Source-linked AI use governance console

For

IT, security and compliance teams in mid-sized organizations whose staff use AI tools

Solves

Staff paste confidential data into AI tools, outputs go unchecked, and no one can show what was sent, what came back or who approved it.

Delivers

Source-linked AI use record with named-owner approval

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 risk and review effort of everyday AI use while keeping a defensible record.

1. Generate text or ideas from user input.
2. Detect and mask personal and confidential data in prompts.
3. Scan AI-generated text for harmful or misleading content.
4. Apply user-defined filters and alert preferences.
5. Track flagged instances and trends in a dashboard.
6. Route prompts to multiple AI models from one interface.
7. Run chats in a privacy mode that blocks training use and exposure.
8. Offer a catalogue of community-built agents.
9. Search social platforms for context.
10. Generate images inside the chat environment.
11. Retain conversation context across chats.
12. Replace real data with fictional values while preserving context.
13. Compare responses from several models and select the best.
14. Redact or process data locally on the device.
15. Provide a browser extension for access.
16. Supply pre-made templates for common content types.
17. Suggest grammar and readability edits as users write.
18. Let multiple agents coordinate through channels and direct messages.
19. Run agents in an isolated virtual machine.
20. Resolve agent conflicts with a leader agent and worktree merges.
21. Let team members collaborate on content in real time.
22. Capture corrections and named-owner approval before consequential use.
23. Export a versioned source-linked AI use record with source references and unresolved questions.

Everything these tools do, in one app

• AI content generation Generates text or ideas automatically from user input.Found in Stableoutput, Serendipity
• Sensitive data redaction Automatically detects and removes or masks personal and confidential information from prompts.Found in ZeroTrusted.ai, AI Eraser
• Real-time content monitoring Continuously scans AI-generated text to flag harmful or misleading content.Found in GPTGuard
• Customizable filters and alerts Lets users set specific filters and alert preferences for content oversight.Found in GPTGuard
• Reporting dashboard Provides a dashboard to track flagged instances and trends over time.Found in GPTGuard
• Multi-model access Allows users to interact with multiple AI models through a single interface.Found in AgentSea, ZeroTrusted.ai
• Privacy protection mode Runs chats in a secure mode that prevents data from entering training pipelines or being exposed.Found in AgentSea, ZeroTrusted.ai
• Agent marketplace Offers a collection of community-built AI agents for various tasks.Found in AgentSea
• Social media search Integrates search across social platforms like X, Reddit, Google, and YouTube.Found in AgentSea
• Image generation Generates images within the chat environment.Found in AgentSea
• Conversation context retention Maintains memory and context across multiple chats.Found in AgentSea
• Data fictionalization Replaces real data with fictional values while preserving context for accurate AI responses.Found in ZeroTrusted.ai
• LLM reliability module Runs queries through multiple LLMs and selects the best response to improve accuracy.Found in ZeroTrusted.ai
• Local data processing Performs redaction or processing entirely on the user's device without sending data externally.Found in AI Eraser
• Browser extension integration Integrates as a browser extension for easy access.Found in AI Eraser
• Template library Provides pre-made templates for different content types like blogs, ads, and emails.Found in Stableoutput
• Real-time editing suggestions Offers suggestions to improve grammar and readability as you write.Found in Stableoutput
• Multi-agent collaboration Enables multiple AI agents to communicate and coordinate tasks via channels and direct messages.Found in Vibespace
• Sandboxed runtime Runs agents in an isolated virtual machine to keep activity separate from the host system.Found in Vibespace
• Conflict resolution Uses a leader agent and worktree strategies to manage merges and resolve conflicts between agents.Found in Vibespace
• Real-time collaboration Allows team members to collaborate in real time on content or projects.Found in Serendipity

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted prompts, model outputs, filter rules and reviewer decisions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted prompts

4. 3

   Model outputs

5. 4

   Filter rules and reviewer decisions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked AI use record with named-owner approval

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for redaction rules, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved model set and filter configuration; final policy and disclosure decisions remain with the compliance owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Policy and filter setup, Live interaction review, Reporting and audit export. Use a queue of flagged interactions, a large central view of the prompt, redacted version, model output and source references, and a right-hand panel for filters, alerts and reviewer comments. Let reviewers compare original and redacted text side by side. Display draft, changes requested and approved states. Provide a client-facing audit link with comments anchored to the relevant interaction. Make the task-specific outcome a source-linked AI use record with named-owner approval visible beside its evidence, review state and value baseline.

Accounts and administration

Organization ownership, model access lists, filter versions, reviewer roles, alert settings, 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

Organization identity providers, model APIs, browser extension endpoints and audit export destinations. Start with file exchange and validate destination specifications before promising direct integration. 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: detect and mask personal and confidential data in prompts; scan AI-generated text for harmful or misleading content. 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

   10 days

   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 IT, security and compliance teams in mid-sized organizations whose staff use AI tools use it to solve "staff paste confidential data into AI tools, outputs go unchecked, and no one can show what was sent, what came back or who approved it"?
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: Reviewed AI interactions per compliance hour and policy incidents after deployment.
4. Measure, then decide. Track reviewed AI interactions per compliance hour and policy incidents after deployment; 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 set and filter configuration; final policy and disclosure decisions remain with the compliance owner. Implement one approved input format, a bounded representative case set and the first two task modules: detect and mask personal and confidential data in prompts; scan AI-generated text for harmful or misleading content. Support the remaining modules with operator review. 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 the source-linked AI use record with named-owner approval. Retain the explicit scope boundary: One approved model set and filter configuration; final policy and disclosure decisions remain with the compliance owner.

What the build depends on. Prompt and output capture, redaction pipeline, asynchronous review jobs, editable version history, reviewer access and tested export formats. High-fidelity governance requires specialist compliance QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved model set and filter configuration; final policy and disclosure decisions remain with the compliance owner.

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 300-1,500 fixed pilot for one defined governance package. Offer a monthly allowance after repeat demand. Quote complex multi-model or agent-runtime work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked AI use record with named-owner approval. 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 risk and review effort of everyday AI use while keeping a defensible record. Demonstrate a concrete source-linked AI use record with named-owner approval using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT, security and compliance 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 AI use record with named-owner approval from a small authorized input set, with a transparent calculation of reviewed AI interactions per compliance hour and policy incidents after deployment and no promised savings.

The first 30 days

1. Week 1: interview five IT, security and compliance teams in mid-sized organizations whose staff use AI tools and inspect a recent example of staff pasting confidential data into AI tools and outputs going unchecked.
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 reviewed AI interactions per compliance hour and policy incidents after deployment, 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: Reviewed AI interactions per compliance hour and policy incidents after deployment. 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

Reviewed AI interactions per compliance hour and policy incidents after deployment; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a source-linked AI use record with named-owner approval. 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 filters, redaction rules and review examples, together with reliable delivery for a narrow governance niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT, security and compliance teams in mid-sized organizations whose staff use AI tools. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

GPTGuard, Stableoutput, AgentSea, ZeroTrusted.ai, AI Eraser, Serendipity and Vibespace, plus manual policy documents and generic AI chat tools. Compare this product with the buyer's present method on reviewed AI interactions per compliance hour and policy incidents after deployment. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the source-linked AI use record with named-owner approval. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data subject rights, source attribution, redaction accuracy and usage permissions. Compliance owners approve policy changes and disclosure scope. One approved model set and filter configuration; final policy and disclosure decisions remain with the compliance owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.