DealPilotAI

Inspiration

The inspiration for DealPilotAI came from a very real problem: buying second-hand electronics online is stressful. A cheap iPhone, MacBook, or PS5 listing may look like a great deal, but buyers often do not know whether the price is fair, whether the seller is trustworthy, or whether warning signs like “urgent sale”, “no bill”, “advance payment”, or vague descriptions should make them walk away.

Most shopping tools help users find cheaper products. But in second-hand marketplaces, the real question is not only “Which one is cheapest?” It is “Which one is safe enough to trust?”

That gap inspired DealPilotAI: a buyer-side AI agent that helps people make safer, smarter, and more confident purchase decisions before contacting a seller.

What it does

DealPilotAI is an autonomous deal intelligence and negotiation agent for second-hand marketplace purchases.

A user can enter a goal like:

“Find me a used iPhone 14 under INR 45,000.”

The agent then follows a structured workflow:

Intent Understanding → Marketplace Search → Deal Analysis → Scam-Risk Detection → Decision Ranking → Negotiation Strategy

DealPilotAI analyzes listings, estimates deal quality, flags risk signals, ranks safer options, explains why the cheapest listing may not be the best choice, and drafts ethical negotiation messages.

It also provides product-specific buyer safety questions such as battery health, IMEI verification, bill/box availability, warranty status, repair history, and walkaway conditions.

The goal is to make second-hand buying feel less risky and more informed.

How we built it

We built DealPilotAI as a deployed full-stack AI-agent prototype.

The frontend is built with Next.js, TypeScript, and Tailwind CSS to create a clean agent-dashboard experience. The backend is built with FastAPI and Python, using a modular agent pipeline.

The backend contains separate reasoning modules for:

• Buyer intent parsing
• Deal scoring
• Scam-risk detection
• Decision ranking
• Product safety checklist generation
• Ethical negotiation message drafting
• Workflow event tracing

Apify is integrated as the marketplace intelligence layer for structured listing extraction, with credit-safe controls, caching, and fallback mode. Zynd AI is used as the agent identity and discoverability layer through an agent-card and webhook-ready service design. Superplane-style workflow events are used to show how the agent pipeline can be monitored and orchestrated in a production-ready flow. GitHub Copilot helped accelerate frontend, backend, debugging, and documentation work. We designed the system to be safe by default: external calls are disabled unless explicitly enabled, so the demo remains stable and credit-protected.

Challenges we ran into

We wanted DealPilotAI to feel like a real autonomous agent, not just a chatbot. That meant the system needed to do more than generate text. It had to understand intent, analyze listings, detect risk, rank options, explain decisions, and generate useful next actions.

Another challenge was integration.

• Apify for marketplace data intelligence
• Zynd AI for agent identity and discoverability
• Superplane for workflow-readiness and event tracing
• GitHub Copilot for accelerated development

We also had to design credit-safety carefully so the project would not accidentally consume API credits during testing or judging.

Accomplishments that we're proud of

We are proud that DealPilotAI is not just a prompt wrapper. It demonstrates a real agentic workflow: understand, search, analyze, detect risk, rank, and negotiate.

We built and deployed a working prototype with a clear user flow, structured backend agents, sponsor integrations, safety-first design, and a polished dashboard.

One of the strongest parts of the project is the “Why Not Cheapest?” reasoning. The agent does not blindly recommend the lowest-priced listing. Instead, it explains why a slightly more expensive listing may be safer due to better seller credibility, clearer description, lower risk signals, or better condition.

We are also proud of the ethical negotiation approach. DealPilotAI does not manipulate sellers or send messages automatically. It prepares polite, transparent negotiation drafts that help buyers communicate better.

What we learned

We learned that building useful AI agents is not just about connecting an LLM. A real agent needs structure, state, tools, safety checks, fallback behavior, and clear decision logic.

We also learned the importance of explainability. If an AI agent recommends a deal, the user should understand why. DealPilotAI therefore gives deal scores, risk flags, safety advice, negotiation targets, and walkaway conditions.

Another important learning was that integrations should have a clear purpose. Apify, Zynd AI, Superplane, and GitHub Copilot each became part of the product architecture rather than just badges.

Most importantly, we learned that the best AI products solve a specific human problem. DealPilotAI focuses on one clear pain point: helping buyers avoid risky second-hand deals.

What's next for DealPilotAI

Next, we want to make DealPilotAI more powerful and practical.

Future improvements include:

• Saved searches and price-drop alerts
• Seller response tracking
• Adaptive negotiation follow-ups
• Product-specific verification flows for phones, laptops, cameras, vehicles, and rentals
• Buyer risk profiles
• Shareable deal reports for buyers

Long term, DealPilotAI can become a trusted buyer-side agent for second-hand electronics, local commerce, rentals, vehicles, and even B2B procurement.This can also be converted as an saas

The vision is simple: help people buy smarter, avoid risky deals, and negotiate with confidence.

Built With

• apify
• ethical
• fastapi
• github-copilot
• json-based-mock/cached-data
• negotiation
• next.js
• pydantic
• python
• rest-apis
• rule-based-ai-agent-pipeline
• superplane-style-workflow-events
• tailwind-css
• typescript
• vercel
• zynd-ai