Frontier AI on your
most regulated data —
verifiable by your auditor
VeilEngine lets regulated teams — healthcare, finance, legal, and their peers — run Claude, GPT, or Gemini on the data their compliance officers protect. Every request produces a signed receipt your auditor verifies offline, without a vendor in the loop.
Running an operations-heavy business outside the regulated verticals? Start with AI Workflow Engines →
AI systems that produce measurable work — under audit-grade controls
Vertical Edge AI works two ways for regulated mid-market teams: govern the AI that touches your most sensitive data, or move a blocked AI workflow into production. Both are built on the same evidence, approval gates, and auditor-checkable controls.
Run frontier AI on regulated data — provably
For teams under board or regulator pressure. Run Claude, GPT, or Gemini on PHI, MNPI, privileged, or claimant data — with signed receipts your auditor verifies offline.
Govern regulated AI→Automate a high-value workflow — governed
For operators with budget and workflow pain. Put document-heavy finance and operations work — invoice reconciliation, AP/AR, reporting — into production with approval gates and an audit trail.
Automate a workflow→Proof, not positioning: a live production deployment reconciling supplier invoices against 1,100+ active purchase orders at a mid-market distribution operator · a sample evidence package you can verify offline · an open verifier any auditor can run.
Your most valuable AI workflows touch your most regulated data — which is why they stall
Every regulated organization hits the same wall: the highest-value AI workflows touch the most sensitive data, and the compliance officer correctly refuses to send it to an LLM provider. VeilEngine resolves the conflict at the boundary — the data is protected before the request reaches the provider.
Frontier productivity, sensitive workflows
Data is secured before reaching the AI provider. Operators see a side-by-side preview of what the LLM will receive, with a semantic-preservation score, before any request leaves the boundary.
Cryptographic receipts, not narrated logs
Every request emits a signed receipt recording workflow, provider, and protection metadata. Receipts are hash-linked into a per-session chain; your auditor verifies each receipt signature offline with our open verifier. (Automated chain-walk verification and a cross-session transparency log are on the roadmap.) No trust in our platform required.
Claude, GPT, Gemini — switch without losing evidence
Workflows are written against the VeilEngine execution layer, not the provider. Swap from Claude to GPT to Gemini for cost, capability, or geopolitical reasons — the evidence fabric and compliance posture follow the workflow.
Reporting on AI is not the same as protecting it
AI-governance tools have converged on dashboards layered over an unprotected execution path. They record how AI is used; they do not change what data reaches the model. Vertical Edge AI starts at that boundary — sensitive data is protected before the request leaves your environment.
Dashboards over self-attestation
Logs reflect what the system says it did. Compliance officers narrate control posture. Audits depend on platform integrity.
Receipts over narration
Signed receipts at each boundary crossing. Your auditor runs the verifier offline — the evidence holds without trusting our platform.
A governed AI workflow, in production
Not every team starts with a compliance mandate — many start with a workflow that consumes hours of manual effort. Seven production engines compose into the document-heavy work mid-market finance and operations teams run every day: invoice reconciliation and three-way matching, AP/AR, multi-source reporting, vendor onboarding. The agents are the mechanism; the deliverable is governed output that holds up.
Seven engines, one platform
Document intake, approval routing, synthesis, reconciliation, drafting, and deadline tracking — composed into the workflow you actually run, not a generic bot.
Finance and operations first
The beachhead is document-heavy finance work — invoice processing, AP/AR, financial reporting — where the hours saved are measurable and the payback is fast.
Approval gates and an audit trail, by default
Every workflow ships with human approval points, eval criteria, and a record of what ran. Where the data is sensitive, the same protection that powers VeilEngine applies — a human in the loop, not an ungoverned agent.
Built for the five industries where data sensitivity has gated AI adoption
We architect for the five regulated industries where data sensitivity has historically gated frontier-AI adoption. The gateway evidence layer is industry-neutral at launch; vertical evidence packs are compiled per engagement once a regulated workflow and framework scope are selected.
Discharge summaries, prior-auth letters, and clinical-documentation drafting on Epic record exports. Data secured before reaching AI — designed so the provider never sees PHI raw.
Research-analyst AI on earnings, peer comps, and MNPI workflows, designed so MNPI is protected before any provider sees it. Restricted-list cross-checks on protected issuer names; designed for the 17a-4 audit trail.
Claims-fraud detection and adjuster-explainability AI on protected claimant identifiers. Signed, replay-by-claim evidence with engagement-scoped retention for reinsurance arbitration.
Contract review, brief drafting, and document review on privileged content, with the privilege boundary kept inside your perimeter. Counsel-directed discipline preserves work product.
IEP drafting and accommodation suggestions for documented disabilities. Parental-consent settings are designed to govern whether protected interactions train models.
The five are where compliance gates AI first — not the limit of who we serve
Both capabilities are independent of industry. The evidence layer maps to whichever frameworks govern your data — manufacturing, energy, government, and sovereignty workloads included — with the control mapping established during the discovery audit. The automation engines are industry-neutral by construction: our live reference deployment reconciles invoices against purchase orders for an operator in none of the five regulated verticals. The vertical pack is where the five become specific; the capability beneath it is not.
Questions a CISO, CCO, General Counsel — or an operations leader — asks first
Our analysis — and proof you can run yourself
Published analysis on the frameworks and failure modes that decide whether regulated AI reaches production — plus a working evidence package you can verify offline. The point of an evidence layer is that you should not have to take our word for it.
Sending Sensitive Data to ChatGPT, Claude, or Gemini: A US Compliance Analysis
The US legal frameworks that govern sending regulated data to third-party AI — HIPAA, SEC Reg S-P, FERPA, ABA Model Rules, the EU AI Act — and what the boundary actually requires.
The Five Characteristics of AI Workflows That Reach Production
Most enterprise AI pilots never ship, and the reason is rarely the model. The five architecture and governance characteristics that separate the workflows that reach production from the ones that stall.
Where 2026 AI Budgets Land: A Back-Office Automation Demand Map
An evidence-based map of where back-office AI budgets are landing in 2026, where pilots stall, and the characteristics of the work that reaches production.
Three questions you can answer right now — in your browser
Three free tools mirror the first hour of a discovery engagement — where your program stands, what the manual work costs, and which workflow to automate first. Each runs privately in your browser; nothing you enter leaves the page unless you choose to send it.
Where does your regulated-AI program stand?
A ~3-minute self-assessment across the four dimensions an auditor or board would ask about: usage, governance, evidence, and exposure.
Take the readiness assessment→What is the manual workflow costing you?
An annual figure for one back-office workflow — labor, rework, and modeled capacity release — from your inputs, with every formula published.
Run the ROI calculator→Which workflow should you automate first?
Seven questions apply the routing logic a discovery conversation uses — diagnostic first, or one governed workflow straight to production.
Run the fit finder→Engagements begin with a scoped workflow audit — production target within six weeks — or with the ~4-week AI Exposure Map, a diagnostic you keep either way.
The workflow you need to govern or automate
Describe a workflow your compliance officer has blocked, or one consuming significant manual hours. We respond with a preliminary plan — the framework gap and evidence path for governance, or the automation candidates and ROI for a workflow build.