Your agent knows their calendar. It doesn't know their mind.
AI agents schedule meetings, triage tasks, and draft replies, blind to whether their human is cognitively ready for any of it. WhenPeak is the performance layer for the agentic economy. One API call gives your agent the context it's missing.
Agents optimise for time. They ignore cognitive state.
Every scheduling agent makes the same hidden assumption: that all hours are cognitively equal. Book the strategy session at 2pm? Fine. Schedule three back-to-back negotiations after a bad night's sleep? No objection. Send the "reply to this important email" nudge during the post-lunch crash? Sure.
The agent has no model of the human's capacity. It treats a calendar slot as a calendar slot. But a 10am deep-work block and a 2pm deep-work block produce measurably different output, research shows working memory and processing speed vary 20-40% across the day.
WhenPeak gives your agent a 24-hour model of its user's cognitive capacity, derived from sleep data and the Two-Process Model of sleep/wake regulation (Borbély 1982).
With one API call, your agent knows:
- · The user's peak analytical window (when to schedule deep work)
- · Their natural dip (when to route admin and email)
- · Their secondary peak (when to schedule calls and collaboration)
- · Their daily readiness score (when to suggest rest instead of pushing)
The agent stops scheduling blind.
Six things your agent can do with one prediction
Schedule deep work at peak
Route the user's most cognitively demanding tasks into their predicted peak window. The same task, done at peak vs dip, can take 15-30% less time with 20-40% fewer errors.
Protect the dip
Automatically schedule low-cognitive overhead: email, admin, routine reviews, into the predicted post-lunch dip, instead of wasting peak hours on it.
Avoid high-stakes scheduling on low-readiness days
When the DPS is low, the agent can flag or reschedule high-stakes meetings: negotiations, board presentations, interviews, where fatigue-driven errors are most costly.
Suggest rest proactively
A low DPS isn't just a scheduling input, it's a wellbeing signal. The agent can proactively recommend lighter days, protecting the user from burnout cycles.
Prepare for the moments that matter
Given the user's peak window and an upcoming high-stakes event, the agent can time preparation, reminders, and even caffeine suggestions to land the user at peak when it counts.
Personalise its own timing
The agent can time its own notifications and check-ins to match the user's cognitive state, engaging during receptive windows, staying quiet during deep focus.
Integrate in under an hour
Direct API (any framework)
curl -X POST https://api.whenpeak.com/api/v1/predict \
-H "Content-Type: application/json" \
-d '{"sleep_time":"23:00","wake_time":"07:00","sleep_quality":"good"}'{
"dps": 74,
"peak_1": { "time": "10:00" },
"dip": { "time": "14:00" },
"peak_2": { "time": "17:00" },
"chronotype": "Third Bird",
"confidence": "low"
}LLM function calling
Pass the OpenAPI spec directly to your model's tool definitions.
https://api.whenpeak.com/openapi.jsonWorks with OpenAI function calling, Anthropic tool use, LangChain, and any framework that accepts OpenAPI tool schemas.
Native integrations
Reference implementation for GPT builders.
Remote MCP server deployed and callable.
Built for apps with real users
Building a VA, a productivity app, or an agent platform with many users? Provision isolated end-user accounts under your developer key. Each user gets their own sleep history, chronotype, and personalised curve that improves over 30 days.
Your developer key (pk_live_...)
├── End user A → own sleep history, own curve
├── End user B → own sleep history, own curve
└── End user C → own sleep history, own curve
↓
All consuming from your tier's monthly quotasupported on Growth tier (50K calls/mo at 1 prediction/user/day)
supported on Scale tier (500K calls/mo)
to full personalisation per end-user
Why this works: the Two-Process Model
WhenPeak isn't a heuristic. It implements the Two-Process Model of sleep/wake regulation, the dominant framework in chronobiology since Borbély (1982) and empirically validated by Dijk & Czeisler (1995).
Process C models the circadian alerting signal, phased to the user's chronotype. Process S models homeostatic sleep pressure building from wake time. Their interaction produces the characteristic daily curve: a morning analytical peak, a post-lunch dip, and a late-afternoon secondary peak. The model is what lets WhenPeak return a defensible prediction rather than a guess.
- Borbély (1982)"A two process model of sleep regulation"Human Neurobiology, foundational framework
- Dijk & Czeisler (1995)"Contribution of the circadian pacemaker and the sleep homeostat to sleep propensity"Journal of Neuroscience, empirical validation
- 222 unit tests"Algorithm validated against expected curve shapes"Across chronotypes and sleep conditions
See what your agent would receive
Stateless prediction latency, fits in an agent loop
To add cognitive awareness to any agent
Variance in cognitive capacity across the day the agent now sees
Live native integrations (ChatGPT, Claude) + open OpenAPI