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Improvado connects Strava activity data to AI agents via MCP. Query training volume, performance trends, segment PRs, and athlete comparisons in plain English. For coaches managing multiple athletes or analysts tracking performance at scale.
Stop digging through Strava's UI to find performance trends. Your AI agent pulls activity data, heart rate zones, elevation, power metrics, and segment performance across any time range. One question gets you what used to take twenty clicks.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Create training notes, update athlete profiles, and log manual activities through your AI agent. Routine data entry that clutters coaching workflows happens in seconds.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Set automated watches on training load, consistency, and performance markers. Know when an athlete is trending toward overtraining or when a training block is producing results.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Create training notes, update athlete profiles, and log manual activities through your AI agent. Routine data entry that clutters coaching workflows happens in seconds.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
A coach managing 20 athletes has to open each athlete's profile individually to check recent activity, assess load, and review session quality. A full roster check takes over an hour every morning. By the time you've finished, the first athlete has already trained again.
Ask your AI agent for a consolidated morning dashboard across the entire roster. It pulls recent activities, flags anomalies in training load, and surfaces athletes who missed sessions — all in a single query covering every athlete simultaneously.
You want to know whether higher weekly volume improved race times, or whether speed sessions are correlated with injury. The data is in Strava, but connecting the dots requires exporting CSVs, building a spreadsheet, and running correlations manually. Most coaches never do it.
Your AI agent queries Strava data and runs the analysis in natural language. Ask about correlations between training variables and outcomes. The MCP server pulls historical activity data and your AI agent does the analysis inline.
An athlete's training load spikes three weeks before a key race. There's no automated alert. The coach sees it in the weekly review — but by then the athlete is already fatigued and the race window is compromised. The data was always there; no one was watching it.
Configure your AI agent to monitor training load ratios and alert proactively. It tracks acute vs. chronic load, flags spikes, and can generate automated recovery recommendations — all based on live Strava data without manual weekly audits.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
All activity data (runs, rides, swims, and other sport types) including GPS data, heart rate, power, pace, elevation, splits, and segment times. Also athlete profile data, gear, kudos, and club information. Both individual athlete data and, for coaches, roster-level aggregations.
Yes, this is one of the primary use cases. With appropriate API access configured for each athlete, your AI agent can query across your entire coaching roster simultaneously — comparing loads, flagging issues, and generating summaries for all athletes in one prompt.
Strava's native analysis covers individual activities and basic trends within their UI. The MCP connection lets you ask open-ended questions, run custom analyses, compare athletes, and correlate training variables with outcomes — things that require exporting data manually in Strava's current toolset.
Write operations are supported where Strava's API permits — creating manual activities, adding descriptions and notes, updating gear assignments, and modifying activity metadata. GPS route data from live recordings cannot be modified, which is by design.
Yes. Data is accessed only through Strava's official OAuth API using credentials you control. Improvado is SOC 2 Type II certified. Athlete data is processed in secure infrastructure and never shared across accounts. Each athlete's data is accessible only under their own authorized token.
Under 5 minutes. Authorize Improvado through Strava's standard OAuth flow, then add one line to your MCP client config. For coaches managing multiple athletes, each athlete completes their own OAuth authorization — no credential sharing required.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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