Give your teams frontier AI.
Keep your knowledge sovereign.
ThreadSync keeps your sensitive knowledge on infrastructure you control — and routes only scrubbed, policy-approved requests out to Claude, GPT, and Gemini. Lock egress by default. Approve what leaves. Log every request for security, legal, and AI-governance review.
For security, AI-governance, and infrastructure teams evaluating controlled model access. No signup required.
For enterprises that need sanctioned Claude, GPT, and Gemini access — without unmanaged data egress, consumer accounts, or unauditable AI workflows.
The enterprise AI problem is not access. It is custody.
Your teams need Claude, GPT, and Gemini. Your security and legal leaders need to know what data left, who approved it, where the answer went, and whether the model quietly became a new shadow repository. Ungoverned access turns private knowledge into unmanaged exposure.
ThreadSync changes the pattern: your knowledge stays inside your boundary, and outside models receive only the minimum approved context — scrubbed and logged — needed to help.
Built for the people who have to sign off.
ThreadSync turns AI governance into operating evidence: controlled egress, approved model access, policy decisions, redaction records, and audit trails your security, legal, procurement, and AI-governance teams can review.
Pass AI security review faster
Show reviewers the actual control path: data boundary, destination allowlist, policy checks, approval rules, retention route, and request-level logging.
Answer audits with records
Export evidence showing who asked, what was scrubbed, which policy applied, where the request went, and how the record was integrity-protected.
Make AI use defensible
Give leadership a governed model for AI adoption: approved tools, accountable usage, controlled data movement, and evidence of enforcement.
Reduce shadow-AI exposure
Offer teams sanctioned access to frontier models without letting sensitive knowledge move through unmanaged consumer accounts or unreviewed tools.
Speed procurement review
Provide the materials enterprise reviewers expect: architecture overview, control mappings, subprocessor list, SSO/RBAC posture, and security packet.
Control model spend
Route usage by policy, model, team, and workflow so AI consumption is visible before it becomes another unmanaged platform cost.
See exactly what leaves, what’s redacted, and what gets logged.
Governance you can see. Here is exactly what happens to one prompt — from your team, out to a frontier model, and into your audit log.
-
The ask
An illustrative, internal-style question — the kind your teams ask every day, with sensitive detail in it.
# Illustrative example — not real customer data Summarize the Acme acquisition memo for the deal team — purchase price $148M, and counsel's risk notes on the Meyer contract.
-
Scrub & policy
The bridge redacts sensitive values, checks the policy for this user and this destination, and requires approval where your rules demand it.
Summarize the ⟪REDACTED:project⟫ acquisition memo — purchase price ⟪REDACTED:$⟫, and counsel's risk notes on ⟪REDACTED:party⟫. policy: allow · destination: zero-retention route · approval: not required -
What crosses
Only the minimum, scrubbed context leaves — routed to an approved model on a zero-retention path.
→ Claude · zero-retention API · org-scoped key · request #a3f9c1
-
The answer returns
The model returns reasoning and drafting. On a zero-retention route, nothing is stored provider-side as your system of record — only approved, scrubbed context ever crossed.
← draft summary + risk table · retained by you, not the provider
-
The record
Every step lands in an integrity-protected audit record your security, legal, and governance teams can export for review.
ts=2026-07-18T14:22:07Z actor=a.ross role=deal-team workflow=acquisition-summary request_id=req_a3f9c1 policy=legal-memo-v4 decision=allow approval=not_required redactions=project,amount,party bytes_out=1.2K provider=anthropic model=claude route=zero-retention destination=approved_api response_retained_by=customer prev_hash=9f2a…c1 record_hash=4e7b…d0 status=ok
This is the whole thesis in one flow: an island, not a silo — everything inside stays connected, governed, and queryable — with a bridge that proves, request by request, that only approved, scrubbed context crosses.
One sovereignty model. Two governed surfaces.
LLM Gateway
A governed bridge to Claude, GPT, and Gemini with org-scoped keys, model allowlists, policy routing, cost visibility, browser-safe sessions, and request-level audit records.
Explore LLM Gateway →Magic Runtime
A governed execution layer for AI-assisted automation: declared inputs, contract-bound runs, default-deny permissions, process isolation, deterministic errors, and hash-chained logs.
Explore Magic Runtime →Lift
Work with our team to stand up your first governed AI workflow — scoped, managed, and in production in weeks.
Security materials your reviewers expect.
Governance is not the pitch — it is the proof. ThreadSync control materials are designed to support enterprise review against common security and AI-governance frameworks. Mappings are provided as review aids, not certifications or legal determinations.
Start with the Trust Center for our security overview, subprocessor list, and procurement packet — or request the full security package for your review.
Start with the boundary that matters most.
Give teams model access without private data leaking into consumer tools — start with LLM Gateway.
Deploy AI-driven automation that can survive a security review — start with Magic Runtime.
Map the complete island-and-bridge model to your environment — request an architecture review.
Build AI without giving away the knowledge that makes it valuable.
We will map ThreadSync to your infrastructure, data boundaries, approval model, and procurement path.