AI agent runtime control and audit console

For

Platform and security teams running AI models and agents in production

Solves

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.

Delivers

Source-linked control decisions and audit records

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce the number of separate tools needed to monitor, secure and control AI models and agents in production.

1. Filter and validate AI-generated content as it is produced.
2. Apply customizable safety policies to content and agent actions.
3. Moderate content across multiple languages.
4. Report moderation and safety activity in dashboards.
5. Map and transform data between formats and schemas.
6. Connect databases, cloud storage and APIs as data sources.
7. Synchronize data across systems as changes happen.
8. Publish API documentation and sample code.
9. Apply validation rules during processing.
10. Detect and block prompt injection attempts.
11. Restrict agents to least-privilege API access.
12. Mask personally identifiable information in real time.
13. Generate readable audit logs of agent activity.
14. Run as a low-latency proxy on AI calls.
15. Add new knowledge to models from few samples.
16. Improve models from human feedback.
17. Load and stack fine-tuned adapters.
18. Keep OpenAI API compatibility.
19. Share and access models in a hub.
20. Audit code, dependencies and behavior before install.
21. Monitor agents, permissions and network activity at runtime.
22. Intercept and evaluate components at point of use.
23. Provide a low-latency verification API.
24. Check dependencies for CVEs, typosquatting and unpinned ranges.
25. Collect traces and metrics with OpenTelemetry.
26. Route data across GenAI tools through a vendor-neutral SDK.
27. Debug AI workflows with visual analytics.
28. Version and track prompts.
29. Experiment with models in an interactive playground.
30. Work with any model or provider through one API.
31. Provide an open-source API with language SDKs.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Model calls, agent actions, prompts, data sources and policy rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect model calls

4. 3

   Agent actions

5. 4

   Prompts

6. 5

   Data sources and policy rules

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Source-linked control decisions and audit records

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, connected models and agents, policy versions, reviewer assignments, approval states, usage allowances, retention limits, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: filter and validate AI-generated content as it is produced; apply customizable safety policies to content and agent actions. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will platform and security teams running AI models and agents in production use it to solve "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"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Blocked policy violations per review hour and audit findings closed before release.
4. Measure, then decide. Track blocked policy violations per review hour and audit findings closed before release; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around source-linked control decisions and audit records. Retain the explicit scope boundary: One approved model provider set and one deployment environment; final security and compliance decisions remain with the buyer's reviewers.

What the build depends on. Event upload and preview, asynchronous processing jobs, editable policy history, reviewer access and tested export formats. High-fidelity production requires specialist security QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved model provider set and one deployment environment; final security and compliance decisions remain with the buyer's reviewers.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 500-2,500 fixed pilot for one defined deployment package. Offer a monthly production allowance after repeat demand. Quote complex multi-provider or regulated deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked control decisions and audit records set. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce the number of separate tools needed to monitor, secure and control AI models and agents in production. Demonstrate a concrete source-linked control decisions and audit records set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Platform and security teams running AI models and agents in production professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked control decisions and audit records set from a small authorized input set, with a transparent calculation of blocked policy violations per review hour and audit findings closed before release and no promised savings.

The first 30 days

1. Week 1: interview five platform and security teams running AI models and agents in production and inspect a recent example of 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.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure blocked policy violations per review hour and audit findings closed before release, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Blocked policy violations per review hour and audit findings closed before release. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Blocked policy violations per review hour and audit findings closed before release; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked control decisions and audit records. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved policies, agent configurations and review examples, together with reliable delivery for a narrow platform and security niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for platform and security teams running AI models and agents in production. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Lakera Guard, Align API, Aegisora, Automorphic, ClawSecure, OpenLIT 2.0 and Overseer AI. Compare this product with the buyer's present method on blocked policy violations per review hour and audit findings closed before release. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, trace storage, reviewer hours, integration setup and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked control decisions and audit records. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.