AI for AI® · DSAIL® Verification Engine
Use AI agents for life science decisions you can prove
Put AI behind submissions, safety, quality, and medical communications.
Every module summary, protocol amendment, or safety narrative is checked against encoded policy before it reaches a reviewer, an inspector, or an agency. Not a smarter prompt. Formal proof.
Built for the standards governing life sciences AI
The standards your inspector will ask about. Jaxon encodes them as enforceable logic — select one to see where it is verified.
Why probability isn't proof
AI is already inside submissions, safety, and medical content. The question is whether you can answer for it.
Most AI controls ask a second model to grade the first. That is still a probability — prompt engineering and retrieval grounding lower the error rate without changing what it is.
Jaxon splits the work. The LLM does what LLMs are good at: it reads the document and answers tightly scoped, rule-specific questions — does this figure match the value in the module it cites?
DSAIL® (Domain-Specific AI Language) computes the verdict.
The problem
AI you cannot verify is AI you cannot defend.
AI drafting module summaries, protocol amendments, safety narratives, and quality investigations introduces errors precisely where they are least tolerable. Jaxon sits as an API layer between your LLM workflow and the output, and it checks each assertion before the document becomes a record.
One of three, every time — and each one names the rule that produced it.
A model that cannot say it does not know will guess. UNKNOWN is where the guess would have been.
Use cases · Life Sciences
Five document systems, one verification layer.
Clinical, regulatory, quality, and safety each answer to different guidance and file different documents. They share one problem: an AI output nobody can prove. Jaxon encodes each standard separately and applies the same deterministic check to all of them.
How it works
How Jaxon verifies an AI output: output in, verdict out, token logged.
Deterministic · signed verification token · auditable trace for every determination
Where the rules come from
For the developer — how you integrate
Define policy rules
Your team — with Jaxon’s onboarding — authors rules in DSAIL®, a domain-specific language that encodes ICH guidance, submission standards, protocol logic, and SOPs as formal logic. Rules compile once and deploy as a versioned, auditable artifact.
Call the API at output
After your LLM generates the draft, POST the output, the source record, and the ruleset context to Jaxon’s REST endpoint. Any language, any framework, any model — no vendor lock.
Parse the verdict
Jaxon returns JSON, one of three verdicts. TRUE: advances, with a cryptographically signed audit token. FALSE: blocks, with a structured exception object (rule_id, violation_type, field, governing_text). UNKNOWN: returned when the source material does not support a deterministic verdict, rather than a probabilistic guess being made in its place.
Log & chain of custody
Every response carries a cryptographically signed verification token binding the output, the ruleset version, the verdict, and the timestamp — ALCOA+ attributable and contemporaneous by construction. Store it alongside the record; produce it to QA, an inspector, or a sponsor audit on request.
Benchmarked against frontier AI
The only system in the test that could prove it
Three models — GPT-5.2, o4-mini, and Gemini 3 Pro — ran two ways each: once with DSAIL® enforcing the rules, once with RAG alone; same models, same inputs. The only variable was DSAIL®.
Only DSAIL produced a human-auditable reasoning trace on every determination. Because the architecture is deterministic, any organization can run the same test against its own policy domain and verify the result. Full benchmark methodology and per-item results available on request — info@jaxon.ai
Deployment — your environment, your security model
Where must your clinical, safety, and manufacturing data stay?
Fastest to production
Most isolated
Constraint
Non-regulated workflows
Constraint
Must stay in your cloud
Constraint
Must stay in your perimeter
Constraint
No external connectivity
Who it’s for
Two roles, one verification layer.
Jaxon serves the quality and regulatory leader who clears AI for production, and the engineering team that has to make it work.
You own
Quality and regulatory own the rules.
Regulatory affairs, quality, pharmacovigilance, and medical leads author the policy, and version-control it. Accountability stays where it belongs: the sponsor remains responsible for the content, and Jaxon provides the documented evidence trail that supports it.
Head of Regulatory Affairs · VP Quality / QA · Head of Pharmacovigilance · Chief Medical Officer
You get
01Production, not pilots
Every module summary, amendment, narrative, and medical response is verified against encoded standards before it becomes a record.
02Exception-only workload
Jaxon clears the routine cases; judgment concentrates on the exceptions.
03Inspection readiness built in
The verification record is the documentation inspectors increasingly expect from organizations using AI.
Get in contact
Ready to move life sciences AI from proof-of-concept to production — and prove every output?
This is where AI governance stops being a policy document and becomes an engineering task. Jaxon’s engineering team works directly with your regulatory, quality, safety, medical, and technology functions to encode your standards and build verification into your environment.
Your standards. Your environment. A verdict you can produce.
What happens next
Bring the workflow — the one where a wrong answer is unacceptable.
We encode your first ruleset — against that workflow, on your actual documents.
You verify it in your environment — before anything is released.
Request API access
Tell us the workflow where a wrong output is unacceptable.
Or write directly: info@jaxon.ai
FAQ
Questions regulatory, quality, and safety teams ask first.
Does Jaxon make our AI use compliant?
No. Jaxon verifies AI outputs against the standards you encode and returns TRUE, FALSE, or UNKNOWN with an audit trail. You still make the compliance determination, and the sponsor remains responsible for the content. Jaxon gives you the defensible record behind it.
What is a deterministic AI verification layer?
It is a check that produces the same verdict every time for the same input and the same ruleset. Instead of asking a second model how confident it is, the LLM extracts atomic facts from the document and deterministic code — your policy compiled into formal logic — computes the verdict. The output is a proof traced to a governing citation, not a score.
Does Jaxon replace our quality system or our validation framework?
No. Jaxon is the technical substrate that makes AI-assisted work documentable inside the framework you already run. Every verification is versioned and reproducible, and the record is stored alongside the document it belongs to.
Can AI draft a case narrative or a Module 2 summary?
AI can draft it; the exposure comes next. Both documents make assertions that must hold against a source — the case report behind the narrative, the study data behind the summary — and neither the draft nor a second model can establish that. Jaxon checks each AI-generated draft against the encoded criteria before it becomes part of the record, so reviewers see either a cleared document or a named exception.
Which LLMs does Jaxon work with?
Any of them. Jaxon is a model-agnostic API layer that works with GPT, Claude, Gemini, open-source models, or your proprietary model, with minimal changes to the underlying pipeline. Rules are authored once and applied at runtime — updated when guidance or policy changes, without retraining any model.
Where can Jaxon be deployed for regulated clinical, safety, and manufacturing data?
SaaS managed cloud for non-regulated workflows, your own AWS, Azure, or GCP VPC, fully on-premises, or air-gapped. Private cloud is the recommended deployment for regulated clinical, safety, and manufacturing data.
What does the audit record actually contain?
Which rule ran, what was checked, what passed, what was held, and who resolved it. The signed token that binds each verification to its output and its ruleset version is described under How it works. What matters for an inspection is that the record exists per document and can be produced without reconstructing it after the fact.
What happens when the source document does not settle the question?
The verdict is UNKNOWN, defined narrowly: the input document did not carry what the rule needed. It is not a confidence score and not a middle state between TRUE and FALSE. Nothing is asserted in its place, and the gap is named so you can supply the missing input.