Teserax - Graph-Based Research Agent

Live Product

Overview

Teserax is a solo-built production SaaS research agent and graph-based thinking tool. The live product turns linear LLM chat into a visual, non-linear exploration space where users can branch into parallel lines of inquiry, merge or summarize findings, and synthesize insights on an interactive canvas.

The current resume describes Teserax as a TypeScript SaaS research agent with multi-step orchestration, tool calling, RAG over live web sources, typed API contracts, async workflows, retry behavior, and failure recovery. The live site metadata reinforces the graph-based interaction model: the product is not just an API wrapper, but a workflow surface for non-linear research and analysis.

Product Walkthrough

Research on a graph

Teserax begins with a normal question, retrieves live sources, and preserves both the evidence and the generated answer as connected nodes. The chat panel remains available for follow-up questions while the canvas exposes how each result relates to the root question and the next suggested investigations.

Turn research into a tunable decision lab

The graph can expand beyond document-like answers into interactive artifacts. In this forecasting example, Teserax decomposes a World Cup prediction into expert analyses, calibration evidence, and a final synthesis, then adds controls that let the user adjust assumptions and rerun the forecast.

Compile instructions into executable workflows

Teserax can also turn natural-language goals into structured workflows. A workflow-control node defines the stages and variables, worker nodes execute focused research or analysis, and their outputs remain available as inspectable artifacts on the graph.

What I Owned

• Designed and shipped the product end to end as a solo project.
• Built the graph-based UX for branching, merging, summarizing, and cross-linking AI responses.
• Implemented multi-step agent orchestration with tool calling and retrieval over live web sources.
• Used Zod-typed API contracts to keep frontend/backend behavior explicit and testable.
• Added async workflow handling, retries, and graceful degradation for long-running AI tasks.
• Deployed and operated the live product at teserax.vercel.app.

Hard Problems Solved

• Research is non-linear: long-form analysis often branches and recombines. Teserax models that directly as a graph instead of forcing every interaction into a single chat transcript.
• Agent steps need structure: tool calling, RAG, retries, and async work require typed boundaries so failures are recoverable rather than confusing.
• Live-source context changes: retrieval over web sources needs graceful degradation when sources are unavailable, slow, or incomplete.
• Solo product ownership: the project required product design, frontend engineering, backend orchestration, deployment, and operational judgment in one system.

Key Features

• Graph-Based Exploration: Branch, merge, summarize, cross-link, and rewrite research paths on an interactive canvas.
• Agentic Research Flow: Multi-step orchestration with tool calling and RAG over live web sources.
• Typed API Contracts: Zod schemas keep request/response behavior explicit across the stack.
• Async Orchestration: Long-running research flows use retries and failure-recovery behavior for graceful degradation.
• Production Deployment: Live SaaS surface deployed publicly and linked from the current resume.

Why It Matters

Teserax is the clearest current proof of solo AI product engineering in the portfolio. It connects an interaction design idea, a graph-based product model, and agentic backend infrastructure into a live system that users can actually try.

Tech Stack

• Frontend: TypeScript, React, graph-based interaction UI
• Backend / AI: FastAPI, tool calling, RAG, async agent orchestration
• Contracts: Zod typed schemas
• Infrastructure: EC2, Cloudflare, Vercel deployment surface