Omnicle

Omnicle

Tech Stack

TypeScript Azure LLMs System Integrations

An enterprise AI automation platform that turns company knowledge into proactive workflows — connecting email, calendars, CRM and databases to automate coordination-heavy work.

Overview

Omnicle is an enterprise AI automation platform built around one idea: automation that thinks ahead. It turns an organisation’s knowledge into intelligent workflows and interfaces that automate routine tasks, connect systems, answer questions, and keep adapting as they learn.

The problem

Organisations of 100+ people are full of coordination-heavy, repetitive, high-stakes work — clustering support tickets, pulling reports together from a dozen sources, comparing vendor proposals line by line. That work eats skilled people’s time and is exactly the kind of process that’s too nuanced for rigid, rule-based automation.

What it does

  • Conversational intelligence — turns institutional knowledge into workflows and interfaces that automate tasks, connect systems and answer questions, adapting as they learn.
  • Workflow design — predefined task sequences triggered by user actions or system events, with interaction patterns that adapt to the input.
  • System integration — connects to email, calendars, Teams, Drive, CRM systems and databases to unify otherwise disconnected data.
  • Proactive automation — monitors incoming data and fires workflows automatically when defined conditions emerge, rather than waiting to be asked.

Where it fits

  • Spotting emerging patterns in high-volume data, e.g. clustering related support tickets
  • Automating report generation that would otherwise be assembled by hand from multiple sources
  • Comparing complex vendor proposals and extracting standardised information
  • Building an institutional knowledge base that stays accessible across the organisation

Delivery

Built on Azure cloud infrastructure and delivered as a working proof-of-concept within two weeks, with no upfront cost — data collection, workflow templating, trigger configuration and output visualisation set up against a real process before any commitment.

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