For Gusto: the control plane and system of record under every AI decision you’re accountable for.
Run AI at the speed of Claude, with full integrity, control, and regulatory compliance. Other tools patch AI risk. Tokto operates the layer where AI actually runs.
“We’re literally selling compliance. It is the product… it better be right and your paycheck better come in on time.”
DINA SEGAL · CHIEF LEGAL OFFICER · GUSTOTokto runs as a unified control layer in the path of every AI call. Inspect. Enforce. Record. Each module solves a problem the office of the CLO and CTO already owns.
The immutable record beneath every AI decision Gusto is accountable for.
The control point inside every AI conversation.
Your eyes where your team can’t physically be.
You have excellent policies, a deeply embedded legal & compliance org, Runlayer gating MCP connections, and enterprise agreements with the model providers. None of it fully holds, because “everybody is coding now.” The control surface moved from a few sanctioned apps to every employee with a Claude window and an idea.
The EU AI Act’s high-risk obligations, including record-keeping, become enforceable. The bar is rising on a clock.
of enterprise prompts already contain data that should never be shared. For a regulated platform, each one is a named exposure.
the old pace of building. Your team “physically can’t keep up” and “can’t be in every room all the time.”
Gusto’s moat is not features. It is that a small business can hand you payroll, benefits, 401(k), and the movement of real money, and trust it will be right, on time, and defensible to a regulator. The same Claude workflow that let one person rebuild an incumbent marketing-review tool to 85% in two days is also one where customer PII, privileged analysis, or a regulated decision can leave the building in a single prompt, with no record it happened and no control that could have stopped it.
For a company whose product is compliance, AI governance is not overhead; it is product integrity. The faster Gusto adopts AI, the more the control layer becomes the thing that lets you keep selling trust at all.
Your AI use isn’t horizontal. It sits on top of a regulatory surface with named owners:
The bar isn’t “we use AI responsibly”; it’s “show us exactly how this decision was made.” This is people’s 401(k).
You move billions, and each license carries its own examination and recordkeeping expectations.
SARs, AML, enhanced due diligence, fraud, with teams in the US and India whose volume scales with the business.
Not because anything is wrong, but because normal exams and oversight never stop.
Layer AI onto that and three gaps appear that policy alone cannot close:
Every AI call across every provider: Claude, ChatGPT Enterprise, and the agents multiplying underneath.
The calls that matter, in real time, before something privileged or regulated leaves the building.
After the fact, what actually happened, for a regulator who is asking right now.
Most “AI governance” is a dashboard bolted onto one provider’s enterprise tier, which gives you per-provider blind spots the moment you run Claude and ChatGPT Enterprise side by side. Tokto turns ungoverned, many-to-many sprawl into a single governed control point, without changing how people work.
No single place to see, control, or prove what happened.
One policy. One record. Every call inspected, enforced, and written to an immutable trail on your own infrastructure.
This is the heart of it. Each item below is something you raised on the call, matched to what Tokto does about it.
You made the cost concrete: the difference between Tokto and its absence is “the difference between you and months of work.” When a regulator asks how a decision was made, the honest answer for most AI-using companies today is reconstruction: pulling logs that were never designed to answer the question. Because Tokto’s record is generated at the moment of every call, regulator-readiness becomes a property of the system rather than an effort you mount per inquiry.
| Capability | What it gives the CLO’s office |
|---|---|
| Immutable, on-prem record | Every AI call captured at source; nothing to reconstruct, nothing that left your control to be governed elsewhere. |
| Per-vertical evidence schema | The fields FINRA expects for retirement differ from a state MTL exam; each is captured to its own standard. |
| Policy-fire trail | Not just what was said, but which rule fired and what action followed: the “how was this decided” answer. |
| Query, scope, export | Respond to an inquiry by filtering to regulator, product, person, and window, in minutes. |
| Forward-compatible | Built anticipating record-keeping mandates (e.g. the EU AI Act’s logging direction), so a rising bar doesn’t mean re-architecting. |
We aligned on where this goes: agents, then agents managing agents, then “one person managing a thousand,” with agents becoming buyers too. In that world, human-by-human review is mathematically impossible. The checkpoint doesn’t care whether the caller is a person or the fourth agent in a chain: every layer passes through the same inspection and lands in the same record. The same checkpoint that governs internal use governs the customer-facing surface too: the chatbot that should never hand out advice it isn’t allowed to give.
The execution plane runs the AI interaction inline, on the hot path, adding ~150ms and invisible unless policy intervenes. The record plane proves what happened, asynchronously, in a sealed ledger. The user never feels the control unless your policy says they should.
“Our pipe and our checkpoint between your pipes.” Coexists with Runlayer: Runlayer governs which connections exist; Tokto governs and records what flows through them.
Top-tier encryption; the system of record lives on your servers, and the policy SLM can run on your premises, so even model-assisted governance never moves sensitive content off-site.
Deterministic rules for must-always-behave cases; pluggable compliance modules; and your own verification agents inserted into the inspection path to pause, QA, or escalate.
User, prompt, model, response, policy, decision, timestamp, cost. Every AI interaction at Gusto is stored as a tamper-evident record on your own infrastructure, SIEM-ready and queryable. When a FINRA examiner, a state money-transmission auditor, or your own board asks what your AI actually did, you answer with evidence instead of effort.
Most AI logs were never built to answer a regulator's question. Tokto's record is generated at the moment of every call and sealed so it cannot be altered after the fact, so the artifact a FINRA, state MTL, or BSA/AML examiner wants is created as the work happens, not reconstructed months later.
Every call follows the same path, and the last step is the one a regulator cares about.
A single AI call, captured field by field. Prompts and responses are stored as cryptographic hashes, so the record proves what happened without exposing the sensitive content itself.
Each record carries the hash of the record before it. Change any field in any record and its hash changes, which breaks every link that follows. Tampering is not just discouraged, it is mathematically detectable.
Filter to a regulator, a product, a person, and a window. Fast, indexed search across every field, on your own infrastructure.
The record stores the prompt, response, model, and decision, so you reconstruct and verify the actual interaction without calling the model again. No token cost, and no risk of a different answer the second time, which matters when the thing you are proving is what happened, not what would happen now.
Tag and enrich records after the fact without touching the sealed entry or the runtime decision. The original is preserved; only searchability improves.
Produce an audit package scoped to a single request: the records, the policy that fired, the decision trace, and the hashes that prove integrity.
Audit package · ZIP (JSON + CSV + PDF + hashes)The record is enterprise-owned and lands where your security and audit teams already work.
| Your regulator | What the record gives you |
|---|---|
| FINRA · broker-dealer & retirement | "Show exactly how this 401(k) decision was made" becomes a scoped query plus a replay of the actual interaction, captured to the per-vertical evidence schema an examiner expects. |
| Money transmission · ~40 state licenses | Each license's recordkeeping expectations are answered by a per-state scoped export, generated from the same underlying record. |
| BSA / AML · SARs, EDD, fraud | The trail proves an agent's decision met a junior-analyst-or-better standard, with full attribution and replay for every flagged case. |
| Attorney-client privilege | When a privileged prompt is detected and routed to legal, the record proves the protection fired before anything became discoverable. |
| EU AI Act · Article 12 record-keeping | Immutable logs generated at source and retained on your own servers, built anticipating logging mandates so a rising bar does not mean re-architecting. |
Not a generic template. This is the same control plane, mapped to what matters when your product is compliance, you answer to roughly 40 regulators, and 500,000 businesses trust you to get it right.
Tokto gives Gusto's Chief Legal Officer one record that ties every AI prompt, every model output, and every agent decision to a person, a policy, a product line, and the regulator it answers to, across roughly 40 money-transmission licenses, FINRA retirement, and BSA/AML, before the inquiry, the exam, or the discovery request arrives.
On a single ordinary day, a money-transmission examiner in one state, a FINRA review of a retirement product, and a BSA/AML inquiry all want the same thing: exactly how a decision was made. Your team is building 10 to 100 times faster than before on Claude and ChatGPT Enterprise, everybody is coding now, and you cannot be in every room. The policies are excellent. The record is not there.
Tokto sits inline between your people, your agents, and every model, alongside Runlayer. Every prompt and response routes through a checkpoint in about 150ms, inspected against your policy and written to an immutable, on-prem record. The marketing-review workflow your engineer rebuilt to 85% in Claude in two days keeps its speed; the checkpoint adds the controls, the redaction, and the audit trail. The checkpoint does not care whether the caller is a person or the fourth agent in a chain, so the same inspection covers agents managing agents and the customer-facing surface too.
When a regulator asks how an AI-touched decision was made, the answer is one filtered query, scoped to that regulator, that product, that person, and that window, instead of months of reconstruction. The Chief Legal Officer sees the same view as the CTO, the financial-crime team, and the examiner.
You flagged a recent matter in which a chief executive's AI-generated legal analysis, produced without counsel, was later used against him in litigation. The question it raises for every in-house team is how you recognize when a non-lawyer is using AI for legal work, and protect privilege before the output becomes discoverable. That detection-and-routing rule is one of the cleanest, most defensible controls Tokto offers, and it generalizes across your whole counsel network.
Privilege and unauthorized AI legal use, case reference ↗You have the highest-signal starting point already in hand. Your engineer rebuilt the incumbent marketing-review tool to ~85% in Claude in two days; the remaining work is precisely the controls layer Tokto provides. We’d point that exact workflow through a Tokto checkpoint in a contained pilot: same speed, now with redaction, marketing-compliance rules, and a full audit trail.
You’re building, by hand and under deadline, the thing Tokto is: real-time control plus an evidence trail, without losing the speed that made AI worth adopting. We’d be glad to put the checkpoint in your CTO’s hands to test against the hardest workflow you’ve got.
✉ Reach out to Rob · Rob@tokto.ai