Case study

Pecauva

AI financial research workspace for document search, research organization, and faster review workflows.

Audience

Finance teams

Stage

Testing

Role

FullStack AI Engineer

Focus

Search, organization, review speed

Problem

Finance teams need to move through dense documents quickly without losing context. The core challenge for Pecauva is turning document search, research organization, and review speed into one coherent workflow instead of a scattered set of manual steps.

Role

I built the product end to end: the application interface, backend workflows, document pipeline, retrieval layer, matrix experience, artifact generation, and review-oriented product flow.

Research panel
The research ledger turns one question into document-level answers, so teams can compare how each uploaded file responds instead of receiving one blended project-level answer.

Project overview
The dossier view gives finance teams one project-scoped place to move into research, drafts, playbooks, results, and compliance review without losing context.

Project agent
A RAG chatbot answers from the uploaded documents in the active project, cites sources, and keeps the conversation grounded in the research set.

System

Ingest

Financial documents enter a structured workspace where files can be organized by source, type, and review context.

Retrieve

Semantic search and document-aware retrieval help teams find relevant material without manually combing through every file.

Review

A research matrix gives analysts a repeatable way to compare findings, validate answers, and move faster through review.

Produce

Reusable workflows turn research outputs into artifacts such as CSV, PDF, XLSX, and compliance-oriented drafts.

Technical Stack

Next.js, ElysiaJS, PostgreSQL, Qdrant, Redis, object storage, sandboxed analysis infrastructure, and export pipelines for structured research artifacts.

Current Status

Pecauva is still in testing, so this case study focuses on product design, system architecture, and implementation scope rather than public adoption metrics.

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