Tools: 💼 Freelance Rate Negotiation Coach - AI-Powered Salary Intelligence

Submission for the Algolia Agent Studio Challenge ## What I Built ## Key Features in Action: ## Screenshots ## How I Used Algolia Agent Studio ## Data Indexing Strategy ## Retrieval-Augmented Dialogue ## Targeted Prompting Approach ## Why Gemini 2.5 Flash? ## Why Fast Retrieval Matters ## Speed Equals Trust ## Contextual Retrieval > Generic LLM Knowledge ## Real-World Impact ## Technical Stack ## Lessons Learned ## What Worked ## Challenges Overcome ## Future Enhancements ## Why This Matters This is a submission for the Algolia Agent Studio Challenge: Consumer-Facing Conversational Experiences I built the Freelance Rate Negotiation Coach, an AI-powered conversational agent that helps freelancers negotiate fair rates using real market intelligence. The Problem: Freelancers struggle to know their market value. They either undercharge (leaving money on the table) or overcharge (losing clients). Market data is scattered across platforms, outdated, or simply doesn't exist for their specific profile. The Solution: A conversational AI agent that provides instant, data-backed rate recommendations based on: Unlike generic salary calculators, this agent converses with users to understand their exact situation, then delivers personalized insights they can use in actual negotiations. 🔗 Live Demo: https://freelance-rate-coach-d435fez0z-asmaes-projects-79bc55e6.vercel.app/ 📹 Video Demo: https://drive.google.com/drive/folders/1Rp76zF26wSCnQZm4-xKaVktqfudEfIkq?usp=drive_link Example Query 1: "What are React Senior rates in Paris?" Example Query 2: "I need help negotiating my rate" I created a specialized freelance_rates index containing: Each record represents real market data: The agent uses Algolia's faceted search to match user queries against the indexed data: This ensures every recommendation is grounded in real data, not hallucinated by the LLM. I engineered the agent's system prompt to prioritize factual retrieval over conversational fluff: This prompt ensures the agent acts as a data terminal, not a chatty assistant. I chose Gemini 2.5 Flash as the LLM for: In high-stakes negotiations, latency kills confidence. When a freelancer asks "What should I charge?", they need an answer now, not after watching a loading spinner. Algolia's <50ms retrieval transforms the experience: Without Algolia, the LLM would give generic advice: ❌ "Senior developers in Paris typically earn €500-800/day" (vague, outdated) With Algolia Agent Studio: ✅ "Based on 145 React Senior freelancers in Paris: €550-680/day, average €615, trend +15% (data from Q4 2024)" Fast, data-backed insights help freelancers: Salary negotiation is one of the highest-impact conversations a freelancer has. Getting it right can mean €10-20K more per year. This agent democratizes market intelligence that was previously: By combining Algolia's fast retrieval with AI conversation, we create a tool that empowers freelancers to earn what they're worth. Built for the #AlgoliaAgentStudio Challenge 🚀 Tags: #algolia #agentstudio #ai #freelance #webdev #chatbot #machinelearning GitHub: https://github.com/AsamaeS/Freelance-Rate-Negotiation-Coach_algolia-challenge Live Demo: https://freelance-rate-coach-d435fez0z-asmaes-projects-79bc55e6.vercel.app/ Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: Agent Response: Based on 320 freelancers: TJM €550-680, average €615 Market trend: ↑ 15% (growing demand) Remote work options can justify +10-15% premium Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Agent Response: Based on 320 freelancers: TJM €550-680, average €615 Market trend: ↑ 15% (growing demand) Remote work options can justify +10-15% premium CODE_BLOCK: Agent Response: Based on 320 freelancers: TJM €550-680, average €615 Market trend: ↑ 15% (growing demand) Remote work options can justify +10-15% premium CODE_BLOCK: Agent asks clarifying questions: → What's your tech stack? → Your seniority level? → Location preference? Then provides: - Exact market benchmarks for your profile - 2-3 data-driven negotiation arguments - Red flags if proposed rate is >15% below market Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Agent asks clarifying questions: → What's your tech stack? → Your seniority level? → Location preference? Then provides: - Exact market benchmarks for your profile - 2-3 data-driven negotiation arguments - Red flags if proposed rate is >15% below market CODE_BLOCK: Agent asks clarifying questions: → What's your tech stack? → Your seniority level? → Location preference? Then provides: - Exact market benchmarks for your profile - 2-3 data-driven negotiation arguments - Red flags if proposed rate is >15% below market CODE_BLOCK: { "tech_stack": "React", "seniority": "Senior", "location": "Paris", "rate_min": 550, "rate_max": 680, "rate_average": 615, "market_trend": "up", "trend_percentage": 15, "sample_size": 145 } Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: { "tech_stack": "React", "seniority": "Senior", "location": "Paris", "rate_min": 550, "rate_max": 680, "rate_average": 615, "market_trend": "up", "trend_percentage": 15, "sample_size": 145 } CODE_BLOCK: { "tech_stack": "React", "seniority": "Senior", "location": "Paris", "rate_min": 550, "rate_max": 680, "rate_average": 615, "market_trend": "up", "trend_percentage": 15, "sample_size": 145 } CODE_BLOCK: CRITICAL SEARCH RULES: 1. Search Algolia index FIRST before answering 2. NEVER invent rates - only use indexed data 3. Always cite: sample_size, rate range, market trend 4. If no exact match, find closest and explain adjustments 5. Warn users if proposed rate is >15% below market CONVERSATION FLOW: - User provides initial info → Search immediately - Present results: "Based on [N] freelancers: €[min-max], avg €[avg]" - Mention trend: "[X]% up/down/stable" - Give 2-3 negotiation arguments from data Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: CRITICAL SEARCH RULES: 1. Search Algolia index FIRST before answering 2. NEVER invent rates - only use indexed data 3. Always cite: sample_size, rate range, market trend 4. If no exact match, find closest and explain adjustments 5. Warn users if proposed rate is >15% below market CONVERSATION FLOW: - User provides initial info → Search immediately - Present results: "Based on [N] freelancers: €[min-max], avg €[avg]" - Mention trend: "[X]% up/down/stable" - Give 2-3 negotiation arguments from data CODE_BLOCK: CRITICAL SEARCH RULES: 1. Search Algolia index FIRST before answering 2. NEVER invent rates - only use indexed data 3. Always cite: sample_size, rate range, market trend 4. If no exact match, find closest and explain adjustments 5. Warn users if proposed rate is >15% below market CONVERSATION FLOW: - User provides initial info → Search immediately - Present results: "Based on [N] freelancers: €[min-max], avg €[avg]" - Mention trend: "[X]% up/down/stable" - Give 2-3 negotiation arguments from data - ✅ 94+ real freelance benchmarks from the French tech market - ✅ Tech stack, seniority, and location matching - ✅ Market trends (+15% for React, +20% for DevOps, etc.) - ✅ Tactical negotiation arguments grounded in real data - 94 benchmark records covering 20+ tech stacks (React, Python, DevOps, Java, Go, etc.) - Structured attributes: tech_stack, seniority, location, rate_min, rate_max, rate_average, market_trend, trend_percentage, sample_size - Faceted data enabling multi-dimensional filtering (mission type, remote work, company size) - User provides context → "React Senior, 6 years experience, Paris" - Agent translates to Algolia query → tech_stack:"React" AND seniority:"Senior" AND location:"Paris" - Instant retrieval → Relevant benchmarks in <50ms - Contextualized response → "Based on 145 freelancers: €550-680/day, trend +15%" - ✅ Free tier with generous limits (perfect for proof-of-concept) - ✅ Fast response times matching Algolia's speed - ✅ Strong reasoning for multi-turn conversations - ✅ Tool use capabilities for clean Algolia integration - 🚀 Instant credibility: Fast = authoritative - 💡 No "AI thinking" theatrics: Direct data retrieval feels professional - 🎯 Real-time decision support: Users can ask follow-up questions mid-negotiation - Specificity: Exact tech stack match - Recency: Current market trends - Sample size: Statistical confidence - Actionable: User knows exactly where they stand - 📈 Negotiate 15-25% higher rates (backed by market data) - ⚠️ Spot lowball offers (agent flags rates >15% below market) - 💼 Justify premium pricing (specialties like TypeScript add €50-100/day) - 🌍 Understand regional differences (Paris €615 vs Lyon €540 for same profile) - Algolia Agent Studio - Conversational agent orchestration - Gemini 2.5 Flash - LLM for dialogue management - Algolia InstantSearch - Chat widget UI - React + Vite - Frontend framework - Vercel - Deployment platform - Structured data wins: Well-indexed benchmarks > throwing raw data at an LLM - Faceted search is powerful: Algolia's multi-dimensional filtering enables precise matching - Prompt engineering matters: Constraining the agent to "data terminal" mode eliminated hallucinations - Speed builds trust: Sub-50ms retrieval makes the agent feel authoritative - Initial Agent Studio network errors: Solved by switching from OpenAI sandbox to Gemini production key - Query parsing issues: Agent initially struggled with "6 years experience" → Fixed by mapping experience years to seniority levels in prompt - Data quality: Ensuring all 94 records had complete, validated market data - 📊 Visual analytics: Charts showing rate evolution over time - 🌍 Multi-country support: Expand beyond France to EU/US markets - 💬 Negotiation scripts: Generate email templates for rate discussions - 📈 Personalized tracking: Save user profile for trend monitoring - Locked in expensive salary surveys - Scattered across freelance platforms - Outdated by months or years