Revisa Express
A six-hour expert review, delivered in fifteen minutes — in production, every day.
Context & Constraints
Academic consulting teams spend a disproportionate amount of time on first-level readings: understanding a client’s paper, diagnosing structural/methodological issues, and assessing quality before deep revision begins.
This initial diagnostic phase was time-consuming, costly, and highly dependent on senior reviewers’ availability. The problem wasn’t writing alone — it was the lack of a fast, structured, and repeatable way to apply academic judgment at scale.
Architecture
A production multi-agent analysis system structured around the 5 pillars of academic writing (Clarity, Methodology, Relevance, Technical Quality, Norms). Each pillar is implemented as a specialized AI agent that produces structured evaluations. A Redactor agent then consolidates all analyses into a single, standardized diagnostic report exported as a branded PDF. Runs in production with Langfuse tracing, Sentry monitoring, and automated health checks.
Revisa Express — multi-agent review pipeline
My Role
Co-founder and sole engineer. Translated tacit academic evaluation criteria into explicit, modular AI agents. Designed, implemented and operate the multi-agent orchestration and backend architecture (Python/Django/CrewAI/PostgreSQL/pgvector/Celery/Docker) — a system my own business depends on daily.
Results & Validation
5 specialized agents mirroring academic evaluation
Automated PDF diagnostic report generation
In production with Langfuse tracing and Sentry monitoring
Reduced analysis time by ~80%. High-quality academic evaluation is not intuition—it is structure. By codifying tacit expert criteria into 5 distinct agents, we proved that complex intellectual critique can be scaled without sacrificing rigor.
Stack
Building something where reliability matters?
I design, ship and operate systems like this one.
Let's talk