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AI Market Research Platform

Four hours of research, done in ten minutes.

A multi-agent research platform that automates market research end to end — from gathering evidence to producing a polished, structured report — while keeping every insight grounded in retrieved data rather than a model's memory.

Attribution
Founder Experience
Period
2023 – 2024
Category
AI & Automation
Source
View on GitHub (opens in a new tab)
ai-market-research-platform2023 – 2024

4h → 10m

Research workflow automated

01The challenge

Manual market research took analysts four hours or more per report. Generic LLM research tools were no shortcut: they tend to hallucinate or produce shallow, generic output that can't be put in front of enterprise clients.

02The solution

A coordinated team of specialised agents, orchestrated as a stateful LangGraph graph and grounded by a Pinecone retrieval layer. A modular FastAPI backend runs the pipeline; a React application lets teams generate, review and manage reports.

03Key features

  • 01

    Multi-agent pipeline covering planning, evidence gathering, synthesis, critique and reporting

  • 02

    Retrieval-augmented generation on Pinecone with semantic chunking and context ranking

  • 03

    Confidence scoring that rewrites low-confidence queries before anything is generated

  • 04

    Configurable report templates for different research and analysis types

  • 05

    Report history, search and analytics in the React frontend

  • 06

    Secure sign-in with JWT and Google OAuth2

04Engineering approach

How it's built.

  • FastAPI
  • LangGraph
  • LangChain
  • Pinecone
  • Groq
  • LLaMA
  • HuggingFace embeddings
  • React
  • Redis
  • Docker
  1. 01

    Agents are separated by responsibility instead of one long prompt — a Scout plans the research and gathers evidence (including live web data), a Synthesizer structures findings, a Critic reviews them for gaps and weak evidence, and a Reporter writes the final report. Each piece is easier to debug and extend.

  2. 02

    Confidence scoring sits between retrieval and generation: weak context triggers an automatic query rewrite instead of a hallucinated answer.

  3. 03

    The graph uses SQLite-backed LangGraph checkpointing so long-running research jobs can resume reliably.

  4. 04

    The FastAPI backend keeps services and agents separate, validates every request with Pydantic, and uses Redis caching with SQLAlchemy persistence.

05Project gallery

Inside the system.

Architecture views drawn from the project's documented design and its public source code.

System view · architecture from verified project notes

Plan, retrieve, generate — with a check.

The pipeline plans the research task, retrieves grounded context, checks its confidence, and only then writes the report.

  1. 0102

    Research request

    A market question and report template

  2. 0203

    Scout agent

    Plans the research and gathers evidence

  3. 0304

    Pinecone retrieval

    Semantic chunking and vector search

  4. 0405

    Confidence scoring

    Rewrites the query if retrieval is weak

  5. 0506

    Synthesize & critique

    Merges findings, then checks for gaps

  6. 06

    Reporter agent

    Structured report via FastAPI + React

Key design decisions

  • Low-confidence context triggers an automatic query rewrite instead of a hallucinated answer.
  • Single-responsibility agents instead of one long prompt — easier to debug and extend.
  • Checkpointed graph state so long-running research jobs resume rather than restart.
Repository mapgithub.com/arslanrajpot/Research_analyst (opens in a new tab)
backend/
├── agents/        scout · enhanced_scout · synthesizer · critic · reporter
├── routers/       FastAPI route definitions
├── services/      business logic
├── models/        data models
├── db/            database layer
├── tools/         agent tools (web retrieval)
└── main.py        FastAPI entrypoint
frontend/src/
├── components/    UI components
├── context/       auth and research state
├── pages/         dashboard · generator · reports · profile
└── services/      API client

06Outcome

What it delivered.

The automated workflow completes in under ten minutes and was deployed across more than eight enterprise clients during the founder's time at Inovaqo.

Manual research workflow automated
4h → 10m
Enterprise clients the platform was deployed across
8+

As published in the founder's portfolio. No figures on this page are estimates or projections.

07Project attribution

Founder Experience

Engineered by our founder, Arslan Arshad, as a Full-Stack Software Engineer (AI & Data Systems) at Inovaqo. Shown as founder experience — it is not a Code Velocity client engagement. The linked repository is published by the founder.

Sources: Founder portfolio (opens in a new tab) · Repository README (opens in a new tab)