Agents that run on race data, not just dashboards
Automating analytical and operational workflows for a global endurance-race operator
2025
Multi-phase delivery
Sports / AI Automation
Starting position
Approx. budget
50,000 – 199,999 €
Budget bucket per Clutch.co project cost category. Exact figures under NDA.
5-phase delivery
Multi-phase
- 1
Phase 1
Workflow inventory + agent scoping with internal team
- 2
Phase 2
Race-data pipeline integration + observability
- 3
Phase 3
Agent framework + first goal-driven agents
- 4
Phase 4
Operator surfaces + supervisor tooling
- 5
Phase 5
Production rollout + supervised iteration
Recurring analytical work that doesn't scale with race volume
As race count, registrations and partner reporting grow, the internal team is forced to repeat the same analytical and operational workflows manually. Traditional BI dashboards answer questions; they don't take actions.
Recurring analytical work consumes scarce in-house analyst time
Operational workflows depend on humans manually orchestrating multiple systems
Race-by-race reporting cycles do not scale with the volume of events
Existing BI tooling visualises but doesn't act
Agentic AI on top of the race data fabric
We designed and built an agentic layer that connects to the race data fabric, runs the recurring analytical and operational workflows autonomously, and surfaces results into the channels where the team already works.
Goal-driven agents
Agents configured around outcomes (a report, a check, an action) rather than imperative scripts.
Race-data integration
Direct connection to the underlying race data pipeline - agents reason over the same source of truth used internally.
Workflow offload
Recurring analytical and operational workflows handled by agents, with humans supervising.
Operator surfaces
Agent output delivered into the channels the team already uses - no separate dashboard to babysit.
How the analytical operating model changed
Analytical and operational work moves from human-orchestrated repetition to supervised autonomous agents working over the same data fabric.
Workflow execution
Before
Humans manually orchestrating multiple systems
After
Goal-driven agents
Configure outcomes once, run the workflow many times.
Tooling posture
Before
BI dashboards (read-only)
After
Agentic action with operator surfaces
Tools that act on insights, not just display them.
Scaling model
Before
Linear with analyst hours
After
Decoupled from analyst availability
Race volume can grow without growing the analyst team.
Operating mode
Before
Ad-hoc analyst requests
After
Supervised autonomous workflows
Analysts supervise instead of execute.
Agentic system operational
The agentic layer is operational and running recurring workflows against live race data. Customer-specific outcomes and savings figures are confidential.
Architecture
Deployment