The FDA is using AI to review your submissions. Are you?

Catch what inspectors will find.
Before they find it.

LinuxToaster BioPharma automates quality checks on regulatory submissions — validating thousands of references, cross-links, and consistency checks that no human team can cover at scale. The same work the FDA is now doing with AI on their side.

Automated Document QC
Full Audit Trail
SOC 2 Type II In Progress
On-Premise or Hosted
Air-Gap Capable

Submissions are getting bigger.
Quality teams aren't.

A single regulatory submission can contain thousands of references, cross-links, and internal consistency requirements. The FDA is no longer using humans to validate them. Biopharma shouldn't either.

Thousands of references that must be valid

Regulatory submissions contain thousands of cross-references, hyperlinks, and citations. Every one must resolve correctly. A single broken link found during review can trigger a deficiency letter.

Errors cost millions per day

A documentation deficiency found during an FDA inspection can delay drug approval by 6–12 months — at $1–5M per day in lost revenue for a commercial-stage product.

The FDA raised the bar

The FDA is using AI to validate references, check links, and flag inconsistencies in submissions. If their AI catches something your team missed, you're already behind.

AI is probabilistic.
Your submissions can't be.

Getting the combination of probabilistic AI and deterministic software right is what delivers results. Most AI tools treat the model as the entire system — prompt in, answer out, hope for the best. LinuxToaster BioPharma uses software harnesses to constrain and validate AI at every step. The AI does what it's good at — reading, extracting, synthesizing. Code does the rest: enforcing rules, validating references, checking structure, ensuring consistency. Every finding in a report is traceable to a verifiable check, not a guess.

Software harnesses, not prompt-and-pray

Regulatory rules, reference validation, and cross-document checks are enforced by code — not left to probabilistic inference. AI handles extraction and synthesis; software handles correctness.

Every finding is traceable

When the system flags a broken reference or a version conflict, that finding comes with a citation chain — which document, which field, which rule it violated. Your team reviews evidence, not AI opinions.

Built on what we learned implementing CIS

We hardened systems to CIS benchmarks before we built document analysis. That experience — knowing where security, auditability, and correctness requirements intersect — is baked into the architecture. We don't bolt compliance on after the fact.

Example: Submission Quality Check
AI Extract & classify — AI reads and ingests documents
Code Validate references — Software checks every link, citation, cross-reference
Code Check consistency — Rule-based cross-document verification
AI Synthesize findings — AI prioritizes and explains flagged items
Code Format report — Structured output with citations and audit trail

The cost of doing nothing
vs. the cost of getting started

These are the numbers your quality and compliance teams already live with.

Cost of a missed finding
$1–5M / day
Revenue lost during an FDA-imposed delay on a commercial-stage product approval
Manual QC burden
1,000+ hours
Person-hours per program for document completeness review across a regulatory submission
First workflow live
2–4 weeks
From approval to running your first automated document quality check
Deployment
$25K – $100K
Pre-configured appliance with encrypted local storage, local AI inference, and air-gap capability. On-premise or hosted in our data center. Your team approves one device — every subsequent application is a software update. Ships ready, not a 6-month IT project.

Quality checks across the submission lifecycle

From trial master files to final regulatory submissions, the same engine applies: AI reads and extracts, deterministic code validates and verifies, humans review and decide.

eTMF Quality Control

Automated consistency checking across your Trial Master File. Identifies gaps, missing signatures, version conflicts, and reference model deviations — then generates a prioritized findings report for human review.

  • Documents in, report out. No system integration needed.
  • Completeness validated against TMF reference model by software, not inference.
  • All findings reviewed by your team before action.

Regulatory Submission Checking

Validate the thousands of references, hyperlinks, and cross-citations in your submission before the FDA's AI does. Software checks every link resolves, every reference exists, every cross-document claim is consistent.

  • Cross-reference CTD modules for internal consistency.
  • Validate every hyperlink, citation, and reference across the package.
  • Flag discrepancies across clinical study reports, labels, and summaries.

On the roadmap

CRO Data Normalization

Standardize heterogeneous data deliverables from multiple CROs into a consistent internal format.

Clinical Study Report QC

Cross-check tables, figures, and in-text references within CSRs for internal consistency before submission.

Adjacent Verticals

The same engine — AI reads, code verifies — extends to defense, legal, and financial services with industry-specific rulesets.

Built for the review
your security team will require

We know the appliance won't get installed until IT and InfoSec sign off. Here's what they'll find.

Data Residency

Documents are stored and processed on an encrypted local appliance. Sensitive data never traverses a network. De-identified content can optionally route to cloud models for deeper analysis — you control what stays local and what doesn't.

  • Encrypted SSD, on-device only
  • Local AI inference — no API calls for sensitive data
  • Optional cloud routing for 10+ providers (Anthropic, OpenAI, Google, and more)
  • No inbound network ports required
  • Air-gap mode for classified environments

Audit & Traceability

Every document processed, every model invocation, every output generated is logged with timestamps, model versions, and input hashes.

  • Immutable audit log on encrypted storage
  • Per-workflow provenance chain
  • Exportable for GxP documentation

GxP Validation & Compliance

We own the validation burden so you don't have to. Our SDLC is fully traceable, our toolchain is controlled to GxP standards, and we deliver complete validation packages — no hidden cost for your quality team.

  • IQ/OQ/PQ documentation delivered with the appliance
  • Controlled SDLC with full traceability
  • Validated toolchain (source control, issue tracking, CI/CD)
  • Your team reviews outputs, not validates our software

Accreditation & Certifications

We're pursuing industry-standard accreditations to streamline your procurement and security review process.

  • SOC 2 Type II — in progress
  • ISO 27001 certification — planned
  • Cybersecurity controls aligned to NIST framework
  • Security questionnaire and vendor assessment available on request

Let's find out if this
solves your problem

If your team is spending thousands of hours on manual document QC — or losing sleep over what an FDA inspection might find — we should talk.

30 minutes with a founder. No pitch deck — we'll ask about your workflow and tell you honestly if we're a fit.