Profit Pilot

ProfitPilot AI is an intelligent, multi-agent assistant that empowers small and medium-sized businesses with data-driven insights and actionable recommendations.

Comment 1

Inspiration

Small and medium-sized businesses (SMBs) often struggle to leverage their data effectively, missing opportunities to optimize profits, manage inventory, and understand their competitive landscape. Many lack the resources for dedicated data analysis teams or expensive software.

We were inspired to create ProfitPilot AI to democratize access to advanced business intelligence, providing an intelligent, affordable, and easy-to-use solution that empowers SMB owners with actionable, data-driven insights. Our goal was to build a system that acts as a virtual business analyst, making sophisticated analysis accessible to everyone.

What It Does

ProfitPilot AI is a multi-agent AI assistant designed to optimize business operations for SMBs. It provides comprehensive insights across various aspects:

  • Intelligent Onboarding & Data Seeding: Streamlines the initial setup process, helping businesses register and generating realistic sample sales and inventory data for new users or demos.
  • Dynamic Business Analytics: Analyzes sales trends, monitors inventory, and provides pricing advice based on the business’s actual data.
  • Competitive Intelligence: Integrates with Google Maps to discover nearby competitors and analyze their public reviews for market insights.
  • Centralized Orchestration: A Root Agent interprets queries, manages context, and delegates tasks to specialized sub-agents for a seamless experience.

In essence, ProfitPilot AI acts as a smart, always-on business consultant, providing personalized recommendations to enhance profitability and operational efficiency.

How We Built It

We built ProfitPilot AI on a robust, scalable Google Cloud ecosystem, leveraging powerful AI and data services:

  • Google Gemini 1.5 Flash API: Core intelligence for NLP, data analysis, insight generation, and interaction. Used for tasks like review sentiment analysis and simulated data generation.
  • Google BigQuery: Central data warehouse for sales, inventory, and business profiles.
  • Google Maps Platform / Places API: For real-time competitor discovery and public review data.
  • Python: Primary language, using libraries like google-cloud-bigquery, google-generativeai, googlemaps, and Flask.

Agentic Architecture

The system uses a Root Agent to orchestrate specialized sub-agents (Onboarding, Comparison, Business Analyst), each equipped with tools for BigQuery, Google Maps, and Gemini.

Challenges We Ran Into

  1. Effective Data Transformation for LLMs: Converting structured data into a format Gemini could understand to yield accurate insights.
  2. Maintaining Context Across Agents: Managing conversation state and passing business_id consistently across agent interactions.
  3. Integrating External APIs Reliably: Handling errors, rate limits, and varied responses from the Google Maps Places API.
  4. Simulating Realistic Data: Using Gemini to generate credible, structured data for sales and inventory.
  5. Prompt Engineering for Diverse Tasks: Iteratively refining prompts for different agents and tasks to prevent hallucinations and ensure clarity.

Accomplishments That We're Proud Of

  • Robust Multi-Agent Architecture: Seamless collaboration between agents for handling complex queries.
  • Comprehensive Business Intelligence: Covers internal data (sales, pricing, inventory) and external insights (competitors, reviews).
  • Intuitive User Experience: Root Agent enables a smooth, user-friendly interaction.
  • Effective Gemini Integration: Used for both structured and unstructured data analysis.
  • Real-world Applicability: Valuable to SMBs and easy to demonstrate via data simulation.

What We Learned

  • Power of Agentic Architectures: Modular design improved focus, maintainability, and scalability.
  • Optimizing LLM Interaction with Structured Data: Structured input is key to high-quality output from LLMs.
  • Importance of Context Management: Consistency in identifiers across agents was critical for effective multi-turn interactions.
  • Leveraging External APIs: Enriched insights through Google Maps data.
  • Iterative Prompt Engineering: Prompt design was a continuous and crucial process.

What's Next for ProfitPilot

  • Integration with More Data Sources: Connect to platforms like Shopify and QuickBooks for automatic data ingestion.
  • Proactive Alerting and Recommendations: Identify and notify users of trends or issues (e.g., low stock, negative reviews).
  • Predictive Analytics: Forecast sales, inventory needs, and market shifts for smarter planning.
  • Customizable Reporting & Dashboards: Develop user-facing dashboards with visual KPIs and insights.
  • Voice Interface Integration: Enable hands-free interaction via voice assistants.

Built With

Share this project:

Updates