ReachOS

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Inspiration

B2B sales teams spend significant time researching potential customers, validating company information, identifying promising leads, and writing personalized outreach emails. Generic messages often lack relevant business context, while manual research makes the process slow and difficult to scale.

We built ReachOS to address this challenge by bringing lead validation, web research, AI-assisted qualification, and personalized outreach preparation into a single workflow. Our vision is to make sales prospecting more research-driven, efficient, and human-centered.

What it does

ReachOS is an AI-powered B2B lead intelligence and outreach platform that helps sales teams turn lead lists into actionable insights.

• Lead import and validation: Import leads from CSV files and check available company and lead information before proceeding.
• Web research: Use Tavily to gather relevant online information about companies and prospects.
• AI-powered qualification: Use NVIDIA Nemotron models through Nebius Token Factory to analyze available lead information and research context, supporting lead scoring and prioritization.
• Personalized outreach: Generate email drafts informed by the available company research and lead details.
• Human-in-the-loop review: Allow users to review and approve or reject outreach drafts before proceeding.
• Centralized workspace: Organize lead information, research results, qualification details, and outreach status in one dashboard.

ReachOS aims to help sales teams spend less time on repetitive preparation and more time evaluating opportunities and building meaningful business relationships.

How we built it

We built ReachOS as a web application with a frontend, backend API, database, and AI-powered research and qualification workflow.

• Frontend: Next.js for the user interface and lead management dashboard.
• Backend: Python and FastAPI for API endpoints and workflow orchestration.
• Database: SQLAlchemy-based persistence, configured for the deployed environment and supporting local development.
• AI reasoning and qualification: NVIDIA Nemotron models accessed through Nebius Token Factory.
• Web research: Tavily for retrieving relevant company and business information.
• Deployment: Vercel for the frontend and Render for the backend, with Docker Compose support for local development.

The workflow connects lead intake with verification, research, AI-assisted analysis, and human-reviewed outreach. This modular architecture allows individual stages to be tested and improved independently.

Challenges we ran into

One of our main challenges was connecting multiple services into a coherent workflow while keeping lead information consistent across the frontend, backend, and database.

We also worked through deployment and configuration issues, including API connectivity, environment variables, CORS settings, Docker Compose configuration, and database persistence.

Another challenge was making AI-generated outreach more useful than generic text generation. Providing research context to the qualification and personalization stages helps the system produce more relevant results, while human review remains important for checking accuracy before outreach.

We also learned that a successful deployment is only one part of a reliable application: configuration consistency, reproducible setup instructions, and clear handling of external-service failures matter just as much.

Accomplishments that we're proud of

We are proud to have developed ReachOS into a deployed, end-to-end lead intelligence application rather than stopping at an isolated AI prototype.

Our key accomplishments include:

• Deploying the frontend and backend so the application can be accessed online.
• Building a CSV-based lead management workflow.
• Integrating web research into lead analysis.
• Connecting AI-assisted qualification with research-informed outreach preparation.
• Implementing a human review step for outreach drafts.
• Supporting local development through Docker Compose.

Most importantly, ReachOS brings several stages of the B2B prospecting process together in one interface, with the goal of making sales research more structured and actionable.

What we learned

We learned that building a useful AI application requires more than choosing a powerful model. The quality of the input data, relevance of retrieved information, reliability of API integrations, and design of the overall workflow all influence the result.

We also learned the importance of human oversight. AI can help summarize information, assess potential relevance, and draft messages, but users still need to verify claims and make final outreach decisions.

Finally, deploying and debugging a multi-service application strengthened our understanding of backend APIs, environment configuration, databases, containerization, and frontend-backend integration.

What's next for ReachOS

Our next goal is to make ReachOS more reliable, measurable, and production-ready.

Planned improvements include:

• Expanding lead verification and improving the quality and traceability of research sources.
• Improving qualification criteria and allowing teams to customize scoring based on their ideal customer profile.
• Strengthening error handling, automated testing, and monitoring.
• Adding clearer evidence and explanations for AI-generated qualification decisions.
• Supporting real email delivery with appropriate safeguards and approval controls.
• Exploring reply classification, follow-up recommendations, and CRM integrations.
• Evaluating the workflow on realistic datasets to measure research quality, qualification usefulness, time saved, and outreach relevance.

Our long-term vision is to evolve ReachOS into a dependable AI sales assistant that combines research, reasoning, and automation while keeping people in control of important decisions.

Built With

• ai
• ai-agents
• artificial-intelligence
• b2b-sales
• business-automation
• fastapi
• generative-ai
• human-in-the-loop
• lead-generation
• lead-intelligence
• lead-scoring
• llm
• natural-language-processing
• nebius-token-factory
• next.js
• nvidia-nemotron
• personalization
• python
• saas
• sales-automation
• sales-intelligence
• tavily
• web-research
• workflow-automation