We built Parse because agents that touch the world need governance.

The problem we saw

AI agents are moving from demos to production. They read files, execute code, make API calls, query databases, and interact with users. Every one of those interactions is a trust boundary — a point where untrusted text can steer the agent toward actions its developers never intended.

Prompt injection is the structural vulnerability that makes this dangerous. An LLM cannot reliably distinguish between its developer's instructions and data it processes. When an attacker hides instructions in a webpage, an email, or a tool response, the model treats them as commands. For an agent with tool access, that means credential exfiltration, unauthorized code execution, or data theft.

We looked at the existing solutions and saw a gap: tools built for enterprise SOC teams, not for the developers and agencies actually building and deploying agents. No transparent pricing. No compliance mapping. No self-serve path. No implementation support.

What we built

Parse is an API-first governance layer designed for the people building AI agents. Screening is the enforcement mechanism: three endpoints cover the three trust boundaries in any agent system, and around them sit the registry, policy surface, and evidence trail.

• Input screening — POST /v1/parse screens untrusted text before it reaches the agent's LLM
• Output screening — POST /v1/screen-output checks generated output before it reaches users, tools, or memory
• Agent trust verification — POST /v1/agent/trust/verify validates identity and intent before accepting delegation from another agent

Each endpoint runs a multi-layer detection pipeline: deterministic pattern matching with text normalization, structural risk analysis for encoded and hidden payloads, LLM semantic analysis, and optional sandbox execution. Every decision produces a structured result with risk categories, a score, and a recommended action.

How we think about security

We believe security tools should be honest about what they do and do not prevent. Parse reduces prompt injection risk. It does not eliminate it. We publish our limitations publicly because false confidence is more dangerous than documented gaps.

Responsible AI

Human-in-the-loop is not a feature flag — it is the default. When Parse encounters an ambiguous case, it returns request_owner_approval instead of guessing. The human operator always has the final say on whether to proceed, block, or investigate further. Screening narrows the decision space; it does not make the decision.

AI should facilitate human creativity, not replace human judgment. Screening is a guardrail that lets teams move faster with confidence — the same way a seatbelt lets you drive at highway speeds. It catches the cases where untrusted text is trying to steer an agent off course, so the humans building and operating agents can focus on the work that actually requires their judgment.

Transparency is a value, not a marketing tactic. Our /llms.txt file includes a What Not To Claim section that documents what Parse does not prevent. We publish our limitations, our detection gaps, and our security posture openly — because a security tool that hides its blind spots is more dangerous than one that names them.

Who we serve

Parse is built for the people doing the work:

• AI agencies and consultancies deploying custom agents for enterprise clients and needing compliance evidence to pass security review
• Engineering teams building autonomous agents that process untrusted data from the web, documents, email, or tool APIs
• Independent developers who want screening and receipts without enterprise sales friction or vendor lock-in
• Security teams evaluating agent deployments and needing audit evidence, policy enforcement, and risk visibility
• Design systems teams building AI-assisted tooling use Parse to screen contractor documentation and external content before it reaches their component-generation agents