Localhub · applied AI and governance

Sales agents with governance

The hard part of AI in a product sits around the model call, in permission, cost, context, limits and what happens when the model gets it wrong. That is what I designed and built.

My role: Agent system design and implementation
When: 2026, in production
Outcome: AI the customer trusts, because it has limits, leaves a trail and can be switched off per conversation.

What I did

A per tenant SDR agent, with reactivating the cold base as the core, because that is where the money already is.
An MCP server with 44 tools that exposes the CRM to the customer's own AI, with an explicit boundary on what stays out, such as billing, team and sensitive data.
A governance layer every call goes through, with a permission and cost gate per tenant, a training screen, a governance screen and a record of what the AI did.
An intent engine that scores each lead from 0 to 100 and returns the next action alongside the score.
Anti ban humanization on WhatsApp, with typing delay, a daily volume ramp and a cadence that postpones a send to protect the customer's number.

Each client's team of agents, with the role, the channel it answers on and an on and off switch per agent

The copilot inside the panel, writing the follow-up. It proposes and the person sends, because automatic sending burns the client's phone number

See all work