Food production planning
Semia is an AI employee for food and beverage production planning. It reads the orders, inventory, attendance, and machine status already sitting in your systems, then drafts tomorrow's schedule every morning for a named planner to approve. Nothing gets migrated, nothing gets installed on the line, and it is useful in about two weeks.
Orders sit in the ERP, inventory in a spreadsheet, attendance in a separate system, and customer promises in WhatsApp and email. The logic that turns all of it into a runnable schedule lives in one planner's head. Operators say it plainly: it all lives in one person's head. When that person leaves, replacing them runs $20-40K by industry estimates, and the context still walks out the door.
Monday's plan meets reality fast: a short crew, a late ingredient truck, a mixer down for maintenance. The planner rebuilds the sequence by hand while the floor waits, and every rebuild is a chance to miss a shelf-life date or an allergen cleanout. By midweek the printed schedule and the actual plant have stopped speaking.
Mid-size food plants bleed $2-3M a year on overproduction, idle capacity, and chargebacks. The buckets are roughly $250K a year in waste, about $2M a year in idle capacity, and around $500K a year in chargebacks. None of it shows up as a line item, which is exactly why it keeps getting paid.
Spreadsheets carry a plant surprisingly far, then stop all at once: a growing SKU list, a second line, or the first serious allergen matrix. That is the plant Semia is built for, the mid-size food and beverage manufacturer that has outgrown Excel but does not want a heavy APS rollout. If that sounds like your plant, see /industries/bakery, /industries/dairy, or /industries/meat.
Semia reads orders, inventory and lots, ingredients, attendance, machine status, and shelf-life risk from SAP, Odoo, Microsoft Dynamics, Excel, Google Sheets, Gmail, Outlook, WhatsApp, and your attendance system. Access is read-only: nothing is migrated and nothing is installed on the line. Granting read access takes about 30 minutes, and your data stays inside your company.
The draft is sequenced for finite capacity on each line, shelf life and FEFO on every lot, allergen changeover cleanouts, and the crew that actually clocked in this morning. These are the constraints that make food scheduling different from generic manufacturing software. If the vocabulary is new to your team, the definitions live at /glossary.
Every morning Semia writes tomorrow's plan and shows plain-language reasoning for every call, with a full audit trail behind it. Nothing reaches the floor until a named planner signs off. When the day changes because of a rush order, an absence, or a breakdown, Semia re-plans and shows what moved and why.
There is no implementation team and no workshop calendar. Semia sits next to your planner, asks questions, and learns by shadowing how your plant actually runs. It is useful in about two weeks, and the planning logic it captures belongs to you, not to us.
At our design partner, the Natalie bakery and desserts plant in Or Akiva, which also supplies the Ben Ami coffee and bakery chain, daily planning went from about 3 hours a day to roughly 15 minutes (design-partner data). That is 12x faster planning, 90%+ less planning time, and 700+ hours a year returned to the person who knows the plant best. The planner reviews and approves instead of assembling.
Traditional APS and ERP planning modules can produce plans; that is not the gap. Deployments tend to be heavy, slow to roll out, dependent on clean structured data, and hard to keep aligned with the undocumented context in the planner's head. Semia captures that context by shadowing, works across the messy systems you already have, and hands a finished draft to a human for approval.
A copilot assists a human who still assembles the plan: it answers questions while the planner does the sequencing. Semia delivers the finished draft, sequenced for capacity, shelf life, and changeovers, so the human's job becomes review and approval. That is the difference between a faster keyboard and an extra planner on the team.
The fastest way to evaluate food production planning software is to watch it plan your plant, not a demo dataset. Book a walkthrough at /demo, or check what it costs at /pricing. If it cannot draft a plan your planner would sign, you will know within about two weeks.
Spreadsheets hold the numbers, but a person still does the sequencing, and every mid-day change means rebuilding by hand. Semia reads live orders, inventory, attendance, and machine status, then drafts tomorrow's plan sequenced for finite capacity, FEFO, and allergen changeovers. The planner reviews and approves a finished draft instead of assembling one, which is where the 90%+ less planning time comes from.
ERP and APS planning modules can produce plans, but deployments tend to be heavy, slow to roll out, and dependent on clean structured master data, and they rarely capture the undocumented rules in the planner's head. Semia learns those rules by shadowing your planner, reads across the messy systems you already run, and delivers a finished daily draft for a named human to approve. It works alongside an ERP like SAP, Odoo, or Microsoft Dynamics rather than replacing it.
Almost none by traditional standards: Semia is read-only, migrates nothing, and installs nothing on the line. Granting read access to your existing systems takes about 30 minutes, and Semia onboards itself by sitting next to your planner and learning through shadowing. It is useful in about two weeks.
No. Semia is built for plants where the plan lives across an ERP, spreadsheets, email, WhatsApp, and one person's head, and it reads that messy reality as it is. Nothing needs to be restructured or migrated first, and all data stays inside your company.
A named human, always. Semia drafts tomorrow's plan with plain-language reasoning for every call and a full audit trail, and a named planner signs off before anything reaches the floor. The planning logic it captures along the way belongs to your company, not to Semia.
Current plans are listed at /pricing. The useful comparison is against what doing nothing costs: mid-size food plants bleed $2-3M a year on overproduction, idle capacity, and chargebacks. A walkthrough at /demo will show what a drafted plan looks like on your own data.