PathFinder makes complete human experiences searchable.
The internet made information searchable; PathFinder makes human career, builder, and life trajectories queryable. People do not struggle today because information is scarce; they struggle because they cannot easily find and verify the pathways of those who were once in their exact situation.
Current search engines answer "What is this concept?" Q&A sites answer "What do people think?" PathFinder answers "What actually happened to people like me?"
By structuring raw human experience into a verifiable, queryable Life Graph, PathFinder enables developers, founders, and students to trace causality, isolate transition patterns, review proof-backed achievements, and query collective human outcomes using first-principles reasoning.
- Official Landing Page: Visit Website
- Web Client Preview: Launch Web App
- Android Client (APK): Download APK File
- Demo Video (YouTube): Watch the PathFinder Pitch & Walkthrough
- PPT Presentation: View Pitch Deck
- Product Blog Post: Read on dev.to
Every year, thousands of individuals attempt complex life changes: transitioning from service-based agencies to product engineering, scaling bootstrapped startups, dropping out of college to build, or breaking into elite research tracks.
When they search for guidance, they encounter fragmented networks:
- LinkedIn: A self-promotional, high-level highlight reel that hides setbacks, context, and structural failures.
- Reddit & Forums: High-context but highly unstructured threads filled with anonymous opinions rather than verifiable timelines.
- AI Assistants: Rephrase generic corporate blueprints but lack access to localized, authentic, and step-by-step human causality.
PathFinder structures raw narrative into a highly connected Life Graph consisting of nodes representing Education, Job, Decision, Failure, Startup, and Achievement. Each node exposes the deep context, raw challenges faced, immediate outcomes, and emotional checkpoints.
PathFinder doesn't index standalone posts or opinions; it indexes causal connections and entire trajectories.
graph TD
User([User Query])
-->|Voice / Text| QueryParser[Intent Understanding & Embedding Generation]
QueryParser
-->|Hybrid GraphRAG Retrieval| Neo4j[(Neo4j AuraDB)]
Neo4j
-->|Cypher Queries| Traversal[Multi-hop Graph Traversal]
Neo4j
-->|Vector Index| Semantic[Semantic Similarity Search]
Neo4j
-->|Relationship Expansion| Neighborhood[Neighborhood Expansion]
Traversal --> Context[Connected Journey Subgraph]
Semantic --> Context
Neighborhood --> Context
Context
-->|Grounded AI Reasoning| Intelligence[Decision Intelligence]
Intelligence
--> Journeys[Relevant Career Journeys]
Intelligence
--> Patterns[Common Decision Patterns]
Intelligence
--> Insights[Personalized AI Insights]
Journeys --> Response[Response Generation]
Patterns --> Response
Insights --> Response
Response
--> Output[Text / Speech<br/>Preferred Language]
graph TD
User([User Chat])
User
-->|Natural Language Conversation| Chat[AI Conversation Interface]
Chat
-->|LLM Extraction & Structured Parsing| Extraction[Journey Information Extraction]
Extraction
-->|Goals • Experiences • Skills • Timeline • Decisions| Draft[Auto-Generated Journey Draft]
Draft
-->|User Review & Corrections| Forms[Editable Goal & Experience Forms]
Forms
-->|GitHub • PDF • Image Proof Submission| Verify[AI Proof Verification]
Verify
-->|Verified Experiences & Metadata| GraphBuilder[Graph Construction Engine]
GraphBuilder
-->|Create Nodes, Relationships & Timeline| Neo4j[(Neo4j AuraDB)]
Neo4j
-->|Users • Goals • Experiences • Skills • Proofs • Decisions| Journey[Connected Career Knowledge Graph]
Journey
-->|Visual Graph & Timeline| GraphViewer[Interactive Journey Explorer]
PathFinder combines graph storage, AI processing, voice services, and a cross-platform client:
- Purpose: Trajectory Topology & Causal Querying
- Integration: Every journey is modeled with User, Goal, Experience, Skill and Proof nodes connected through
HAS_GOAL,HAS_EXPERIENCE,BUILT_SKILL,CONTRIBUTED_TO,HAS_PROOFandTRANSITION. Outcomes are experience fields; decisions label transition relationships. Neo4j combines graph traversal and vector search to retrieve relevant experiences and related journeys. - Engineering Rationale: Human journeys are linear over time but branch out causally through decisions. Representing decisions and pivots as labeled transition relationships allows the retrieval pipeline to follow those relationships directly. The retrieval pipeline combines vector similarity with graph relationships to find transition patterns.
- Purpose: Voice-to-Text Ingestion & Local Language Translation
- Integration: User voice inputs are converted to text using the Sarvam Saaras STT API with
"translate"mode enabled, allowing regional Indian languages to be parsed directly into English narratives. - Engineering Rationale: People express high-fidelity emotional details, failures, and complex career contexts far more naturally via voice than typing. Sarvam AI ensures that raw regional expressions are captured, translated, and standardized for upstream LLM processing without losing structural nuance.
- Purpose: Structured Information Extraction, Duplicate Prevention, and Session Storage
- Integration:
- Gemini acts as our structured extractor. It parses narratives into strict schemas for Neo4j import.
- Upstash Redis stores onboarding conversation history and drafts with a 24-hour session expiry.
- Engineering Rationale: Parsing irregular, conversational human speech into structured JSON is highly complex. Gemini’s native JSON Schema support allows us to extract dates in standard
MM YYYYformats, categorize emotion labels, and isolate outcomes. Upstash Redis ensures high-performance session state mapping.
- Purpose: Interactive Flowcharts & Cross-Platform Experience Visualization
- Integration: Single TypeScript codebase compiling to Web, iOS, and Android. Integrates a custom Web-fallback alert layer to handle native modal behaviors on the web.
- Engineering Rationale: Users need to review their graphs on the fly. Expo allows us to construct a responsive, rich visual canvas using Cytoscape inside webviews, bridging native haptics (
expo-haptics) and sharing protocols (expo-sharing) cleanly with standard web targets.
Traditional background screening is a slow, centralized, and expensive process. Resumes are highly falsified and LinkedIn profiles are unchecked. PathFinder disrupts this $20B recruitment and background verification market by introducing:
- The "Proof-of-Experience" Graph: Instead of static CV text, candidates present a Neo4j trajectory with verified evidence (e.g. proof-backed outcomes, github logs, verified certifications).
- Targeted Predictive Hiring: Companies query exact transition pathways (e.g., "Show me engineers who have successfully migrated legacy architectures to microservices inside startups"), matching candidates based on real verified outcomes rather than vanity college credentials.
- AI Pathway Simulation: Allow users to model future choices (e.g., "If I pursue a Master's abroad vs. bootstrapping a SaaS, what does the graph suggest my likelihood of reaching 50 LPA is, based on historical user nodes?").
- Decentralized Verification Protocol: Move verification certificates and proof logs onto decentralized chains to issue cryptographically signed, immutable career milestone achievements.
- Automated Mentorship Matching: Connect users whose current Goal matches the exact Achievement node of a senior verified user in the same career vector.
.
├── backend/ # Express.js API, Graph Traversal Engine & AI Processors
│ ├── src/
│ │ ├── ai/ # AI Providers (Gemini, Groq, Sarvam)
│ │ ├── controllers/# Route Request Handlers
│ │ ├── db/ # Seed data & graph schema migrations
│ │ ├── processors/ # Zod validation schemas & JSON extraction processors
│ │ ├── routes/ # API Route Definitions
│ │ └── services/ # Neo4j connections & Upstash Redis integrations
│ └── package.json
│
├── frontend/ # Expo React Native App (iOS, Android, Web)
│ ├── app/ # Expo Router file-based screens
│ ├── api/ # Server endpoints call wrappers
│ ├── components/ # UI Design System & Landing Page modules
│ └── package.json
│
└── docs/ # Deep-dive system documentation
├── architecture.md # Complete system architecture blueprint
├── how-it-works.md # Step-by-step lifecycle flow
└── setup.md # Environment setup & local run guides
Read the following deep-dive manuals to understand, build, and deploy PathFinder:
- Architecture Guide: Explore our Neo4j schema, AI ingestion pipeline, and caching layer design.
- Product Lifecycle Guide: Follow a journey from voice recording to verification and query retrieval.
- Local Setup Manual: Follow the step-by-step instructions to configure variables, migrate databases, and run the monorepo locally.
| Technology | Role | Alternative Considered | Trade-off / Rationale |
|---|---|---|---|
| Neo4j | Graph Storage & Traversal | PostgreSQL with recursive CTEs | Neo4j native index-free adjacency traverses multi-degree relationships in |
| Expo | Mobile & Web Frontend | Native Swift/Kotlin | Expo Router and React Native Web provide a unified codebase, reducing UI divergence. |
| Gemini 3.1 | JSON Trajectory Extraction | Llama-3-70B on Groq | Gemini 3.1 Flash-Lite offers superior structured output validation and larger context windows for long, complex verbal histories. |
| Clerk | JWT Authentication | Custom Express Sessions | Clerk handles token synchronization across React Native Native, Web, and Express middlewares. |
| Upstash Redis | Onboarding Session Storage | Local Redis Instance | Managed Redis stores conversation history and journey drafts with automatic expiry. |
| Cloudinary | Proof Asset Storage | Local File Storage | Cloudinary generates optimized thumbnails and secure CDN delivery URLs for images and PDFs submitted as verification evidence. |
See the current architecture, commit history audit, and maintenance and validation guide.