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a modern digital hub for Business Analyst.

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bite-sized guides, frameworks;

real-world case studies.

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global network;

usable resources.

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BA workspace.

Where BAs learn, practice, and ship real work, supported by peers, mentors, and an AI co-pilot tailored to BA workflows.

Business Analysts can co-work with AI Agents within a digital workspace.

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BA Foundations

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Challenges

Scenario: improve unclear requirements.

Scenario: identify missing acceptance criteria.

Scenario: create a user story from a case.

Scenario: detect process gaps.

Scenario: spot risks in a design.

AI-assisted Spaces

Pre-defined templates:

Create Requirement Document

Analyze Current State

Generate Process Map

Clarify Scope

Validate User Story

Multi-agent engines:

Provide context

Agents collaborate

Produce BA outputs

Acceptance testing

Workspace import/export

Communities

Welcome > cohort assignments

Daily / Weekly Challenges

Leaderboards

Peer Review

Events

How it works at a glance

Sense

Connects tasks, docs, meetings, and Slack. Understands priorities, owners, and dependencies.

Guide

Surfaces next best actions, clarifies blockers, and aligns timelines across teams.

Orchestrate

Automates status, nudges handoffs, and keeps everyone on the same page.

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3‑step demo flow

Connect and map

Connect your tasks, docs, and calendar

Auto‑map projects, owners, milestones, and risks

Focus and collaborate

Daily focus view shows next best actions and blockers

Smart handoffs keep work moving across teammates

Review and improve

Friday status compiles progress, decisions, and risks

Insights suggest what to tackle next sprint

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Flow diagram: from chaos to clarity

Ideas/Requests ──► Intake (triage, dedupe)

│

▼

Plan (scope, owners, dates)

│

▼

Execute (AI focus view)

│ ▲

Blockers ◄─┘ │

│ │

▼ │

Handoffs/Dependencies ──┘

│

▼

Review → Insights → Next sprint

User journey (first week)

Day 1: Connect workspace

↓

Auto‑map workstreams

↓

Generate living plan (owners, milestones, dependencies)

↓

Daily focus view + next best actions

↓

Smart handoffs and blocker detection

↓

Friday: one‑click review → status auto‑generated

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A Friday status, without the Friday scramble

AI compiles progress, risks, decisions, and next steps from the week’s activity.

You add the human judgment. Click send.

PRD: Knowledge Visualization

Overview

This feature enables teams to automatically extract, organize, and visualize knowledge from recorded conversations, whether from meetings, chat apps, support systems, or voice transcripts.

The visualization would provide an intuitive understanding of topics, relationships, decisions, and action items, allowing users to discover insights and reduce duplicated communication.

It leverages advanced AI reasoning with structured memory to preserve both the depth (reasoning context) and breadth (coverage of many discussions) of organizational knowledge.

Goals & Objectives

Centralize insights from scattered conversations across multiple platforms.

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Visualize knowledge structure (topics, people, timelines, dependencies) in an interactive map or dashboard.

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Enable knowledge discovery through semantic search, summarization, and relationship exploration.

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Preserve reasoning chains, showing why certain decisions were made, not just what was said.

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Reduce knowledge loss from unstructured communication.

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Problem Statement

Teams would record hundreds of conversations, but the knowledge inside these conversations is fragmented and invisible. Without visualization, it’s difficult to:

Understand what’s already been discussed or decided.

Identify experts or recurring topics.

Reuse existing insights or reference prior reasoning.

The result is duplicated discussions, inconsistent decisions, and slower knowledge transfer across teams.

Target Users

Business Analysts & Product Managers to be able to find past discussions, context or rationales for decisions.

Support / Success Teams to be able to identify patterns in customer issues or feedback patterns.

Developers / Designers / Tech Team Members to be able to revisit feature dependencies & technical reasoning.

Executive / Stakeholders to be able to gain a bird’s-eye view of organizational knowledge flow and decision evolution.

Functional Requirements

Data Collection: Integrate with conversation sources (e.g., meeting recordings, support chat, ticket systems).

Processing Layer: Use NLP to extract entities, topics, decisions, action items, and relationships, storing them as a structured knowledge graph.

High-Reasoning Context Handling: Implement a hybrid architecture combining hierarchical summarization, graph-based retrieval, and selective memory recall to maintain both reasoning depth and wide coverage.

Visualization UI: Interactive graph view (nodes = topics/people, edges = relationships; timeline and cluster modes show discussion evolution.

Search & Filter: Natural-language search, filter by topic, date, source, or participant/project, powered by embeddings and graph traversal.

Knowledge Evolution Tracking: Track how understanding or discussions changed over time across conversations.

Context Reconstruction: When users click a node, reconstruct key reasoning paths and relevant excerpts from conversation history.

Technical Approach: Enabling Long Context with High Reasoning

To overcome traditional RAG limitations (breadth vs. reasoning depth), this system uses a three-tiered reasoning architecture:

Local Layer:

Process individual conversations with entity and topic extraction.

Create short summaries and structured knowledge nodes.

Intermediate Layer:

Aggregate related nodes (e.g., all conversations about “Whisper integration”).

Generate contextual summaries that preserve reasoning links (causal relationships, dependencies).

Global Layer:

Use a reasoning LLM to synthesize across summarized clusters.

Enables deep reasoning over very large organizational knowledge bases.

All layers connect through a graph database, allowing retrieval of both granular facts and high-level narratives.

Risks & Mitigations

Information overload: Introduce summarization and clustering.

Integration complexity: Start with one or two data source(s).

Reasoning Drift / Hallucination: Retain citation links to original conversational; provide confidence scores.

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