AI · Cognitive Performance

Cerevas is a cognitive performance system for knowledge work.

Measure attention, learning, and decision quality.
Improve what you can quantify.

The problem

Cognitive performance is unmeasured.

2.5 h
Average productive focus time per knowledge worker per day
40%
Of the workday lost to context-switching and cognitive overhead
23 min
Average re-engagement time after a single interruption
0
Tools that give real data on your attention quality or mental load

What's missing

  • No tool measures attention quality over time
  • No tool tracks personal learning retention rates
  • No tool estimates cognitive load during a workday
  • Calendar and task tools measure activity, not mental output

Why it matters

  • Knowledge workers make decisions based on cognitive state they cannot see
  • Unmeasured performance cannot be systematically improved
  • Small gains in attention quality compound over months and years

The approach

Measure. Model. Improve.

Every Cerevas product follows the same method: quantify the cognitive variable, build a personal model from your data, then surface targeted interventions.

Step 1
Measure

Collect behavioral signals from your work sessions

Step 2
Model

AI builds a personal cognitive profile from your data

Step 3
Improve

Actionable recommendations calibrated to your profile

Focus

Attention quality

  • Tracks sustained attention and focus session depth
  • Identifies peak cognitive windows
  • Models interruption recovery time
Learn

Learning retention

  • Models personal retention curves per topic
  • Adapts review timing to actual recall rate
  • Surfaces knowledge gaps before they compound
Flow

Cognitive load

  • Estimates decision fatigue from behavioral proxies
  • Recommends task sequencing by cognitive weight
  • Tracks weekly mental energy patterns

Products

Three tools. One platform.

Cerevas Focus
Attention optimization
Tracks focus session depth, interruption frequency, and peak attention windows. Generates a daily work protocol calibrated to your cognitive profile.
Beta
Cerevas Learn
Retention and recall
Models individual retention curves, adapts review scheduling to actual recall data, and maps knowledge gaps.
In development
Cerevas Flow
Cognitive load tracking
Estimates decision fatigue in real time, recommends task sequencing, and builds a longitudinal model of mental energy.
In development

Early access

Get early access to Cerevas Focus.

Private beta is open. Join the waitlist.

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Products

Three systems. One cognitive platform.

Each product targets a distinct dimension of performance. Together they form a complete model of how you think, learn, and decide.

Cerevas Focus

Attention optimization for deep work

StatusPrivate beta
AccessWaitlist
PlatformWeb
PricingFree in beta

What it does

  • Tracks deep work sessions and measures attention continuity
  • Identifies interruption patterns and calculates recovery cost
  • Models the conditions where your focus is consistently highest
  • Generates a daily schedule placing hard work in peak attention windows

What it measures

  • Focus session depth and duration
  • Attention continuity score over time
  • Interruption frequency and recovery time
  • Peak cognitive window by hour and day of week

What it improves

  • Deep work hours per day
  • Interruption recovery speed
  • Alignment between task demand and cognitive state
Cerevas Learn

Retention-first learning system

StatusIn development
AccessWaitlist
PlatformWeb + mobile
PricingTBD

What it does

  • Models your personal forgetting curve per topic
  • Surfaces review sessions at the point of maximum retention leverage
  • Maps knowledge gaps and estimates their downstream impact
  • Tracks which learning conditions produce the most durable encoding

What it measures

  • Recall accuracy over time per topic
  • Retention half-life and decay rate
  • Knowledge gap severity score
  • Encoding quality by session context

What it improves

  • Long-term retention without additional study time
  • Speed of identifying gaps before they surface as errors
  • Alignment between review timing and actual memory state
Cerevas Flow

Cognitive load and decision quality

StatusIn development
AccessWaitlist
PlatformWeb + integrations
PricingTBD

What it does

  • Estimates cognitive load in real time from behavioral signals
  • Recommends task order based on cognitive weight and current load state
  • Flags workflows and meeting patterns that increase overhead without proportional output
  • Builds a longitudinal model of mental energy across the week

What it measures

  • Cognitive load index throughout the day
  • Task-switch frequency and cumulative cost
  • Decision fatigue onset timing
  • Weekly energy curve by role and context

What it improves

  • Decision quality at high-stakes moments
  • Task sequencing to protect peak cognitive hours
  • Identification of structural friction reducing output

Request early access.

Cerevas Focus is in private beta. Join the waitlist.

Free during beta. No commitment.

Insights

The science behind each product.

Research summaries on attention, learning, and cognitive load — the findings that directly inform how Cerevas is built.

Attention

Sustained focus degrades after 90 minutes

Ultradian rhythms produce natural 90-minute cognitive cycles. Working past this threshold produces measurable declines in decision quality. Focus's session model accounts for this cycle.

Memory

Spaced retrieval outperforms re-reading by 4×

Active recall at expanding intervals produces roughly four times the long-term retention of passive review. Learn's review engine is built directly on this retrieval-practice literature.

Cognitive load

Decision quality drops after the 20th decision

Decision fatigue is quantifiable. Flow tracks behavioral proxies for load accumulation to estimate where this threshold falls in your individual workday.

Attention

A single notification costs 23 minutes of focus

Interruption recovery is not instantaneous. Focus tracks the re-engagement curve after each interruption and uses it to model true available deep work time.

Memory

Encoding context affects retrieval reliability

Memory encoded in a specific context is retrieved more reliably in a similar context. Learn tracks encoding conditions to identify what environment produces the most durable learning for you.

Cognitive load

Context switching leaves a residual attention trace

Each task switch leaves residual activation from the prior task. This residue compounds across the day. Flow's load model accounts for cumulative switch cost, not just frequency.

About

Cerevas builds systems that measure cognitive performance.

Early-stage AI company. Los Angeles. Founded 2026.

What we build
  • AI systems that quantify attention quality, learning retention, and cognitive load
  • Personal models that improve with continued use
  • Tools for knowledge workers who think for a living
  • A long-term cognitive performance platform across Focus, Learn, and Flow
How we build it
  • Behavioral signal collection — no wearables required
  • Personal AI models trained on individual data, not population averages
  • Measurement-first: every feature is anchored to a quantifiable cognitive variable
  • Scientific literature informs product design; we do not make clinical claims
What we don't claim
  • No clinical or medical claims
  • No fabricated outcome data
  • Learn and Flow are in development — no finished product yet
  • Focus is in private beta with a small group of early users
Status
StageEarly-stage startup
Founded2026
LocationLos Angeles, CA
FocusPrivate beta
LearnIn development
FlowIn development
Scientific basis
  • Attention and working memory research
  • Spaced retrieval and memory consolidation literature
  • Cognitive load theory
  • Ultradian rhythm studies
  • Interruption and recovery research