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.
Bigcircle puts reversible, format-preserving tokenization in front of the provider call, so the model never sees the raw record and the structure of the answer survives. A per-request record says what was encoded, and a leakage suite in CI fails if a raw value can reach a provider. Encode and decode add single-digit milliseconds.
A data boundary encodes sensitive fields before the provider call and restores them after the response, so the provider processes structure without the raw record.
Sending the raw record means the provider holds the identity, while tokenization lets the model keep the structure of the answer without that identity.
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 operations
An institutional fund and portfolio operations platform. It ingests statements and brokerage data from every custodian, normalises and reconciles them automatically, surfaces breaks while they are still small, and keeps the source lineage a property of the data rather than a report assembled afterwards.
Executive operationsIn production
A 24/7 AI chief-of-staff for founders and leadership teams. The leader states the outcome they want; the agent works the sequence across every system the routine touches, checks its own output, and comes back with finished work or the one thing it could not resolve.
SalesIn production
A real-time meeting-intelligence stack for account executives. It notices the call has started, transcribes it on the rep's own machine, guides them while they are still talking, and hands back a structured CRM-ready summary the moment the call ends.
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.