AI for AI® · DSAIL® Verification Engine
AI compliance for financial services — decisions you can prove
AI compliance for financial services now comes down to one question: can you show that every AI output was checked against your own policy before it reached a client, a committee, or an examiner?
Jaxon is the policy-enforcement layer for AI-assisted financial workflows — a deterministic verification engine that returns a PASS, FAIL, or UNKNOWN verdict with an audit trail.
Built for the rules governing financial AI
The rules your examiner will ask about. Jaxon encodes them as enforceable logic — select one to see where it is verified.
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 — were run two ways each: once with DSAIL® enforcing the rules, once with RAG alone. Same models, same inputs. The only variable was DSAIL®.
Tested on SEAD-4 personnel security adjudication against ground truth. Only DSAIL® produced a human-auditable reasoning trace on every determination. Because the architecture is deterministic, any firm can run the same test against its own policy domain and verify the result.
Segments · Financial Services
AI compliance for financial services: five rulebooks, one verification layer
Each answers to different regulators and files different documents. They share one problem: an AI output nobody can prove. Jaxon encodes each rulebook separately and applies the same deterministic check to all of them.
Why probability isn't proof
AI is already inside credit, research, and client communications. 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 RAG 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 extracts atomic facts — does this credit memo exceed the documented sector limit? Deterministic code, your rules compiled by DSAIL® (Domain-Specific AI Language), takes those facts and computes the verdict. The policy logic never lives inside a prompt.
One of three, every time — and each one names the rule that produced it.
The output under comparison
Credit memo · Banking & Private Banking
A memo that breaches a sector limit.
No sector-limit check ran. Nothing to cite.
It stays in proof-of-concept.
Held before it reached a human, a committee, or a regulator.
Credit memos ship, with the exception on record.
Replaces ad-hoc prompt engineering with a version-controlled compliance layer.
How it works
How Jaxon verifies an AI output: output in, verdict out, token logged
Deterministic · signed audit token · auditable trace for every decision
Where the rules come from
For the developer — how you integrate
Define policy rules
Your team authors policy rules in DSAIL®, a domain-specific language that encodes regulatory requirements and firm procedures as formal logic. Rules compile once and deploy as a versioned, auditable artifact.
Call the API at output
After your LLM generates output, send the AI text and policy context to Jaxon's REST endpoint. Any language, any framework, any model. No SDK required, though SDKs are available.
Log & chain of custody
Every response includes a cryptographically signed audit token linking the output, the ruleset version, the result, and the timestamp. Store it in your audit log; provide it to examiners upon request.
Deployment — where must your data stay?
Fastest to production
Most isolated
Constraint
Non-regulated workflows
Constraint
Must stay in our cloud
Constraint
Must stay in our perimeter
Constraint
No external connectivity
Jaxon is listed on AWS Marketplace.
You own
The business owns the rules
Compliance, Legal, Risk, Credit, Supervision, and subject-matter leads author the policy, version-controlled.
CCO · Chief Risk Officer · Head of Fair Lending · Chief Credit Officer · BSA Officer · Head of Model Risk · Counsel
You get
01Consistent decisions
Every determination applies the same encoded rules, with no variance between reviewers, desks, or offices.
02Exception-only workload
Jaxon clears the routine cases; your judgment goes to the ones that need it.
03Audit-ready
The verification record is the documentation examiners and supervisors increasingly expect.
Get in contact
Ready to embed verified AI into your financial workflows?
This is where AI compliance for financial services stops being a policy document and becomes an engineering task. Jaxon's engineering team works directly with your credit, compliance, risk, and technology functions to encode your rules and stand up verification inside your environment.
Your rules. 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 reaches a client, a committee, or an examiner.
Request API access
Tell us the workflow where a wrong output is unacceptable.
Or write directly: info@jaxon.ai
FAQ
Questions financial services and compliance teams ask first.
Does Jaxon make my firm compliant?
No. Jaxon verifies AI outputs against the rules you encode and returns PASS, FAIL, or UNKNOWN with an audit trail. Compliance remains your determination. 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 model risk management framework?
No. Jaxon is the technical substrate that makes the framework documentable. Usage boundaries and violations are logged inside your risk architecture rather than bolted on, and every verification is versioned and reproducible.
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 policy or regulation changes, without retraining any model.
Where can Jaxon be deployed for regulated financial data?
SaaS managed cloud for non-regulated workflows, your own VPC, fully on-premises, or air-gapped. The LLM never sees your policy logic, and Jaxon never holds your data at rest.
More questions on specific rules
How do banks validate AI models used in credit and lending decisions?
Validation has to be independent, documented, and completed before deployment, and the obligation is not transferred by using a vendor model. Jaxon checks each AI-generated decision against your documented underwriting criteria and permissible factors, blocks restricted variables and proxies, and produces a per-decision chain of custody you can hand to an examiner.
Can AI decline a loan application or deny a claim on its own?
The regulatory direction is toward accountability for adverse decisions, with emphasis on disclosure, auditability, and reasons that match the actual basis of the decision. Jaxon supports that posture: ambiguous facts return UNKNOWN rather than a forced pass or fail, and an adverse-action notice whose stated reasons do not match the decision the model actually made returns FAIL before it is issued.
Does Regulation B apply to AI and machine-learning credit models?
Yes, with no exception for model complexity. Reg B’s adverse-action requirements apply to AI and machine-learning models the same way they apply to a human decision, which means the reasons stated in the notice have to match the basis the model actually used. Jaxon enforces documented underwriting factors, blocks restricted variables and proxies, and verifies each notice states reasons consistent with the decision the model made — before the notice is issued.
Can AI draft a SAR narrative?
AI can draft it; the exposure is what happens next. A closed alert that should have escalated, or a SAR narrative that contradicts the underlying transaction data, is a BSA finding with personal-liability stakes for the BSA officer. Jaxon verifies every AI-assisted disposition against your encoded escalation criteria, checks the narrative for consistency with the transaction record, and validates sanctions-screening dispositions against restricted-party lists before close-out.
Does FINRA Rule 2241 apply to AI-generated research?
The rule governs the content, disclosure, and distribution of investment research regardless of what drafted it. The same is true of SEC Regulation AC. Jaxon checks AI-generated research against research independence requirements, blackout periods, and restricted lists, verifies certification statements are present and correct, and flags forward-looking statements that lack cautionary language before distribution.
How do hedge funds and private equity firms document AI use for LPs and auditors?
The documentation an auditor or a limited partner expects is a per-output record: which rule ran, what was checked, what passed, what was flagged, and who reviewed it. Jaxon produces that record for every IC memo, investor report, and trade thesis — verified against the fund mandate, IRR and leverage limits, concentration caps, and fee terms before the output circulates.
Can AI produce a credit rating?
Not a regulated one, and Jaxon does not attempt to. Jaxon verifies that your credit valuations conform to the methodology your firm adopts; it does not issue credit ratings, and “credit valuation” does not connote a rating from a Nationally Recognized Statistical Rating Organization. Your firm owns the methodology, the thresholds, and the judgment. Rating agencies are required to publish their methodologies, which is what makes a published methodology something a firm can adopt, encode, and verify against.