Crowdsourced routing for African last-mile delivery
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.
MtaaniFlow empowers delivery riders to report real-time street conditions, traffic jams, and hidden shortcuts through an easy-to-use mobile app. This data is aggregated and used to generate optimal routes that avoid common pitfalls in unmapped African cities like Lagos and Nairobi. Fleet operators gain a dashboard to assign jobs, track performance, and reduce delivery times significantly.
Quick commerce operators and delivery platform executives expanding in African urban markets (Lagos, Nairobi, etc.)
Exclusive focus on rider-contributed hyperlocal data with voice and icon-based reporting, creating a moat of proprietary African street intelligence that global players can't replicate quickly.
supportive
Flutter app for receiving routes, navigation, and reporting road conditions with voice notes
Web view inside Flutter for assigning deliveries and monitoring drivers in real-time
Simple interface for riders to report issues that feeds into routing algorithm
Algorithm that incorporates live reports to suggest best paths using PostGIS
Live location and status updates for all active deliveries
Insights on delivery times, common problem areas and heatmaps
Works without internet with periodic sync using cached maps
Turn-by-turn voice directions supporting Swahili and local accents
Gamification and scoring based on reports and timeliness
Integrate with existing dispatch systems
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| subscription_tier | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| phone | text | No |
| role | text | No |
| company_id | uuid | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| company_id | uuid | No |
| rider_id | uuid | Yes |
| pickup_location | text | No |
| delivery_location | text | No |
| status | text | No |
| assigned_route | jsonb | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| location | point | No |
| condition_type | text | No |
| description | text | Yes |
| severity | int | No |
| created_at | timestamp | No |
Relationships:
/api/routes/optimizeGenerate optimized route based on current reports and PostGIS data
/api/reportsSubmit a new road condition report with location
/api/deliveriesFetch active deliveries for a company or rider
/api/deliveries/{id}/assignAssign delivery to a rider
/api/analyticsGet performance analytics and heatmaps for company
/api/auth/loginPhone-based OTP authentication
5 drivers max
None
Custom
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 120 | 12% | $360 | $4,320 |
| Month 6 | 650 | 22% | $3,575 | $42,900 |
MtaaniFlow turns rider reports into superpowers for your delivery fleet. Built for Lagos, Nairobi, and beyond.
Reach out to 20 quick commerce operators in Lagos and Nairobi via LinkedIn and local Facebook groups for delivery businesses. Offer 3-month free Pro access in exchange for feedback and case studies. Attend meetups like Nairobi Tech Week or Lagos Startup Week to demo the product and secure pilots with early-stage delivery startups.
Global coverage and reliability
Lacks hyperlocal informal data for African streets
Community powered by actual delivery riders in each city
Strong route optimization algorithms
Not tailored to African market conditions or data sources
Built from the ground up for African urban challenges with local data
Integrated into large platform
Not available to other operators
Independent SaaS usable by all quick commerce companies
Network effects from rider participation: more reports lead to better routes which attracts more riders and companies, creating a valuable proprietary dataset of African street intelligence.
Quick commerce boom in Africa with companies like Jumia, Glovo, and local players expanding rapidly while street mapping and traffic data remains poor. Smartphone penetration among riders is rising, making crowdsourcing feasible now.
Low adoption from delivery riders for reporting
Design ultra-simple UI with incentives like performance bonuses and run pilot programs with paid riders
Inaccurate geo data or algorithm failures
Start with conservative routing suggestions and allow manual overrides, use established mapping providers as base
Solo dev burnout during 8 week build
Follow strict scope, use no-code where possible for landing page, prioritize must-have features
Data privacy concerns with location tracking
Implement GDPR-like compliance from start and get explicit consent
Success: At least 10 express strong interest and willingness to pay $25/mo
Success: 80% retention after 2 weeks and positive feedback on time savings
Success: 50 signups in first week with 8 paid conversions
Success: Acquire 10 new customers via referrals within a month
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