Inspiration
Manual B2B revenue recovery is a slow, error-prone process often plagued by human fatigue and fragmented data. We recognized that account analysts often spend hours manually reviewing invoices and contracts to identify risk. Our inspiration was to build a cohesive, autonomous pipeline that leverages the full power of the Amazon Nova model family to turn raw documentation into real-world recovery actions.
What it does
The Multi-Agent B2B Revenue Recovery & AI Risk Management Suite is an autonomous system that manages the end-to-end lifecycle of revenue at risk.
- Multimodal Data Extraction: It ingests invoices, contract PDFs, usage screenshots, and handwritten notes to extract structured risk signals.
- Deterministic Risk Scoring: The system calculates a precise risk score based on overdue payments, usage drops, and contract proximity.
- Automated Strategy & Execution: It generates bespoke recovery strategies and uses autonomous agents to navigate CRMs and send outreach emails.
- Responsible AI Oversight: Every action is passed through a deterministic "Guardian" gate to ensure safety before execution.
- Executive Reporting: It provides board-level summaries and visual dashboards representing the total revenue recovered.
How we built it
We built the system entirely on the Amazon Nova model family via the Amazon Bedrock runtime. The architecture consists of six specialized agent stages:
- Nova Pro: Powering the Data Agent for multimodal understanding of complex documents like invoices and usage graphs.
- Nova Lite: Used for the Risk, Strategy, and Audit Agents to generate narratives, playbooks, and executive summaries.
- Nova Act: Powering the Execution Agent for autonomous CRM navigation and activity logging.
- Nova Canvas: Utilized to generate visual dashboard elements and momentum indicators for recovery reports.
Technical Logic
The system relies on specific mathematical models for risk assessment and safety:
Risk Score ($s$):
$$s = s_{overdue} + s_{usage} + s_{contract} + s_{delays}$$
Recovery Probability ($p$):
$$p = 0.65 - \delta_{overdue} - \delta_{usage} - \delta_{delays}$$
Guardian Safety Gate ($r$):
$$r = r_{base} + r_{tone} + r_{value} + r_{level} + r_{escalation} + r_{prob}$$
Challenges we ran into
One of the primary challenges was ensuring autonomous execution remained safe and auditable. To solve this, we implemented a deterministic Guardian Gate. This gate uses zero LLM logic for the final verdict, scoring actions based on factors like tone urgency and contract value to decide if an action can be auto-executed or requires human approval. We also had to manage the complexity of multimodal data where downstream agents need structured signals regardless of whether the source was a PDF, a handwritten note, or a database entry.
Accomplishments that we're proud of
- Integrated Nova Trio: Successfully orchestrated four different Nova models (Pro, Lite, Act, Canvas) in a single, unified pipeline.
- Proven Efficiency: The system demonstrated a savings of 2 hours per account for account analysts.
- High-Value Impact: In our test environment, the system managed a pipeline value of $487K with a projected 72.5% success rate for recovery.
- Autonomous Agency: Achieving real-world execution through Nova Act, which can navigate CRMs and log activities without manual intervention.
What we learned
We learned that Nova Pro's multimodal capabilities are robust enough to replace manual data entry even for non-standardized documents like handwritten notes. We also discovered the importance of a "Human-in-the-loop" UI; by routing medium-risk strategies to a dashboard, we can maintain high safety standards while still benefiting from AI-driven strategy generation.
What's next for Multi-Agent B2B Revenue Recovery & AI Risk Management Suite
Our roadmap includes expanding the ecosystem by building native connectors for platforms like Snowflake, Salesforce, and Composio. We also aim to refine the Guardian Gate logic to support more complex enterprise compliance rules and further enhance the Nova Canvas visualizations for real-time streaming dashboards.
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