Negotiator AI

Autonomous AI Procurement & Price Negotiation Agent Built for the OpenAI Hackathon.

Negotiator AI delegates your vendor outreach, price comparisons, and rate negotiations to AI. Describe your service scope or attach a quote; our autonomous agent discovers local service providers via Google Places, places real conversational phone calls with ElevenLabs AI, extracts itemized pricing, and negotiates binding deals using AI leverage strategies.

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The problem

Getting a fair price should not require becoming a part-time procurement specialist. Yet hiring a contractor, buying a laptop, planning a wedding, or sourcing a business service often means chasing vendors, repeating the same brief, comparing vague totals, and hoping the lowest number does not hide the most expensive surprise.

Negotiator AI is a voice-first procurement agent that turns that messy process into one auditable workflow: discover relevant local businesses, call approved contacts, turn spoken answers into comparable quotes, and use verified pricing as negotiation leverage.

What we built

A user can describe a need by voice or upload an existing quote, invoice, PDF, or PNG. Negotiator AI then:

1. Builds one locked brief - captures the requirements that every vendor should receive so the comparison stays fair.
2. Discovers local options - searches Google Places for nearby businesses, including address, rating, maps link, and business phone data.
3. Runs live outbound conversations - uses ElevenLabs Conversational AI and Twilio to contact approved, consented demo participants for a real phone-call experience.
4. Tracks real call outcomes - distinguishes calling, processing a completed call, no answer, a documented decline, and a quote received.
5. Creates evidence-backed quote records - extracts only amounts explicitly spoken in the transcript, flags missing terms, and keeps the transcript beside the resulting quote.
6. Finds negotiation leverage - selects the lowest comparable verified quote and uses it to request a price match or measurable added value from the higher-priced vendor.
7. Produces an executive comparison - presents line items, totals, terms, red flags, and savings in one decision-ready report.

For the live demo, calls are deliberately limited to three verified, consented participants. That keeps the experience safe and testable while showing the same orchestration pattern a production, user-approved vendor-calling workflow would use.

Why Codex and GPT-5.6 mattered

Codex was not a one-time autocomplete tool for this project. It was the engineering partner that let us turn a deceptively simple idea - “call multiple vendors and compare prices” - into a real, failure-aware system.

Using Codex with GPT-5.6, we designed and implemented:

• the Next.js App Router API surface for discovery, call initiation, call status, webhooks, negotiation, and reporting;
• the TypeScript domain model that preserves a locked job brief, call state, transcript, quote, leverage, and audit output;
• the event-driven call lifecycle that reconciles Twilio telephony state with ElevenLabs post-call transcripts;
• durable cross-instance call storage for serverless deployment, so a webhook can update the same call that initiated in a different function instance;
• guardrails that prevent invented prices: quotes only use values stated in the source transcript, and incomplete calls remain declines rather than fabricated estimates;
• the responsive six-vendor discovery and three-call experience, including explicit Not picked up and Processing quote states.

GPT-5.6 was especially valuable for reasoning across these connected constraints: asynchronous webhooks, serverless persistence, telephony failure states, and a UI that must remain honest when a vendor does not answer or a transcript is delayed. Codex accelerated implementation, but every workflow was reviewed, tested, and shaped around the real system behavior.

How we built it

• Product and frontend: Next.js 16, React 19, TypeScript, Tailwind CSS
• Voice and telephony: ElevenLabs Conversational AI and Twilio outbound calling
• Discovery: Google Places API (New Text Search)
• Quote intelligence: structured transcript normalization with evidence constraints
• Reporting: GPT 5.6-generated executive report from the collected quote and negotiation data
• Reliability: Redis-compatible durable state for webhooks and serverless status polling

The hard parts

Voice calls do not end when the UI thinks they do

A phone can ring forever, go unanswered, be cut short, or finish while its transcript is still being processed. We had to model those states separately. The interface now stops pretending every call is active: Twilio status reconciliation surfaces Not picked up and Processing quote, while the transcript webhook supplies the final evidence.

“A price” is not a comparable quote

A vendor saying one number is not enough. We needed scope, fees, validity, binding terms, and exclusions. The system treats absent details as absent details. It does not fill the gap with an AI guess.

Serverless webhooks need durable memory

An outbound call can begin in one Vercel function and finish in another. In-memory state loses that connection. We added durable call records keyed by the internal call ID and provider conversation identifiers so callbacks can recover the right quote workflow.

What we learned

The most exciting lesson was that useful agents are not just chat interfaces with a phone number. They need a trustworthy state machine, evidence boundaries, graceful failure handling, and a product experience that explains uncertainty instead of hiding it.

Negotiator AI is our answer to a practical question: what if everyone had a procurement officer that could do the calling, keep the receipts, and still let the human make the final decision?

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⚡ Workflow & Architecture

flowchart TD
    A[User Brief: Voice / Text / Doc] --> B[AI Intent & Vertical Classification]
    B --> C[Google Places Vendor Discovery]
    C --> D[Autonomous Outbound AI Dialing]
    D --> E[ElevenLabs Post-Call Transcript Webhook]
    E --> F[AI Line-Item Metric Extraction]
    F --> G[AI Price Match Leverage Strategy]
    G --> H[Executive Evidence Audit Report]

1. Intake (/): Voice conversation via InlineVoicePrompt or document upload (/api/intake/parse-document).
2. Autonomous Stream (/agent-run): Orchestrates classification, discovery, outbound dialing, metric extraction, leverage calculation, and report generation in a unified stream.
3. Call Execution (/calls): Manages batch outbound calls, live audio waveforms, and transcript displays.
4. Negotiation (/negotiate): Surfaces calculated price-match leverage strategy, target vs. benchmark quotes, and potential savings.
5. Executive Report (/report): Renders audited quote comparisons, verified binding badges, line-item breakdowns, and PDF/export tools.

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🌐 Routes Overview

Route	Purpose
/	Product landing page with in-place voice assistant & text input.
/agent-run	End-to-end autonomous agent execution stream.
/discover	Vendor discovery results powered by Google Places API.
/calls	Outbound AI call batch manager with live audio spectrum visualizers.
/negotiate	AI price-matching leverage strategy and savings tracker.
/report	Cryptographically verified executive quote audit report.

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🛠️ API Endpoints

Endpoint	Method	Description
/api/verticals/classify	POST	Classifies user prompt into service vertical schemas.
/api/discovery/google-places	POST	Discovers local service providers via Google Places API.
/api/calls/initiate	POST	Starts ElevenLabs outbound AI phone call to vendor.
/api/calls/[id]/status	GET	Returns status, quote, and transcript for an active call.
/api/webhooks/elevenlabs	POST	Webhook for post-call transcripts; extracts itemized quote data.
/api/negotiate	POST	Computes AI price match strategy between quotes.
/api/report/generate	POST	Generates evidence-backed executive audit report.

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💻 Local Setup & Installation

Prerequisites

• Node.js 18.17+
• npm

Installation Steps

1. Clone Repository & Install Dependencies:

   git clone https://github.com/gargieesingh/NegotiatorAI.git
   cd NegotiatorAI
   npm install
   

2. Configure Environment Variables: Copy .env.local.example to .env.local and supply your API keys:

   cp .env.local.example .env.local
   

   Required environment variables:

   ELEVENLABS_API_KEY=your_elevenlabs_api_key
   ELEVENLABS_INTAKE_AGENT_ID=your_intake_agent_id
   NEXT_PUBLIC_ELEVENLABS_INTAKE_AGENT_ID=your_intake_agent_id
   ELEVENLABS_NEGOTIATOR_AGENT_ID=your_negotiator_agent_id
   ELEVENLABS_OUTBOUND_PHONE_NUMBER_ID=your_phone_number_id
   GOOGLE_PLACES_API_KEY=your_google_places_api_key
   OPENAI_API_KEY=your_openai_api_key
   

3. Run Development Server:

   npm run dev
   

   Open http://localhost:3000 in your browser.

4. Production Build:

   npm run build
   

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🧪 Testing Path for Judges

1. Voice / Text Intake: On http://localhost:3000, click Voice Agent to start a real-time voice briefing, or type a request like "I need 3 contractor quotes for AC repair in Lucknow" and click Run Agent.
2. Autonomous Stream: Watch /agent-run classify your vertical, discover local vendors via Google Places, place outbound calls, extract metrics, and generate leverage strategies automatically.
3. Review Results & Reports: Inspect /calls for live audio waveforms & transcripts, /negotiate for price match calculations, and /report for the final executive evidence audit report.

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🛡️ License & Disclaimers

• Verified phone calls placed during demos use consented test numbers.
• Quotes and estimates generated by Negotiator AI are based on transcript data and should be verified directly with vendors before signing binding contracts.