Modern teams generate an enormous amount of data every day. A single project might have task updates in Asana, design feedback in Slack, stakeholder requests in email, and verbal commitments from a Zoom call. Each source tells part of the story, but nobody has time to manually piece it all together.

This is where AI synthesis comes in — the process of automatically collecting, normalizing, and correlating data from multiple sources to create a unified view of what's happening across your team.

The three-layer pipeline

Task Force processes information through three distinct layers:

1. Data ingestion — raw data flows in from connected integrations (Gmail, Slack, Asana, Zoom, Granola, Fireflies) and is stored as-is

2. Canonical normalization — each piece of data is transformed into a standardized record format, regardless of where it came from. A Slack message about a deadline becomes the same data structure as an Asana due date change

3. AI analysis — the normalized records are analyzed to detect patterns, conflicts, and opportunities. The output is a set of microtasks — specific, actionable recommendations

Conflict detection: when sources disagree

One of the most powerful features of multi-source synthesis is conflict detection. Imagine this scenario: a project manager sets a deadline in Asana for Friday, but in a Slack thread, the developer says they need until next Wednesday. In a traditional setup, this conflict goes unnoticed until someone misses the deadline.

Task Force catches these conflicts automatically. When data from different sources contradicts, the system flags it with a "conflict evidence" tag and raises the risk level to HIGH. The manager sees it immediately in their priority queue and can resolve the misalignment before it becomes a problem.

Evidence-based confidence

Not all recommendations are created equal. A microtask backed by data from 4 different sources (an Asana task, a Slack conversation, an email thread, and a meeting transcript) has much higher confidence than one based on a single Slack message.

Task Force calculates a confidence score for every recommendation (0–100%) and displays it prominently. High confidence (≥85%) means multiple sources agree. Low confidence (<60%) means the system is less certain and the manager should review the evidence before acting.

This isn't just AI making decisions — it's AI providing a well-researched brief so managers can make better decisions, faster.