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AI for AI® · DSAIL® Verification Engine

AI decisions you can prove, at exchange scale

Not a smarter prompt. Formal proof.

Jaxon is an API that embeds a deterministic verification layer, proved by a formal logic solver, into your existing AI workflows. Every AI-generated listing determination, regulatory report, trade confirmation, or research communication is mathematically checked against your codified ruleset before it reaches a regulator, issuer, or counterparty. Built on DSAIL® (Domain-Specific AI Language).

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Encoded rule frameworks

Jaxon is an API embedded by your engineering team: no UI, no portal, no dashboard. Developers own the integration; your compliance, legal, and risk leads own the rules.

FINRA Rule 6830 — CAT SEC Rule 605 — execution quality SEC Rule 606 — order routing SEC Rule 612 — minimum pricing FINRA 2241 — research reports FINRA 2210 — public communications SRO rule filings — Form 19b-4 SEC Reg SCI — systems integrity MiFID II EMIR Dodd-Frank Title VII ISDA

By the numbers

Four facts that frame every conversation about AI in a regulated exchange.

The pressure

24 hours

To put an SCI event in writing to the Commission, after notifying it immediately. Whatever your systems produced, you have a day to describe it accurately.

SEC Regulation SCI, Rule 1002(b)

5 years

Every document an exchange makes or receives in its self-regulatory activity stays on file, the first two in an easily accessible place. The reasoning behind a determination is read long after the determination.

SEC Exchange Act Rule 17a-1

The answer

1×

Encode the listing rule or supervisory procedure once. Jaxon applies that ruleset version to every AI output at runtime, so a determination made years ago can be re-derived against the rules as they stood.

Signed

Every verified output carries a cryptographically signed token linking the output, the ruleset version, the result, and the timestamp. The written account exists when the check runs, not when it is asked for.

Why probability isn't proof

Reducing the error rate is not the same as being able to show your work.

Without Jaxon

✕ No record of the check

Prompt engineering and RAG reduce error rates but cannot provide the deterministic guarantee that compliance officers need to sign off. An AI-generated listing determination that misapplies governance requirements, or a CAT report that fails data quality standards, is a regulatory finding waiting to happen.

With Jaxon

✓ Signed audit token

Your developers encode listing rules, CAT requirements, and supervisory procedures as formal DSAIL® logic — Jaxon's deterministic, domain-specific AI language. Jaxon executes it against every AI-generated output, returning either a verified result or a structured exception naming the rule, the governing section, and the field in error.

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®.

SEAD-4 benchmark results: Jaxon with DSAIL compared with RAG-only frontier models across completeness, consistency, correctness, and auditability.
With Jaxon Without Jaxon (RAG only)
Metric Jaxon (large LLM) Jaxon (small LLM) GPT-5.2 o4-mini Gemini 3 Pro
Completeness 100% 100% 100% 100% 100%
Consistency 100% 100% 79% 50% 79%
Correctness 100% 100% 36% 36% 34%
Auditability ✔ ✔ ✕ ✕ ✕

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

Use cases — stock exchanges and market operators

Four AI compliance use cases for stock exchanges.

Jaxon's highest-rated deployment for stock exchanges is listing compliance automation and regulatory reporting validation. For exchanges managing thousands of listed companies under continuous SEC supervision, deterministic AI verification delivers the clearest, most immediate return in these workflows.

01

Listing compliance and corporate governance review

Exchanges · LOB managers · developers

Securities exchanges enforce complex listing standards — financial thresholds, governance requirements, shareholder approval obligations, and SEC-mandated disclosure rules — that must be applied consistently across thousands of listed companies. AI is increasingly used to draft compliance assessments, deficiency notices, and staff comment responses, but inconsistent application creates SEC oversight risk and issuer disputes. Jaxon applies a formal verification layer — a formal logic solver, not a second model grading the first — to AI-generated listing compliance assessments, governance review summaries, and staff comment responses, checking them against current exchange listing standards and applicable governance requirements before they are communicated to the listed company or filed with the Commission.

SEC 8-K Nasdaq listing rules NYSE Listed Company Manual SOX governance Audit committee independence

Immediate fit — highest near-term exchange deployment value

How it works

How Jaxon verifies an AI output: output in, verdict out, token logged.

Step 01

AI output

Any LLM: a listing determination, a CAT report, a trade confirmation, a research note.

Step 02

DSAIL® engine

A formal logic solver evaluates the facts against your encoded, version-controlled ruleset — never a probabilistic guess.

Step 03
✓ True ✕ False ? Unknown
  • TRUE — advances, with a signed audit token.
  • FALSE — blocks, naming the rule, the field, and the governing text.
  • UNKNOWN — not enough to decide, rather than a probabilistic guess.
Deterministic · auditable trace for every decision

Where the rules come from

Your policy, in your own language

Your compliance team states the policy as it already exists — listing rules, regulatory obligations, supervisory procedures. Jaxon's onboarding team distills it into formal DSAIL® logic and derives the tightly scoped, rule-specific questions the LLM answers at runtime.

One artifact, not model weights

The unit you author is the rule, compiled once and deployed as a versioned, auditable artifact. When regulations change, you update that one ruleset — no model is retrained, and every past verdict stays reproducible against the version that produced it.

Watch

A verifiable AI compliance layer that replaces prompt engineering.

Engineering teams integrate once. Compliance teams gain permanent defensibility.

Deployment

Your environment, your security model.

Fastest to productionMost isolated

Constraint

Non-regulated workflows

Deployment 01

SaaS / managed cloud

Fastest time to production. Jaxon manages the infrastructure, and data is processed in isolated, encrypted execution environments.

Listed on AWS Marketplace.

Constraint

Must stay in your cloud

Deployment 02

Private cloud (VPC)

Recommended

Deployed inside your own AWS, Azure, or GCP VPC. Your data never leaves your cloud environment, and you keep full control over network egress. Recommended for regulated financial data.

Constraint

Must stay in your perimeter

Deployment 03

On-premises

Deployed on your own infrastructure. No data leaves your physical perimeter, and Jaxon supports exchange-internal networks with no external API dependencies. Suitable for most regulated exchange environments.

Constraint

No external connectivity

Deployment 04

Air-gapped

Fully disconnected deployment for mission-critical environments with no external connectivity.

Reg SCI — vendor systems

Reg SCI obligations sit with the SCI entity, not the vendor. DSAIL® is built so you can discharge yours: perimeter deployment keeps it inside your capacity management, BC/DR, and change control, and deterministic execution with immutable versioned rulesets makes behavior reproducible for SCI testing. Where DSAIL® is classified in your SCI environment, Jaxon commits contractually to vendor-side support.

Who it's for

Two roles, one verification layer proved by a formal logic solver.

Jaxon serves the compliance leader who must defend every AI-assisted decision and the engineering team that has to make it work in production.

For the LOB manager

You control the applications. You sign off on AI-assisted decisions. You face the regulatory examination.

Chief compliance Head of market regulation Head of listing qualifications
  • 01 Consistent determinations, every time Defensible listing determinations across thousands of issuers. Every analyst applies the same encoded rules.
  • 02 Decisions you can defend A full audit trail showing every AI-generated output was formally verified against your codified ruleset before a human acted on it.
  • 03 Reviewers see exceptions only Structured reports naming the rule violated, the governing section, and the offending content. Ninety percent of output passes without escalation.
  • 04 Ready for examination Scale review without proportional headcount growth. Jaxon's verification record is the documentation SEC examiners increasingly expect.

Request access

Ready to embed verified AI into your exchange compliance workflows?

Talk to our team about a ruleset walkthrough specific to your exchange — listing standards, CAT requirements, ISDA terms, or research disclosure rules.

We map the encoding work before any commercial conversation.

  • 01A ruleset walkthrough of the workflows you already run.
  • 02A scoped encoding estimate for those rules.
  • 03A deployment model matched to your security perimeter.

Request API access

Or write directly: info@jaxon.ai

FAQ

Questions exchange and market teams ask first.

What is Verifiable AI?

Verifiable AI checks each AI decision against a structured, enforceable ruleset and returns a result you can audit instead of a probabilistic guess. For a stock exchange, every output carries a TRUE, FALSE, or UNKNOWN verdict for each rule, plus an auditable trace showing the evidence and the exact rule applied. The verdict is deterministic, so the same input always produces the same answer.

What is AI compliance for stock exchanges, and why do exchanges need it?

AI compliance for stock exchanges is the practice of checking every AI-generated output against the exchange's regulatory obligations before it is acted on. Surveillance alerts, disclosure reviews, listing determinations, and regulatory reports must satisfy rules such as SEC Reg NMS, FINRA 2210, and MiFID II, where a judgment call is not a defense once the decision is challenged. Because a probabilistic model cannot show its work, Jaxon applies a structured, enforceable ruleset to each output. It returns a TRUE, FALSE, or UNKNOWN verdict with an auditable trace, so the basis for every decision is documented and repeatable under examination.

How is DSAIL® different from RAG or an LLM-as-a-judge?

RAG retrieves context and an LLM-as-a-judge offers an opinion, and neither can prove a verdict. DSAIL® (Domain-Specific AI Language) has a language model extract the facts, then a formal logic solver that cannot hallucinate proves whether those facts satisfy each rule. Every TRUE, FALSE, or UNKNOWN is mathematically provable and traced to the specific rule and citation that produced it.

Does Verifiable AI replace human compliance review or speed it up?

It does both. You keep the defensibility of formal review because every verdict rests on an encoded rule and an auditable trace, and you get it at API speed without the human-review bottleneck. As a result, reviewers move from reading every item to handling the UNKNOWN cases the system flags for judgment.

How does Jaxon fit with our existing models and cloud setup?

Jaxon is API-driven and model-agnostic, so it sits on top of whatever language models you already run. It deploys on-premises, in a private cloud, or fully air-gapped, and it works alongside cloud retrieval setups from providers like AWS, Microsoft, Google, or IBM rather than replacing your stack.

Is AI allowed for trade surveillance under FINRA?

Yes. FINRA expects firms to monitor trading activity and detect manipulation, and it does not prohibit AI from assisting that work, but it does expect supervised, documented results. Jaxon verifies each AI surveillance output against the encoded rule. It attaches the evidence and the governing citation to the verdict, so the review rests on a documented check rather than on the model's confidence.

What is model risk management, and does it apply to an exchange?

Model risk management is the practice of validating, documenting, and monitoring the models an organization relies on. The most commonly cited formulations — Federal Reserve SR 11-7 and OCC 2011-12 — bind banks, not exchanges. For a market operator, the same expectation arrives through Reg SCI, which requires SCI systems to be tested and their changes documented, and through FINRA supervision obligations for member firms. Applied to AI, that means showing why an output can be trusted rather than asserting it. Jaxon validates each AI output against a version-controlled ruleset and produces an auditable trace, so validation and monitoring rest on evidence.

What does a DSAIL® verdict include, and what happens when evidence is missing?

Every verdict ships with the rule that fired, the evidence the model extracted, and the governing citation, plus the document version and the ruleset version. When the evidence supports a rule, the verdict is TRUE; when it violates a rule, the verdict is FALSE; and when the document does not contain enough to decide, the verdict is UNKNOWN rather than a confident guess. The solver does not invent a result, so a missing fact surfaces as a flag for review rather than a silent error. Because rulesets are version-controlled, you can reproduce any past decision exactly and show how policy changed over time.

Does Jaxon make our exchange compliant?

No. Jaxon verifies AI outputs against the rules you encode and returns TRUE, FALSE, or UNKNOWN with an audit trail. Compliance remains your determination; Jaxon documents the basis for it. The rules are yours, the sign-off is yours, and the verification record shows the check was performed against a specific ruleset version before anyone acted on the output.

Does DSAIL® become part of our Reg SCI environment?

Reg SCI obligations sit with the SCI entity, not the vendor. DSAIL® deploys inside your perimeter, so it falls under your own capacity management, BC/DR, and change control. Deterministic execution against immutable versioned rulesets makes its behavior reproducible for SCI testing. Where you classify DSAIL® as part of your SCI environment, Jaxon commits contractually to vendor-side support.