Predictive ETAs for chaotic African streets
Delivery companies in African cities like Lagos and Nairobi lose hours per trip navigating unmapped streets, endless traffic, and informal economies while trying to meet consumer demand for instant convenience.
PulseRoute analyzes historical delivery data, local calendars (market days, holidays), weather, and real-time inputs to forecast accurate arrival times and recommend the best departure windows. Quick commerce operators can optimize their entire fleet scheduling to meet customer promises for instant delivery despite unpredictable conditions.
Quick commerce operators and delivery platform executives expanding in African urban markets (Lagos, Nairobi, etc.)
Unique incorporation of 'informal economy signals' like vendor market days, religious events, and boda traffic patterns into ML models, providing superior ETA accuracy compared to generic tools.
professional
ML model trained on African city data to predict accurate ETAs
Web-enabled Flutter dashboard to plan daily dispatch windows
Pipeline to import past trips and enrich with market day calendars
What-if modeling for different dispatch times and conditions
Adjusts predictions when new reports or weather changes occur
Curated database of market days, holidays, and recurring events per city
Pulls weather forecasts and correlates with historical delay data
Sends predictive delay warnings via push and SMS
Compares actual vs predicted ETAs across operators
Automated weekly model retraining pipeline
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| subscription_tier | text | No |
| cities | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| company_id | uuid | No |
| origin | text | No |
| destination | text | No |
| actual_duration | int | No |
| market_day | bool | No |
| weather | text | Yes |
| timestamp | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| company_id | uuid | No |
| order_id | text | No |
| predicted_eta | int | No |
| confidence | int | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| city | text | No |
| event_type | text | No |
| start_date | timestamp | No |
| end_date | timestamp | No |
| impact_factor | int | No |
/api/predictReturn ETA prediction for a route with context
/api/schedule/simulateRun what-if simulation for a batch of orders
/api/trips/importImport historical trip data for model training
/api/eventsFetch local events and market days for a city
/api/analytics/accuracyReturn prediction accuracy metrics over time
50 orders per day
None
Custom
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 80 | 15% | $300 | $3,600 |
| Month 6 | 520 | 28% | $3,640 | $43,680 |
PulseRoute uses African-specific signals and machine learning to deliver accurate ETAs and optimal dispatch times.
Contact operations leads at 15 established quick commerce companies via warm LinkedIn intros and local accelerator networks (e.g. CcHUB, iHub). Offer free 60-day Enterprise trial including custom model fine-tuning on their historical data in exchange for a video testimonial and 6 months paid commitment if successful.
Strong enterprise dispatch features
Generic models not tuned for African informal signals
Specialized ML features for market days and boda patterns
Excellent route visualization
No predictive ETA component for volatile markets
Focus on prediction rather than just routing
Large driver network in Africa
No B2B fleet optimization SaaS for operators
Dedicated operator analytics and planning tools
Proprietary dataset combining public and customer-contributed trip data enriched with African-specific features (market days, informal holidays) that improves model accuracy over time.
Explosion of instant delivery expectations across African cities combined with increasing availability of historical trip data from platforms and improving weather APIs. ML tools have become accessible to solo developers.
ML model performs poorly on new cities
Start with two cities only (Lagos, Nairobi), use transfer learning, and require minimum data volume before expanding
Operators unwilling to share historical data
Offer significant value (free optimization) before asking for data and provide strong privacy guarantees
Longer build time due to ML complexity
Use simple scikit-learn models first instead of deep learning; iterate with real users quickly
Success: At least 8 confirm current ETA accuracy is below 65% and would pay for improvement
Success: Achieve >82% ETA accuracy within 15 minutes on test set with pilots
Success: 40 signups and at least 6 paid Pro conversions in first 14 days
Success: Reach $3,500 MRR and 75% retention at month 4
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