Scoutly AI
Built Scoutly AI to automate the manual cross-referencing of financial, tactical, and statistical data that football recruitment teams rely on. Users submit natural-language queries such as "Find a midfielder under €60K/week," and a coordinator agent built on deepagents and LangGraph routes the request through specialized sub-agents for financial budget extraction, player database search, hallucination verification, statistical analysis, tactical formation fit, email drafting, and PDF report generation. The entire pipeline streams live to the client over SSE, with human-in-the-loop approvals at critical decision points.
Technologies Used
Problem Statement
Football recruitment analysts spend enormous amounts of time manually cross-referencing wage budgets, transfer statistics, tactical fit, and player performance data scattered across disconnected sources. This manual process is slow, error-prone, and makes it difficult to quickly evaluate whether a specific player recommendation is actually financially and tactically viable for a club.
Solution
Scoutly AI collapses this manual workflow into a single natural-language query. A coordinator agent decomposes the request and dispatches it to specialized sub-agents: a Financial agent extracts budget constraints, a Scouter agent searches the player database, a Verifier agent runs anti-hallucination checks and automatically re-scouts if a recommended player name can't be verified, an Analysis agent produces radar charts and scatter plots, a Tactical agent evaluates formation fit, and Email/PDF agents assemble a management-ready report. Hot-reloadable prompts let the system's instructions be tuned without restarting containers, and real-time SSE streaming keeps the entire agent execution transparent to the user.
Key Features
Multi-agent coordinator pipeline (Financial, Scouter, Verifier, Analysis, Tactical, Email, PDF agents)
Anti-hallucination safeguards that automatically re-scout unverified player names
Human-in-the-loop approvals at critical pipeline stages
Real-time SSE streaming of agent execution for full transparency
Rich analytics with radar charts, scatter plots, and tactical evaluations
Automatic email drafting and management-ready PDF report generation
Hot-reloadable prompts for iterating on agent instructions without redeploying
Engineering Challenges
Preventing LLM hallucination of player names and stats within a high-stakes recommendation flow
Coordinating seven specialized agents through a single coherent pipeline without losing context
Balancing autonomous agent execution with meaningful human-in-the-loop checkpoints
Streaming complex multi-agent state changes to the client in real time via SSE
Results & Metrics
Delivered end-to-end automation from natural-language query to vetted PDF scouting report
Eliminated a significant portion of manual cross-referencing work for recruitment analysts
Built a verifiable pipeline where hallucinated player data is caught and corrected automatically
Lessons Learned
Dedicated verifier agents are essential for catching hallucinations before they reach a human decision-maker
Hot-reloadable prompts dramatically speed up iteration on multi-agent behavior
SSE streaming of intermediate agent steps builds meaningfully more user trust than a single final response