AI app for it and development · no coding needed
AI agent runtime control and audit console
Reduce the number of separate tools needed to monitor, secure and control AI models and agents in production.
Made for: Platform and security teams running AI models and agents in production
What it does for you
The problem
Model and agent behavior in production is monitored, filtered and secured through several separate tools, so policies, logs and data flows are split across vendors.
What it gives you
Source-linked control decisions and audit records
What you give it
Model callsagent actionspromptsdata sourcespolicy rules
Build your own version of Lakera Guard, Align API and more
One app with what these 7 AI tools do, yours to keep and change: Lakera Guard, Align API, Aegisora, Automorphic, ClawSecure, OpenLIT 2.0, Overseer AI.
Everything these tools do, in one app
- Real-time content filtering Filters or validates AI-generated content as it is produced to catch harmful or unwanted output.Found in Lakera Guard, Overseer AI
- Customizable safety policies Lets users define their own rules for what content or actions are allowed.Found in Lakera Guard, Overseer AI
- Multi-language support Handles content moderation across multiple languages for global use.Found in Lakera Guard
- Analytics and reporting Provides dashboards and reports on moderation or safety activity and trends.Found in Lakera Guard, Overseer AI
- Automated data mapping Automatically aligns and transforms data between different formats and schemas.Found in Align API
- Multiple data source support Connects to databases, cloud storage, and APIs to pull and push data.Found in Align API
- Real-time data synchronization Keeps data consistent across systems as changes happen.Found in Align API
- API documentation and samples Offers extensive documentation and sample code to speed up integration.Found in Align API
- Data validation rules Applies customizable rules to validate and transform data during processing.Found in Align API
- Prompt injection detection Identifies and blocks malicious attempts to manipulate AI models via prompts.Found in Aegisora, Automorphic
- Least-privilege API access Restricts AI agents to only the API permissions they need.Found in Aegisora
- PII masking Detects and hides personally identifiable information in real time during AI operations.Found in Aegisora, Automorphic
- Audit logs Generates readable logs of AI agent activity for compliance and debugging.Found in Aegisora
- Zero-latency proxy Operates as a fast proxy layer that adds no noticeable delay to AI calls.Found in Aegisora
- Few-shot knowledge infusion Adds new knowledge to language models using as few as 10 training samples.Found in Automorphic
- Self-improving models Models improve over time by incorporating human feedback.Found in Automorphic
- Adapter loading and stacking Quickly loads and stacks fine-tuned adapters for efficient model updates.Found in Automorphic
- OpenAI API compatibility Integrates seamlessly with existing OpenAI API-based codebases.Found in Automorphic
- Model sharing hub Provides a platform for sharing and accessing publicly available models.Found in Automorphic
- Pre-install security audit Scans code, dependencies, and behavior before installation to catch risks.Found in ClawSecure
- Runtime monitoring Continuously watches AI agents, permissions, and network activity in real time.Found in ClawSecure
- In-agent security companion Intercepts installations and evaluates components at the point of use.Found in ClawSecure
- Low-latency verification API Provides fast checks during install or automated workflows.Found in ClawSecure
- Dependency vulnerability checks Scans dependencies for CVEs, typosquatting, and unpinned version ranges.Found in ClawSecure
- OpenTelemetry-native tracing Collects traces and metrics using OpenTelemetry for deep observability.Found in OpenLIT 2.0
- Vendor-neutral SDK Allows flexible data routing across multiple GenAI tools without vendor lock-in.Found in OpenLIT 2.0
- Visual debugging tools Provides visual analytics to identify and resolve issues in AI workflows.Found in OpenLIT 2.0
- Prompt versioning Manages and versions prompts to track changes and improve iteration.Found in OpenLIT 2.0
- Interactive model playground Offers a playground to experiment with models and evaluate response quality.Found in OpenLIT 2.0
- Model-agnostic API Works with any AI model or provider through a single API.Found in Overseer AI
- Open-source API with SDKs Provides an open-source API and language-specific SDKs for easy integration.Found in Overseer AI
How it works, step by step
- Filter and validate AI-generated content as it is produced
- Apply customizable safety policies to content and agent actions
- Moderate content across multiple languages
- Report moderation and safety activity in dashboards
- Map and transform data between formats and schemas
- Connect databases, cloud storage and APIs as data sources
- Synchronize data across systems as changes happen
- Publish API documentation and sample code
- Apply validation rules during processing
- Detect and block prompt injection attempts
- Restrict agents to least-privilege API access
- Mask personally identifiable information in real time
- Generate readable audit logs of agent activity
- Run as a low-latency proxy on AI calls
- Add new knowledge to models from few samples
- Improve models from human feedback
- Load and stack fine-tuned adapters
- Keep OpenAI API compatibility
- Share and access models in a hub
- Audit code, dependencies and behavior before install
- Monitor agents, permissions and network activity at runtime
- Intercept and evaluate components at point of use
- Provide a low-latency verification API
- Check dependencies for CVEs, typosquatting and unpinned ranges
- Collect traces and metrics with OpenTelemetry
- Route data across GenAI tools through a vendor-neutral SDK
- Debug AI workflows with visual analytics
- Version and track prompts
- Experiment with models in an interactive playground
- Work with any model or provider through one API
- Provide an open-source API with language SDKs
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for AI agent runtime control and audit console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds AI agent runtime control and audit console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links6 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce the number of separate tools needed to monitor, secure and control AI models and agents in production. For platform and security teams running AI models and agents in production, convert model calls, agent actions, prompts, data sources and policy rules into source-linked control decisions, audit records and reviewed policy updates. The benefit is a testable hypothesis, measured through blocked policy violations per review hour and audit findings closed before release; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect model calls, agent actions, prompts, data sources and policy rules, then follow this sequence: 1. Filter and validate AI-generated content as it is produced. 2. Apply customizable safety policies to content and agent actions. 3. Detect and block prompt injection attempts. Resolve uncertain cases with qualified reviewers, approve source-linked control decisions and audit records, and measure blocked policy violations per review hour and audit findings closed before release against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved model provider set and one deployment environment; final security and compliance decisions remain with the buyer's reviewers. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, policy accuracy and usage permissions. Buyers approve substantive policy changes and enforcement scope. One approved model provider set and one deployment environment; final security and compliance decisions remain with the buyer's reviewers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One approved model provider set and one deployment environment; final security and compliance decisions remain with the buyer's reviewers. Implement one approved input format, a bounded representative case set and the first two task modules: filter and validate AI-generated content as it is produced; apply customizable safety policies to content and agent actions. Support the third module with operator review: detect and block prompt injection attempts. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Buyer-owned model endpoints, agent frameworks, data sources and identity systems. Cloud log storage, ticketing and security information and event management destinations. Start with file exchange and validate destination specifications before promising direct enforcement. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Policy and data-source setup, Live agent and model activity, Review and audit console. Use a service list for connected models, agents and data sources, a central activity timeline with filters, and a right-hand panel for policy rules, source links and reviewer notes. Let users compare policy versions side by side. Display allowed, blocked, flagged and pending-review states. Provide a read-only audit link with comments anchored to the relevant event. Make the task-specific outcome source-linked control decisions and audit records visible beside its evidence, review state and value baseline.
