not an audit trail. A conversation in a chat window does not produce versioning, approvals, or traceability. If your quality system is expected to be 21 CFR Part 11-compatible, a chat log is not a controlled record of how a decision was reached.

The data question, which matters more than the accuracy one

A 510(k) contains your device design, test results, predicate strategy, and intended use. Before clearance, that is confidential commercial information. Where it goes when you paste it into a chat window is a compliance question, not a preference.

On personal accounts, your conversations train the model unless you stop them. This is not hidden. OpenAI's own policy page states that when you use its services for individuals, it may use your content to train its models, and that opting out is something you do through Data Controls or its privacy portal [12]. Business tiers are the reverse: OpenAI does not train on inputs or outputs from ChatGPT Team, ChatGPT Enterprise, or the API by default [12]. The consumer default applies across Free, Plus and Pro [13].

Paying does not fix it. This is the trap. Plus and Pro are consumer tiers, and training is on by default there too. The plans that exclude training are Enterprise, Edu, Team and API, which are a different contractual regime rather than a more expensive subscription [12, 13]. "We pay for it, so it must be private" is the assumption most likely to be wrong.

Opting out is forward-looking only. Turning training off stops future conversations being used. It does not remove content already absorbed into a completed training run, and it does not stop retention, which continues separately [13].

Three practical consequences for a regulatory team:

  • Check which tier your organisation is actually on before anyone pastes submission content anywhere
  • Treat an opt-out as protection going forward, not as a fix for what has already been shared
  • Remember that retention and training are separate settings, and turning one off does not turn off the other

This is the gap regulatory software should close explicitly. Complizen's published position is zero training use on customer data, stated as applying across documents, prompts, outputs and metadata, with tenant isolation enforced at the API layer, AES-256 encryption at rest, TLS 1.3 in transit, and audit logs recording who accessed what and when [14]. Whatever tool you use, those are the specific commitments worth asking any vendor to put in writing.

What actually fixes it

Not a smarter model. Grounding, meaning the system retrieves from a real source and cites it rather than predicting plausible-sounding text.

A follow-up Stanford study measured the difference directly. Evaluating purpose-built legal research tools against a general model, it found hallucination rates of 17% and 33% for two retrieval-grounded professional platforms, against 43% for GPT-4 [11]. That is the actual distinction between a general chatbot and domain software: whether the answer arrives with a traceable citation you can open and check.

Read that result honestly in both directions. Grounding cut the error rate substantially. It did not reach zero, and 17% is still a long way from a number you would file on without checking. Verification matters, and expert review matters, whatever tool produced the draft. Any vendor claiming their AI does not hallucinate is making a claim the research does not support for anyone.

How the two workflows actually differ

The comparison that matters is not software against people. It is one workflow against another.

  Manual workflow AI-assisted workflow
Predicate research Hours spent reading 510(k) summaries one at a time Superagent searches FDA's clearance database and returns cited candidates
Adverse event check Often skipped or scoped separately MAUDE check on predicate risk is a standard workflow
Drafting First drafts written from scratch, billed by time AI drafts every section, expert reviews before filing
Pricing model Hourly, fixed-fee, or retainer; scope changes billed [1, 2] Fixed once scoped, no hourly billing on anything [3]
AI response Frequently billed separately, by the hour [1] Included in the original fee [3]
Status visibility Email updates, whenever they get sent Shared workspace both sides can see
Your work afterwards Scattered across inboxes and local drives Stays in your workspace, versioned and audit-traceable

The status point is worth dwelling on. When a consultant is your official correspondent with FDA, they see your submission's progress and you do not. Collaboration is built so you can invite a consultant into the work without handing over your whole workspace, and so a handoff carries its context with it.

Your submission history is an asset, and most companies give it away. Every predicate rationale, testing decision, and FDA exchange from your first submission is what makes your second one faster. If it lives in a consultant's files, you start from scratch next time. Cloud keeps every document, version, and approval in one role-gated workspace, SOC 2-aligned and 21 CFR Part 11-compatible.

Consultants are using these tools themselves

Worth saying plainly, because the framing of this page could suggest otherwise: Complizen is not built to put regulatory consultants out of work, and several use the platform themselves.

The reason is straightforward. A consultant's value has never been in how fast they can read 510(k) summaries. It is in knowing which predicate will survive review, reading a deficiency letter correctly, and telling a client when their intended use statement is too broad. Regulatory AI software does the first kind of work faster. It does not do the second kind at all.

If you already have a consultant you trust, keep them. Collaboration is built so you can invite them into specific work without handing over your entire submission history, and so a handoff carries its context with it. Your consultant works faster, you see what is happening, and the hours go into judgment rather than research.

If your consultant bills hourly for database work, that is worth a conversation — with them, not instead of them. Most are glad to spend their time on the parts of the job that actually need them.

The second pass: catching what expert review misses

This is the use that surprises people most, and it is the reason Complizen's own regulatory professionals run the platform even on work they have already reviewed themselves.

An expert knows what to look for. A database knows what exists. Those are different things, and the gap between them is where submissions get held up.

No reviewer, however experienced, holds FDA's entire clearance history, recall record, and adverse event database in their head. When Complizen runs a completed draft back through the platform after human review, it regularly surfaces things the review did not catch:

  • A predicate with an adverse event history that changes how defensible the comparison is
  • A recognised consensus standard that applies to the device but was not in the testing plan
  • A required test missed because the product code's guidance was updated after the reviewer last worked in that space
  • A modification to the device description or indications that closes a gap a reviewer would otherwise have to defend in an Additional Information response

None of that is a criticism of the expert. It is a statement about scale. A person reviewing a submission is checking it against what they know. The platform checks it against what FDA has published, across every clearance, recall, and adverse event on record.

The practical value is the order in which you use them. Expert judgment sets the strategy and makes the calls. The draft audit runs afterwards as a systematic check for anything a human pass could reasonably have missed. Neither replaces the other, and doing both catches more than doing either twice.

Which approach fits your situation

Five situations, and what tends to work in each.

Regulatory consultants and small RA firms. The constraint on a consulting practice is how many submissions it can run at once, and research and drafting are where the hours disappear. A platform lifts that ceiling without adding headcount, and the billable time shifts to the judgment clients are actually paying for.

In-house regulatory teams with judgment but no spare hours. You know what a good predicate argument looks like. Spending three weeks assembling one is the problem, not knowing how. Software handles research and drafting while your team keeps every call.

Founders without a regulatory hire. You need the submission done properly and cannot justify a full-time RA hire yet. What matters here is a known cost and a named expert rather than an open-ended hourly engagement.

International manufacturers. If you hold a CE Mark, CDSCO licence, MFDS approval, or TFDA registration, some of your existing evidence transfers to a 510(k) and some does not. Working out which is which before committing to testing budgets is the highest-value early step. You will also need a US Agent and establishment registration, which are separate requirements with their own timing.

Companies expecting to file more than once. Your first submission's predicate rationale, testing decisions, and FDA exchanges are what make the second one faster. That compounding only happens if the work lives somewhere you control.

When you need a consultant more than a platform

Three situations, stated plainly.

  • Your device has no viable predicate. If you are heading for De Novo or PMA, you need deep strategic advisory from the start, not submission automation.
  • You need someone physically in the room. Some companies want a consultant at their facility, in their design reviews, embedded in the team. That is a different service.
  • Your device sits in a genuinely unusual category. A specialist with fifteen years in your exact product code may see something no database surfaces. That expertise is worth paying for.

Being honest about these is not a disclaimer. It is how you judge whether the rest of this page applies to you.

Frequently asked questions

Can regulatory AI software replace a 510(k) consultant?

For research, drafting, and documentation work, largely yes. For regulatory judgment, no. Deciding whether a borderline predicate will survive review, reading a deficiency letter correctly, or telling you an intended use statement is too broad are all calls that need an experienced person. The realistic outcome is not fewer consultants but a different split of what they are paid for.

How much does a 510(k) consultant cost?

Published industry rates in 2026 run $150 to $500 per hour for solo consultants, with fixed fees from $15,000 to $80,000 or more for a complete submission, and retainers between $5,000 and $25,000 a month [1, 2]. Cost varies with device complexity, how much testing guidance is included, and how much documentation you already hold.

What should I check before signing a 510(k) consulting agreement?

Ask three questions. Is the price fixed or does scope change trigger hourly billing? Is drafting the response to an FDA Additional Information request included, or billed separately? And are FDA's own user fees passed through at cost or marked up? One commonly cited industry pricing model is a fixed submission fee plus hourly billing for FDA response preparation [1], which is the point where budgets become least predictable.

What is FDA regulatory AI software?

Software that applies AI to regulatory work for FDA submissions: searching FDA's clearance database for predicate candidates, checking adverse event history, identifying applicable consensus standards, and drafting submission sections. The useful distinction is between tools that retrieve and cite FDA's own records and tools that generate text without a traceable source.

Can I just use ChatGPT or Gemini for my 510(k)?

For explaining concepts, summarising documents you paste in, and tightening prose, yes. For anything involving a citation, no. Stanford's research measured hallucination rates of 58% to 88% on specific legal queries, and citation-fabrication studies found 18% to 55% of generated references were entirely invented depending on model. A fabricated K-number or predicate device in a submission is checked against FDA's own records. The technical fix is grounding: retrieval-based professional tools measured at 17% and 33% against 43% for GPT-4, because they cite a real source rather than predicting plausible text. Lower, not zero, so verification still matters whatever the tool.

Is it safe to paste 510(k) content into ChatGPT?

Check which tier you are on first. On consumer accounts, including Free, Plus and Pro, OpenAI's own documentation states data sharing is enabled by default and must be switched off manually under Settings, Data Controls. Business, Enterprise, Edu and API tiers do not train on inputs by default. Paying for a personal subscription does not change this, because Plus and Pro are consumer tiers. Opting out is also forward-looking: it stops future conversations being used, but does not remove content already absorbed into a completed training run.

Will software make my 510(k) clear faster?

Nothing changes FDA's review clock. Any provider promising faster clearance is overpromising. What you can influence is how quickly you reach a filed submission and how few rounds of FDA questions come back, and both come from better predicate selection and more complete documentation at the point of filing.

Can AI catch things an expert reviewer misses?

Yes, and the reason is scale rather than skill. A reviewer checks a submission against what they know. A database check runs it against everything FDA has published across clearances, recalls, and adverse events. That difference tends to surface predicates with adverse event histories, recognised standards missing from a testing plan, or tests required by product-code guidance updated since the reviewer last worked in that area.

Do regulatory consultants use AI platforms themselves?

Increasingly, yes. The constraint on a consulting practice is how many submissions it can run at once, and research and drafting are where the hours go. Consultants using a platform take on more clients without hiring, and spend billable time on judgment rather than lookup.

When is a traditional consultant the better choice?

When your device has no viable predicate and you are heading for De Novo or PMA, when you need someone physically embedded in your team and design reviews, or when your device sits in an unusual product code where a specialist may see something no database surfaces.

Who can see my 510(k) submission status at FDA?

Only the official correspondent and any designated delegates. If a consultant is listed as your official correspondent and you have not been added as a delegate, you cannot see your own submission's progress in FDA's portal. This is worth settling before filing.

What happens to my submission documentation afterwards?

That depends on the arrangement. If the work lives in an outside firm's files, your next submission starts from scratch. If it lives in a workspace you control, every predicate rationale and testing decision from the first filing is reusable on the second. This matters most for companies expecting to file more than once.

Key takeaways

This is not software against consultants. Expert review is a required step in Complizen's own workflow. The question is which work needs a person and which is better handled by FDA regulatory AI software first.

Fixed pricing is the practical difference. $11,000 to $20,000 once scoped, no hourly billing, and the FDA Additional Information response included rather than billed while your clock is on hold.

Nothing makes FDA review faster. Better predicate selection and more complete documentation reduce the rounds of questions. That is the honest version of speed.

Your submission history should be yours. It is what makes your second filing faster than your first, and it does not compound if it lives in someone else's files.

A traditional consultant is still right for some situations. No viable predicate, on-site embedded work, or a genuinely unusual product code.

Start with the free Gap Assessment if you want to know where your device stands before committing to anything. Book a demo if you want to see how the platform handles your specific submission.

Book a demo → · Request a free Gap Assessment → · See all pricing →

References

  1. MedEnvoy Global — How Much Do Medical Device Consultants Charge? (industry pricing survey; hourly, fixed-fee, retainer, and hybrid models). https://medenvoyglobal.com/blog/how-much-do-medical-device-consultants-charge/
  2. Cruxi — FDA 510(k) Consultant pricing and comparison (published consultant rate ranges by provider type). https://cruxi.ai/pages/regulatory/fda-510k-consultant.html
  3. Complizen — Services pricing: all nine services, fixed prices, and what is never charged. /services/pricing
  4. Complizen — Platform pricing: Free, Starter, Pro, and Enterprise plans. /platform/pricing
  5. Complizen — 510(k) Submission service: scope, the nine parts of a submission file, and the five-step process. /services/510k-submission
  6. Federal Register — Medical Device User Fee Rates for Fiscal Year 2027 (91 FR 48134). https://www.federalregister.gov/documents/2026/07/30/2026-15335/medical-device-user-fee-rates-for-fiscal-year-2027
  7. Dahl, Magesh, Suzgun and Ho — Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models, Journal of Legal Analysis 16(1):64–93 (2024), doi:10.1093/jla/laae003. https://arxiv.org/abs/2401.01301
  8. Foley & Lardner LLP — Stanford Study Finds High Percentage of Errors Using Large Language Models in Legal Contexts (secondary source reporting the Dahl et al. findings). https://www.foley.com/p/102ixtc/stanford-study-finds-high-percentage-of-errors-using-large-language-models-in-leg/
  9. Walters and Wilder — Fabrication and errors in the bibliographic citations generated by ChatGPT, as summarised in subsequent citation-fabrication research. https://arxiv.org/pdf/2604.03173
  10. LLRX — What the Science Says About Hallucinations in Legal Research (covering sycophancy and false-premise reinforcement). https://www.llrx.com/2026/02/what-the-science-says-about-hallucinations-in-legal-research/
  11. Magesh et al. — Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (Stanford), as reported in LLRX. https://www.llrx.com/2026/02/what-the-science-says-about-hallucinations-in-legal-research/
  12. OpenAI — How your data is used to improve model performance (official policy page). https://openai.com/policies/how-your-data-is-used-to-improve-model-performance/
  13. Evenfall — Does ChatGPT train on your conversations? (secondary source summarising OpenAI's consumer-tier defaults and the forward-looking limits of opting out). https://www.evenfall.ai/guides/does-chatgpt-train-on-your-conversations
  14. Complizen — Trust and Security: data handling, zero training use, tenant isolation, encryption, and audit logging. /trust