01 / FIELD VIEW
Notice how the field varies.
Review mapped zones and see where vegetation patterns may deserve a closer look.
C
Illustrative satellite view · sample dataAI field intelligence for farmers in West africa
AI that sees the field
before it changes.
Hasashe uses AI to bring satellite observations, weather forecasts, and field information into one clear view, so farmers in West africa can plan planting, irrigation and inputs earlier and lose less to dry spells and late rains.
Beta access opening soon for selected farmers in Western africa
WEATHER WINDOWRainfall outlook
↗FIELD ZONEWorth a closer look
01THE FIELD, IN CONTEXT
When the season shifts,
timing matters.
For a farmer in Nigeria, a planting date that is a week off, irrigation done too late, or a dry spell noticed after the damage is done can cost a season’s seed, fertiliser, labour and income.
Commercial farmers and the agronomists who advise them all face it: conditions change field by field, and the signals sit in separate places. AI helps because it can read satellite imagery, rainfall forecasts and field records together, every day, and flag which field needs attention first.
FIELD CONDITIONS
DECISION WINDOW
01Conditions shift
Weather and field signals change through the season.
02Signals are scattered
It takes time to bring observations into one picture.
03Mistakes get expensive
Late planting, mistimed irrigation and wasted inputs add up to lost income.
02THE HASASHE APPROACH
A clearer picture, one field at a time
From scattered signals
to a steadier view.
Hasashe is an AI-powered field intelligence platform built for farmers in West africa. It combines information about the land, the crop, and the coming weather in one easier view, and shows a farmer which field to check first and why.
Instead of asking a farmer to interpret every chart alone, Hasashe can bring forward the signals that may matter, explain why they appear, and leave the final decision with the person who knows the farm.
See how it could work ↓WEATHER↘
SATELLITE↗
FIELD NOTES↗
One field view
Information, brought together with care.
“
WHY THIS MOMENT
Useful information should arrive
while there is still room to plan.
West africa’s digital economy is growing quickly and mobile phones are now part of farm life, yet many farm decisions are still made from memory, habit and scattered information. Climate variability is making seasons harder to predict. Satellite data and AI forecasting are now practical at farm level, so farmers can get timely, understandable guidance without handing over their judgment.
A PRACTICAL OPPORTUNITY
Observation → understanding → choice
03AI TECHNOLOGY
The intelligence behind the interface
Let the data speak
with context.
AI is useful here when it turns many changing signals into a forecast a farmer can inspect, question, and use as one input to a decision.
01 · INPUTS
What the system reads
Satellite observationsVegetation patterns and change over time
Weather informationForecasts and observed rainfall
Farm contextCrop, planting date, field notes, and readings where available
Data availability and quality will vary by location.
→MODEL WORKFLOW
02 · ANALYSIS
What it could estimate
FAI
Computer vision reads satellite imagery for vegetation change, time-series models forecast moisture and rainfall, and risk models flag fields worth reviewing. Each model is evaluated against field evidence before we rely on it.
→FARMER REVIEW
03 · OUTPUT
What a farmer sees
FIELD ADVISORY
A dry spell may be building.Check the moisture trend and upcoming rainfall before deciding what to do.
Why this appearedForecast + field context
Forecast confidenceShown with uncertainty
The advisory should show the supporting evidence and uncertainty. It is a guide for review, not an automatic instruction.
Why GPUs and the cloud: satellite imagery and multi-year weather records are large, and every field needs fresh analysis each day. GPU acceleration makes image analysis and model inference fast enough to deliver advisories on time, and cloud infrastructure lets the same system grow from a few beta farms to thousands of fields.
04PRODUCT FEATURES
A useful view of the work ahead
Made for the rhythm
of the season.
Hasashe is being shaped around the moments when a farmer wants a quick overview, a little more evidence, or a record to return to later.
02 / FORECAST
Follow the trend, not just today.
Place recent observations beside the forecast, with the limits of each made visible.
SOIL MOISTUREILLUSTRATIVE
604020
PASTTODAYFORECAST
03 / RISK SIGNALS
Bring attention to what may matter.
Review weather-related risks as signals to investigate, not certainty about what will happen.
Dry conditionsElevated
Heavy rainfallWatch
Heat stressPossible
Illustrative risk indicators
04 / MOBILE VIEW
Keep the field close.
A mobile-first summary designed to make the main advisory easy to find on the move.
hasashe.•••
BLOCK C · MAIZEFIELD NOTEDry spell may be building.Review the forecast before planning irrigation.
MOISTURE TREND41%
⌂▦◷
05 / FIELD REPORT
Return to the reasoning.
A concise report can gather the current view, key forecasts, and notes in one place.
FIELD REPORTWEEKLY VIEW
Block C FIELD SUMMARY
FORECASTFIELD NOTESACTIONS
06 / DAILY ADVISORY
Know why a note appeared.
Each advisory should connect a suggested check to the data and assumptions behind it.
TODAY'S FIELD NOTE
✳Rain is possible in three days.
Forecast confidence
View evidence ↗
All product panels on this page are illustrative. Their example values are not customer results or validated forecasts.
05MEASURABLE BENEFITS
What we will measure
in the beta.
These are beta goals, not proven results. We will publish measured results once we have them.
Earlier warnings
Beta goal: flag dry spells and late rains sooner than a farmer would notice alone.
Better timing
Beta goal: help farmers choose planting and irrigation windows with more confidence.
Less wasted input
Beta goal: reduce water, seed and fertiliser spent at the wrong time.
Faster field checks
Beta goal: show which fields need a visit first, so less time is spent checking all of them.
06HOW IT COULD WORK
A steady process,
from field to decision.
The product should make each step understandable, and let the farmer remain in control throughout.
Add a field
Set the field boundary, crop, and planting details that are available.
↗Bring signals together
Hasashe organizes weather, satellite, and field information for review.
↗Review the outlook
See a forecast or advisory with its evidence and uncertainty.
↗Choose what to do
Use the information to support a decision that fits the farm.
↗07TECHNOLOGY & INFRASTRUCTURE
A practical foundation for a data-rich product
Cloud for reach.
Acceleration where it helps.
We plan to use AWS cloud services to run the platform and organize field information, and NVIDIA technology to speed up the parts of the AI work that benefit from a GPU.
FARMERPhone or browser
DELIVERYCloudFront + S3
APPLICATIONAPI + sign-in
FIELD DATAPostgreSQL + imagery storage
SCHEDULED WORKData refresh + forecasts
OPTIONAL GPU SERVICECUDA · TensorRT · Triton
Core web platform GPU and edge toolsHasashe system design · how the parts work together
01 / AWSaws
How we use AWS
We plan to use Amazon S3 to store the website files and field imagery, and CloudFront to deliver them quickly. Amazon ECS with Fargate would host our backend APIs, Amazon RDS for PostgreSQL would hold farm and field records securely, and Amazon Cognito would manage sign-in. CloudWatch would show how the services perform. Amazon Bedrock could power plain-language explanations of each advisory.
EventBridge and Lambda would run scheduled jobs such as checking for new weather data, with Step Functions ordering multi-step jobs. For larger AI workloads we would use Amazon SageMaker AI or GPU instances in AWS to train and run models, so the platform can grow with the number of fields.
02 / NVIDIANVIDIA
How we use NVIDIA
We plan to use CUDA to run many calculations at once, which speeds up satellite image processing and model development. TensorRT would optimize trained models for fast inference on NVIDIA hardware, and Triton Inference Server would serve those models to our backend so advisories arrive quickly as the platform scales.
Jetson is an edge option for a later stage: running AI on a small computer on or near a farm, for example on a camera or sensor reading when internet access is limited. It is not needed for the core web platform.
✳
Designed with care for field information.
We plan access controls, encrypted connections, backups and clear data-retention rules. For security or data-protection questions, write to CONTACT_EMAIL. Our Privacy Policy explains how we handle the information you give us.
08WHO IT IS FOR
A focused starting point
Built around the people
who know the land.
Hasashe is being designed for farmers and agricultural teams who want clearer field context and a more useful way to plan around changing conditions.
We launch in West africa, working directly with the farmers we can support well. Hasashe starts with a free plan, and advanced features are unlocked through a monthly or yearly Pro subscription. Prices will be published when plans open.
INITIAL PRODUCT FOCUS01—03
LAUNCH MARKETWest africa
Launch market, starting with crop farmers in the north. Other West african markets come later on the roadmap.
Farmers · agronomists · field teamsPlansFree + Pro subscription · monthly or yearly
01 / FREE
Core field intelligence at no cost.
Basic features, such as the field view and the daily field note, are free for every farmer.
02 / PRO
Advanced tools, monthly or yearly.
Advanced features, such as risk signals and field reports, are unlocked with a Pro subscription. Pricing will be announced when plans open.
09PRODUCT PROGRESS
Where we are
Beta access
is opening soon.
The first version of Hasashe works, and selected farmers in West africa will be invited to test it. Screens on this site use sample values to make them easy to follow; they are not validated results. Join the waitlist to be considered for early access.
Beta access opening soon · selected users in West africa
01
Build the first versionField map, forecast, advisory and report views
✓02
Invite selected beta usersFarmers in West africa, in small groups
●03
Evaluate forecasts with usersMeasure model usefulness before making claims
○04
Proposed product progressionPrepare for wider accessScale only after the product is ready
○10ROADMAP
Built carefully.
Improved with evidence.
A roadmap should follow what the product learns in the field. These are proposed stages, not dated commitments.
PHASE 1 · MVP DEVELOPMENT
COMPLETEMVP built
Field setup, a clear dashboard, and a dependable data pipeline.
PHASE 2 · BETA TESTING
OPENING SOONBeta with early users in West africa
Understand what farmers need to see, when they need it, and what context helps.
PHASE 3 · AI MODEL IMPROVEMENT
PLANNEDImprove the AI models
Compare predictions with observations and make uncertainty easier to understand.
PHASE 4 · CLOUD DEPLOYMENT
PLANNEDPrepare reliable delivery
Strengthen availability, monitoring, data protection, and operational workflows.
PHASE 5 · GPU-ACCELERATED SCALING
PLANNEDScale the right workloads
Use GPU acceleration when measured workloads justify it.
PHASE 6 · MORE West africaN MARKETS
PLANNEDGrow with local evidence
Expand to other West african markets once West africa is established.
11THE PEOPLE BEHIND HASASHE
Good tools are built by people who listen
People, purpose,
and patient work.
Hasashe is built by software developers, AI engineers, product designers and agricultural domain experts. Meet the people leading the work.
01
Samson Fasasi is an agricultural researcher with over 5 years hands-on experience in agricultural research and rural development. He previously led a climate advisory pilot that delivered localized planting guidance to farming communities across northern West africa. At Hasashe, he leads the vision of putting reliable weather and field intelligence in every farmer’s hands.
02
Adedokun Samuel is a software and AI engineer with years of experience in building data-driven products. He has developed machine-learning systems that combine satellite imagery, weather data, and user reports to support decisions in agriculture and other climate-sensitive sectors. At Hasashe, he leads the technical direction, from the data pipeline to the AI models that turn complex signals into clear, timely recommendations.
03
Bolanle Adeyemi is a product manager and field operation specialist. She has overseen user research and feedback programs involving about 300 farmers and extension workers. At Hasashe, she shapes the product around farmers’ needs and leads beta operations, field feedback, and partner coordination.
✳
A conversation can start simply
Let’s talk about
what your fields need.
Tell us a little about your work. Get Started, or ask for a demo, and we will reply by email.
↳Or write to us at CONTACT_EMAIL. We use your details only to reply to your request. See our Privacy Policy.