Business judgment on AI products
For teams calling LLMs and MCP tools inside their apps: when a model is about to run a tool call, Conduct receives the request, applies preset rules to allow, block or rewrite it, and returns the result to the caller. What users get is a constrained call result; the rule configuration and delivery format still need verification.
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Start inside the user's day · Public facts + observable behavior · 2026-09-01
Developers building AI agents need to ensure that model calls to external tools comply with security policies.
Currently developers often rely on manual log review or simple custom filtering rules, which is inefficient and error-prone.
AI agents may execute unauthorized tool calls, leading to data breaches or system damage, but effective interception mechanisms are lacking.
The trend is models acting directly on external tools, so an error shifts from a wrong sentence to a corrupted record, making a pre-call interception layer a real need. A wedge is to start with finance or healthcare, where operation trails are mandatory, and sell rule sets plus audit records as compliance deliverables rather than just an open-source library.
The open-source project has gained some attention in the developer community, but no adoption or payment evidence yet.
An English validation note will follow from the public evidence.
Worth dissecting. The open-source project has gained some attention in the developer community, but no adoption or payment evidence yet.
The trend is models acting directly on external tools, so an error shifts from a wrong sentence to a corrupted record, making a pre-call interception layer a real need. A wedge is to start with finance or healthcare, where operation trails are mandatory, and sell rule sets plus audit records as compliance deliverables rather than just an open-source library.
For teams calling LLMs and MCP tools inside their apps: when a model is about to run a tool call, Conduct receives the request, applies preset rules to allow, block or rewrite it, and returns the result to the caller. What users get is a constrained call result; the rule configuration and delivery format still need verification.
The open-source project has gained some attention in the developer community, but no adoption or payment evidence yet.
An English validation note will follow from the public evidence.
The product claims to help users complete: “For teams calling LLMs and MCP tools inside their apps: when a model is about to run a tool call, Co”. User evidence has not yet verified pain intensity or the cost of doing without it.
The assessment is recorded; an English explanation is pending.
The assessment is recorded; an English explanation is pending.
The assessment is recorded; an English explanation is pending.
02
Market comparison
Local supply: Not found in covered sources
Demand evidence: Not yet verified
Public coverage has been recorded for this market. · 2026-09-17
Local supply: Not found in covered sources
Demand evidence: Not yet verified
Public coverage has been recorded for this market. · 2026-09-17
There is no full analysis yet. Start with the direction above.
Public information is limited; this view will update as more evidence appears. It was recently added and does not yet have verifiable usage data.
Full analyses of similar products: deepseek-harness, open-kimi-ppt-skill
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Evidence trail
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Use these searches when the official site is missing or the current link is only a lead.