One gateway for every AI workflow — from debugging a single chatbot to observing a fleet of autonomous agents across multiple providers.
Chatbots, summarizers, and any application making LLM calls. Every request is an opportunity for PII or secrets to leak.
Every chat completion, summary, or AI-generated response sends user context to an external model. That context contains names, emails, account numbers, and sometimes credentials. Without a security layer, sensitive data leaves your infrastructure with every API call — a compliance incident waiting to happen.
Grepture sits in the request path and scans every outbound payload. PII is masked with reversible tokens, secrets are redacted, and prompt injections are blocked — all before the request reaches the model. On the way back, masked tokens are restored so your app delivers complete, personalized responses. The LLM never sees real data.
Autonomous agents with tool access, multi-step workflows, and MCP servers. You can’t predict every request an agent will make — but you can secure the network path.
AI agents call tools, chain LLM requests, and make autonomous decisions. They pull data from databases, call internal APIs, and send context to external models in ways that are hard to predict or audit. Traditional application-level controls can’t keep up with agentic workflows.
The gateway sits on the network path — between the agent and every external service. No matter what the agent does, every outbound request is scanned for PII, secrets, and sensitive patterns. Every inbound response is logged. One security layer covers every tool call, every LLM request, every MCP interaction.
Knowledge bases pulling from internal docs, wikis, and databases. Retrieved chunks often contain data that should never reach an external model.
Retrieval-augmented generation pulls chunks from internal documents, knowledge bases, and databases. These chunks contain secrets (API keys left in docs), personal data (employee info, customer records), and proprietary content. Every retrieved chunk is a potential data leak when sent to an external model.
Grepture scans every chunk in the request payload before it leaves your network. Secrets are blocked, PII is masked, and proprietary patterns trigger alerts. The AI model works with clean context. Your knowledge base stays private.
LLM calls are a black box. No way to inspect what was sent, what came back, or replay issues when something goes wrong.
When your AI feature breaks or produces unexpected results, you have no way to see what prompt was actually sent to the model. Debugging means adding logging, redeploying, and trying to reproduce the issue. Multi-turn conversations and agent loops make it even harder to trace what went wrong.
Grepture captures every request and response in a structured conversation viewer. See the exact prompt sent to the model, diff before/after redaction, and replay any request with one click. Trace multi-turn conversations and agent loops from start to finish.
No visibility into what AI calls actually cost per feature, per endpoint, or per user. Token usage is a black box until the bill arrives.
AI API costs add up fast, but most teams have no way to attribute costs to specific features, endpoints, or users. You see one big bill at the end of the month with no breakdown. Expensive prompts, redundant calls, and inefficient models go undetected until budgets blow up.
Grepture logs token usage for every request with per-model cost estimation. See cost breakdowns by endpoint, by model, and by conversation. Spot expensive prompts, compare model costs, and make data-driven decisions about your AI spending.
Teams using OpenAI, Anthropic, Google, and other providers need unified observability and consistent security. One gateway, one dashboard, one audit trail.
Teams using multiple AI providers end up with fragmented observability and inconsistent security controls. Each integration has different logging, different cost tracking, and different risk exposure. There’s no single place to see all your AI traffic — and no unified view of costs, conversations, or data protection.
Route every model call through one gateway with unified observability, consistent detection rules, and a single dashboard. Same prompt inspection, same cost tracking, same conversation tracing, same security policies — across OpenAI, Anthropic, Google AI, Azure, and any other provider.
Grepture isn’t limited to AI providers. Wrap any outbound HTTP call with grepture.fetch() and apply the same detection rules to webhooks, payment APIs, third-party integrations — anything.
Sensitive data doesn’t only leak through AI calls. Webhooks send customer data to third-party services. Payment integrations pass PII to processors. Analytics platforms receive user context. Every outbound HTTP call is a potential data leak — and most have zero scanning or controls.
Use grepture.fetch() as a drop-in replacement for fetch(). Every outbound request flows through the proxy, scanned against the same detection rules you use for AI traffic. Same PII detection, same secret scanning, same audit trail — for any HTTP call to any external service.
Your employees are pasting sensitive data into ChatGPT, Claude, and other AI tools right now. No proxy, no policy, no audit trail — until now.
Every employee with a browser has access to public AI tools. They paste customer data, internal documents, and code snippets into ChatGPT without thinking twice. IT has no visibility, no control, and no audit trail. Traditional proxies can’t help because the data never flows through your infrastructure.
Grepture Browse is a Chrome extension that detects sensitive data directly in AI chat inputs before it’s sent. It works where the data actually enters the AI — in the browser. PII, secrets, and sensitive patterns are flagged and redacted in real time. No proxy required for basic protection. Connect to the Grepture proxy for unified policies and a complete audit trail.
Drop-in SDK. See your first request in under a minute.
Free for up to 1,000 requests/month · No credit card required
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