Sales Insights for merchandising. Support Co-pilot for CX. Executive Cockpit for leadership. Built in your tenant, governed by your IT, ready for Azure AD or Okta.
Today
Custom agents. RBAC. Audit trail in your DB.
Q1 2026
Predictive ML — forecasting, churn, anomaly.
Phase 1 is an analysis platform — three role-specific AI analysts running on your data, behind your access controls. Phase 2 stacks predictive ML on top of the same foundation.
Three named agents shipped on day one — not a generic chatbot. Sales Insights for merchandising, Support Co-pilot for CX, Executive Cockpit for leadership. Each one is a Python module: its own system prompt, schema, suggested questions. New agents drop in as new modules.
RBAC built the way your IT already manages access. Users belong to groups; agents are visible to groups; the cockpit shows each user only the agents their groups permit. Maps directly to your Azure AD or Okta group claims when SSO is wired.
Nothing leaves your perimeter. Agents query through a curated semantic layer per app — they cannot write to your data, by design. Every interaction is logged in your own database. LLM providers run under no-retention, no-training contracts.
Designed so your security team's first read is approval, not objection. Every assumption your governance review will check is answered here.
One platform, three sequenced phases. Each one stacks on the access model and audit trail of the one before it. Honest about what runs today and what comes next.
Role-specific AI analysts your teams can use on day one. Built for AlfaMall, deployed in your tenant, governed by your IT.
Predictive ML on top of the same data and access model — a separate data-science workstream, sequenced after the analysis layer is in your team's hands.
Move from "agent answers when asked" to "agent surfaces what matters". Integrations into the systems that execute on the recommendations.
