Internal AI Assistant

Most internal AI chats we see are a company wiki with a chat box — fine for FAQs, useless for anything that requires crossing two systems. We build an internal agent that acts: reads from your docs, your CRM, your ticketing system, your project tools, and composes an answer from whichever combination is actually needed.

What changed from 2024 to 2026

Three things. MCP standardized tool access, so the agent talks to Notion, Slack, Salesforce, HubSpot, Linear, and Jira through one protocol instead of eight SDKs. Memory became a first-class concern, so the agent remembers that last Tuesday you were digging into the Q2 pipeline and doesn’t ask “what account?” for the fifth time. And observability matured — every tool call logged, every source cited, so when someone asks “where did the agent get that number?” there’s a real answer.

What we build

Unified access over your stack. One agent, every tool. Docs (Notion, Confluence, Google Drive), CRM (Salesforce, HubSpot, Pipedrive), ticketing (Zendesk, Intercom), issue tracking (Linear, Jira), analytics (the warehouse via DataConnect).

Scoped by identity. Each employee’s agent inherits their existing permissions — Okta / Google Workspace / Azure AD — at the MCP layer. Marketing doesn’t see finance. Support doesn’t see HR.

Where your team already works. Slack, Teams, a web app, or embedded into existing internal tools. Same agent, multiple surfaces.

Answers with receipts. Every fact the agent states links to its source: the ticket ID, the Notion page, the row in the warehouse.

Memory that helps. Short-term for the current thread, long-term for recurring facts about teams and projects, opt-out controls for anyone who wants a clean slate.

Why this beats a SaaS copilot

A vendor’s copilot knows your emails and not much else. An internal agent we build knows your schema, your definitions, your escalation rules — and it runs in your infra with your audit trail. The day you swap Salesforce for HubSpot or Claude for GPT, nothing downstream breaks.