Your enterprise
needs an immune system.
Not another firewall.
Compiled converts any described threat into a mathematical antibody that scans every AI action and communication in real time, inside your environment. Sub-100ms. Zero egress. No LLM in the path.
Trusted by security and compliance teams at
The problem
AI is moving faster than governance.
By a lot.
Every approach built for the last decade — rules, signatures, LLM-as-judge — has a structural defect that AI-native threats expose. The tools exist. The architecture is wrong.
LLM-based scanners add 300ms–2s per scan.
That is not a product decision — it is a fundamental architecture choice. If intelligence runs at scan time, latency compounds with every API call, every agent action, every email.
Signature and rule-based systems break on paraphrase.
Adversaries do not repeat themselves verbatim. A behavioral model trained on the shape of intent — not the exact words — generalises. Keyword detectors do not.
Cloud-routed scanners send your communications to a third party.
Regulated industries cannot accept that. Compiled's scanner runs inside your environment. Only a 7KB math function crosses the boundary — inbound, at deploy time.
How it works
Security that compiles your policy.
Not one that reads it at runtime.
Intelligence is frozen into a mathematical object before it ever touches your production traffic. Scanning is arithmetic, not inference.
Write one sentence. We do the rest.
Describe the threat in plain English: "Detect communications that may constitute insider trading." Our pipeline synthesises thousands of behavioural examples, trains a contrastive embedding model, and distils the result into a ~7KB weight vector. No labelled data from you.
- Synthetic data generation via language model
- Contrastive training — 0.981 linear AUC on held-out real data
- Hard-negative style-confound test before any antibody ships
A 7KB function ships in. Nothing ships out.
The compiled antibody is a tiny weight vector. It deploys into your Azure or AWS tenant as a sidecar container. Your messages, documents, and agent outputs never leave your perimeter — they are embedded locally and scored against the vector in memory.
- Azure Container App or AWS Fargate — your tenant, your VNet
- Embedding model runs locally (Azure OpenAI or bge-small air-gap)
- Vector update = replace 7KB file; no redeployment of your stack
Sub-100ms. No language model in the path.
At runtime the scanner is a single matrix multiply: embed the text, dot-product with the antibody vector, threshold. Intelligence lives in the vector, not in a model running live. That is why we are categorically faster than every LLM-based approach.
- Throughput: thousands of messages per second per node
- Score output: 0–1 float + Safe / Flagged / Blocked verdict
- Full audit log inside your tenant; SIEM-ready JSON
Products
One compiler. Two surfaces.
Any described threat.
AI Agent Governance
Every action an AI agent takes — tool call, file write, API request, email send — is scored against your policy antibody in real time. Agents that drift from policy are flagged or blocked before the action completes.
- →Inline action interception — pre-execution scoring
- →Multi-agent orchestration support (LangChain, AutoGen, Semantic Kernel)
- →Policy per agent role, not per rule
- →Audit trail: every decision, every score, immutable log
Communications Surveillance
Email, chat, voice transcripts — every communication is scanned for behavioural patterns that indicate insider trading, market manipulation, or regulatory policy violations. Zero egress. Audit-ready output.
- →SEC, FINRA, FCA, MiFID II regulatory coverage
- →Covers email, Bloomberg, Teams, voice-to-text
- →0 false positives on real Fed/earnings comms in validation
- →Calibrated on your communications distribution before go-live
Proven out-of-the-box — compile once, detect anywhere
The network
Every customer makes every
other customer smarter.
Without sharing a single byte of data.
When a new threat pattern emerges at one institution, the geometry of that threat — its mathematical centroid in embedding space — is aggregated with differential privacy guarantees and shared across the network. Raw messages are never shared. Individual embeddings are never shared. Only the mathematical shape of behaviour crosses tenant boundaries.
What teams are saying
The architecture does the persuading.
We had an LLM-based scanner in pilot for six months. Latency was 800ms average and it still missed paraphrase variants. Compiled went live in a week, runs at 60ms, and the style-confound test gave us the confidence our legal team needed.
The zero-egress architecture was non-negotiable for us. The fact that it deploys into our Azure tenant and the only thing that crosses the boundary is a 7KB weight file was the deciding factor over three other vendors.
We described our insider trading policy in one paragraph. Two days later we had a working detector. That is not an exaggeration. The compile time for a new antibody is measured in hours, not quarters.
Integrations
Deploy into your existing stack.
Compiled installs as a container into your cloud tenant. No new SaaS vendor relationship. No data processor agreement for message content. Available on Azure Marketplace for one-click tenant deployment.
One-click tenant deployment. Runs inside your Azure subscription — VNet-isolated.
Ready to give your enterprise
a behavioral immune system?
A 45-minute call with our team covers your threat model, your architecture constraints, and what a compiled antibody would look like for your specific regulatory context.