Возможности

Всё, что нужно вашей команде для учёта времени, утверждений, отчётов и контроля бюджета.

Mon
6
Tue
7
Wed
8
Thu
9
Fri
10
Sat
11
Sun
12
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
Neural Network Opt.09:00-12:00
Model architecture review
Data Pipeline13:00-15:30
ETL process design
Computer Vision R&D09:30-12:00
Image preprocessing
Neural Network Opt.13:00-16:00
Training pipeline refactor
Data Pipeline09:00-11:00
Schema migration scripts
Algorithm Research12:00-14:30
Literature review
Computer Vision R&D09:00-13:00
Object detection model
Neural Network Opt.14:00-16:30
Hyperparameter tuning
Algorithm Research09:00-11:30
Benchmark testing
Data Pipeline12:30-16:00
Performance testing

Умный учёт времени

Регистрируйте часы за секунды в интуитивном недельном календаре. Каждая запись привязана к проекту, готова к аудиту по умолчанию.

Подробнее →
Approved
Pending
Not submitted
Team Member
W1
W2
W3
W4
W5
W6
Sarah Chen
Emma de Vries
Marcus van Dijk
Tom Bakker

Процесс утверждения

Двухэтапное утверждение с полным журналом аудита. Сотрудники отправляют, администраторы проверяют — каждое действие с отметкой времени.

Подробнее →

Reports

AI-generated R&D reports from tracked hours

Neural Network Opt.
2025
Admin Notes

Context for January report

Key milestone: completed distributed GPU training setup. Focus on neural network optimization benchmarks.
Logged Activities312h
W1 — 28.5h

Model architecture review and benchmark setup

Jan 6Sarah Chen

3h

Training pipeline refactor for GPU distribution

Jan 7Marcus van Dijk

4h

ETL process redesign and testing

Jan 8Emma de Vries

2h 30m
January 2025Approved
Generate
Approve

1. Research Activities

The team focused on neural network architecture optimization and data pipeline improvements. Model benchmarks showed a 18% performance increase over baseline. Training pipeline was refactored for distributed GPU computation.

2. Detected Issues

Memory consumption during large batch processing exceeded available GPU VRAM. Image preprocessing pipeline showed inconsistent results across different input resolutions. Need to investigate quantization approaches.

3. Achieved R&D Results

Completed model architecture review with documented findings. ETL process redesign reduced processing time by 40%. Object detection model achieving 84% accuracy on test dataset.

4. Status & Projected Progress

On track for Q2 milestones. Computer Vision R&D accelerated — planning to allocate additional hours in February. Algorithm research identified 3 promising approaches for implementation.

Автоматизированные отчёты

ИИ генерирует структурированные отчёты для аудита из утверждённых часов. Проверяйте, редактируйте и экспортируйте в PDF.

Подробнее →

R&D Hours

Track usage against your annual budget

2025
Usage Progress
1247 / 2000 hours62%
Approved 1160hPending 87h

Monthly Usage

312
Jan
298
Feb
356
Mar
281
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Forecast
Projected: 203%
0h500h1000h1500h2000hW1W13W26W39W52
ActualProjectedTarget PaceBudget

Usage by Project

Neural Network Opt.520h (42%)
Data Pipeline380h (30%)
Computer Vision R&D210h (17%)
Algorithm Research137h (11%)

Управление бюджетом

Отслеживайте выделенные часы относительно фактического использования в реальном времени. Прогнозирование показывает результат при текущем темпе.

Подробнее →