Plenty of AI work stalls at legal review, because the honest answer to what leaves our network is everything. A data boundary fixes that: sensitive fields are encoded before the request and restored after the response, so the provider processes structure without ever holding a name, an account number or an address. It adds single-digit milliseconds and it changes the conversation with compliance.
01 / DATA_BOUNDARY
For teams who cannot send raw records to a provider. Encode on the way out, decode on the way back, so sensitive fields never leave your side.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
Masking destroys the structure a model needs to reason. Tokenization preserves it, so the answer is as good as it would have been on raw data, and the raw data still never leaves.
The encode and decode cost is small enough that it is never the reason a request is slow. That matters, because a boundary teams disable under load is not a boundary.
On FinSight the anonymization work existed so the audit could be passed, not only so the data was safe. The per-request record is what makes that conversation short.
Week 1
Not every field is PII and over-redaction destroys the answer. We work through your data with whoever owns the risk.
Week 1-2
Detection and reversible tokenization in front of the provider call, so structure survives and identity does not cross.
Week 2-3
The answer is restored for your users, complete, with the original values your systems already hold.
Week 3-5
A record of what was redacted per request, and the test suite that shows nothing leaked, for the review that follows.
Fund operationsIn production
An institutional fund and portfolio operations platform. It ingests statements and brokerage data from every custodian, reconciles them automatically, and presents one operational view with the audit and compliance trail regulated finance depends on built in.
Executive operationsIn production
A 24/7 AI chief-of-staff for founders and leadership teams. An agent that plans, acts, and self-corrects across email, calendar, docs, CRM, and ERP through a secure tool layer, runs recurring workflows end to end, and never exposes a credential.
SalesIn production
A real-time meeting-intelligence stack for account executives. It detects a live meeting, transcribes it on-device, guides the rep during the call, and writes a structured, CRM-ready summary the moment it ends, with no audio ever leaving the laptop.
Production-proven
Built by engineers who've already shipped this in production.
Barely, when tokenization is reversible and format-preserving. The model reasons over structure, and structure survives. Lossy masking is what degrades answers.
The ones you and your risk owner decide. We bring a default set for common regimes and then work through your actual data, because over-redaction is its own failure.
Yes, and that is the deliverable that matters. A per-request record of what was encoded, plus a CI suite that fails if a raw value can reach a provider.
Out of scope for this layer, and worth saying plainly. This stops the next request, not the last one; historical exposure is a separate conversation.
Evals, routing, and audit trails for teams whose demo cannot survive real traffic.
View servicePolicy checks around model output and agent actions, for systems where a bad action is expensive.
View serviceModels running locally, for data that cannot leave the machine.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.