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Lab Project

RevFlow AI

RevFlow is a long-running lab project exploring how applied AI, workflow automation, and domain-aware backend architecture can reduce operational friction for modern revenue teams.

Problem

What RevFlow is trying to solve

Revenue teams are rarely blocked by a lack of data. They are blocked by fragmented systems, manual handoffs, inconsistent follow-ups, and the amount of context a person needs to reconstruct before taking the next action. Sales, customer success, finance, and operations teams often move across CRMs, spreadsheets, Slack threads, emails, notes, and internal tools just to understand what happened with an account, which opportunities are stuck, and where revenue is leaking because of delays or missed context.

Product Idea

Workflow intelligence for revenue teams

RevFlow is designed as a workflow intelligence layer for revenue teams. It does not try to replace the CRM. Instead, it organizes scattered revenue signals around accounts, opportunities, tasks, notes, activities, and user actions, then uses AI to help with summarization, prioritization, recommendations, and controlled workflow execution.

Core Workflow

How the system turns context into action

01

Assemble context

Bring together account history, recent activity, open tasks, notes, opportunity state, and user context so the user does not need to search across systems manually.

02

Build workflow-aware AI input

Prepare scoped, domain-aware context for the AI layer instead of sending raw UI prompts or noisy data directly to the model.

03

Generate structured insight

Ask the model for predictable outputs such as summary, risk level, recommended action, reasoning, and confidence rather than free-form text.

04

Validate and store output

Let the backend parse, validate, persist, and explain AI-generated recommendations before they appear inside the user workflow.

05

Human reviews or acts

Keep important business actions reviewable, auditable, and reversible so AI supports the workflow without silently controlling it.

Case Study

High-Level Architecture

RevFlow is structured as a layered application with a frontend workspace, API layer, authentication and user context, domain data layer, workflow orchestration layer, AI reasoning layer, background jobs, integrations, and persistence. The important design choice is that AI does not directly control the system. AI produces structured outputs that the application validates, stores, and turns into controlled workflow state.

  • Frontend workspace for records, workflows, insights, and actions
  • API boundary for validation, auth context, business operations, and persistence
  • Domain layer for accounts, opportunities, contacts, tasks, notes, and activities
  • Workflow layer for states, transitions, recommendations, and follow-ups
  • AI layer for summarization, classification, drafting, risk detection, and reasoning
  • Persistence and audit layer for generated insights and user approvals

Case Study

AI Design Philosophy

The AI layer is treated as a backend service capability, not a UI gimmick. Prompts are controlled by backend workflows, context is assembled deliberately, and outputs are expected in structured shapes that can be validated before being shown to users or used by workflow logic.

  • AI is workflow-aware, not generic chat-first
  • Structured outputs are preferred over free-form responses
  • Backend validation decides what becomes application state
  • Humans stay in control of meaningful business actions
  • Every recommendation should preserve enough reasoning to be trusted

Case Study

Core Modules

The project has been evolving in modules so the architecture can grow gradually instead of becoming a large, brittle automation tool. Each module owns a clear responsibility inside the revenue workflow lifecycle.

  • User and workspace management
  • Revenue entity management
  • Workflow engine
  • AI context builder
  • AI output parser and validator
  • Insight storage
  • Async job processing
  • Audit and traceability

Case Study

Key Engineering Decisions

RevFlow is interesting because it asks how AI can be embedded into operational workflows without making the system unpredictable, opaque, or hard to maintain. That affects the data model, prompts, UI states, workflow transitions, and logging strategy.

  • AI as a service layer rather than a standalone assistant
  • Workflow-first architecture instead of passive dashboards
  • Incremental modules so new workflows can be added over time
  • Reviewable recommendations over blind automation
  • Traceable AI outputs with reasoning, confidence, and historical state