Built on operator experience, not just theory
Bayseian's founding team has been involved in AgTech from the start. Bayseian co-founder Aamir Faaiz previously co-founded and led engineering at an AI-driven poultry weight-prediction and welfare-analytics company, building production computer-vision and forecasting systems for farms before founding Bayseian. That hands-on background shapes how we approach this work: we know where the data actually breaks down on a farm or in a processing plant, not just how it looks in a slide deck.
How it works · one operating layer for agriculture
Weighing systems, feed and QC records, compliance certificates, welfare logs and site paperwork rarely talk to each other, let alone to the ERP finance and operations run on. We build the layer that digitises it, forecasts on top of it, and pushes it into the systems your teams already use, instead of adding one more dashboard nobody opens.
What it does · three capabilities
Paper QC logs, weighing tickets and site records turned into structured, queryable data. OCR and automated extraction replace manual entry across every workstream.
Forecasting models built on the same discipline as production weight-prediction and welfare-analytics systems. They turn historical site data into forward-looking signal.
Connectors that push digitised, forecast data straight into the agricultural ERPs and reporting tools your operations teams already run on, including MCP, the emerging standard for connecting AI agents directly to internal systems.
Where this shows up · six recurring use cases
Paper-based QC and inspection records turned into structured, searchable data. The Avara Foods pattern, applied wherever it recurs.
Certification deadlines, audit trails and regulatory reporting kept current automatically, across every site.
Sensor and observation data brought together to flag welfare and health issues earlier, across sites and shifts.
Field and site sensor data (weighing, environment, feed) unified into one pipeline instead of a dozen disconnected dashboards.
Farm-to-plant-to-retailer tracking that stands up to audit. Every batch traceable back to its source.
The same forecasting discipline applied upstream: historical and seasonal data turned into forward-looking yield and demand forecasts for planting and procurement decisions.
Proof over promises
Enterprise data pipeline digitisation for one of Europe's largest poultry integrators (>$1Bn annual revenue). Digitised and centralised FeedQC data pipelines with OCR, Power BI, and unified reporting across multiple operational workstreams.
$1Bn+
Client annual revenue
100%
Manual entry eliminated
Whether it's a single site or an enterprise-scale supply chain, we've built the digitisation and analytics layer before, and we know where it actually breaks.