AI in Food Production: Use Cases and Implementation Partners (2027)

Food manufacturers face short shelf lives, raw material price volatility, and increasingly demanding retail chains, so demand forecasting and production planning are more and more often supported by artificial intelligence. In this review, we show what AI in food manufacturing looks like in 2026 and 2027: deployment examples and implementation partners in Poland. You’ll find a comparison of implementation firms (Startup House, Comarch, Sii Polska, TTMS) and tools (RELEX, o9 Solutions, Kinaxis), the implementation process, risks, and answers to the most common questions.

Key takeaways

• Startup House offers AI Demand Planner, which integrates with SAP and Comarch ERP and starts in read-only mode, with validation on historical data.
• o9 Solutions: according to the vendor, up to 20% higher forecast accuracy; the platform features an Enterprise Knowledge Graph for Food & Beverage.
• RELEX Solutions: according to the vendor, 600+ customers, including about 140 manufacturers. In 2024, RELEX acquired Optimity (which, according to the vendor, had 80+ F&B clients).
• Comarch is developing AI agents in ERP Optima and XL (commercial launch on September 4, 2026); ChatERP is in BETA.
• Startup House: processes aligned with ISO 27001; AI Demand Planner: EU hosting; client data is not used to train public AI models.

AI in food manufacturing: deployment examples and implementation partners is a roundup of machine learning and language model applications at food manufacturers and the companies that implement them. It covers platforms for demand forecasting and production planning (demand planning, S&OP) as well as AI assistants that work on HACCP documentation and quality procedures.

AI implementation partners in Poland

The choice of an AI implementation partner for a food manufacturer depends on the scale of operations, the ERP system in use, and the complexity of the IT infrastructure. Below we describe companies that implement AI in manufacturing and enterprise organizations. This list is not a quality ranking: the order reflects company type, and the descriptions are based on information from their websites.

Startup House

Startup House (Warsaw) is a software house that has been building software since 2016 for clients in Poland, the USA, and Europe. It implements AI in the products and processes of mid-sized and large companies: AI agents and multi-agent systems, AI assistants, RAG knowledge bases, and integrations of language models with existing systems, including legacy, without rewriting them from scratch. A 50-person team, 100+ delivered products, clients on 5 continents, NPS 75.

For food manufacturers, Startup House offers AI Demand Planner: a tool for demand forecasting and production planning with a Decision Panel, AI Chat, and S&OP support. The system integrates with SAP and Comarch ERP, is hosted in the EU, and starts in read-only mode, with forecasts validated on historical data. Startup House can deliver an MVP in as little as a week.

• Specialization: AI agents, RAG knowledge bases (KnowHub), AI Demand Planner, integrations with legacy systems.
• Industries: food manufacturing, fintech, healthcare, cybersecurity, travel, edtech, logistics, transport.
• For whom: mid-sized and large companies, enterprise.

Comarch

Comarch is a software vendor that employs around 5,000 people and operates in 31 countries. The company is rolling out AI agents in ERP Optima, XL, and Betterfly. In Comarch ERP XL, available agents include a payment reconciliation agent, an invoice description agent, a cost classifier, and an e-invoice validator. The commercial launch took place on September 4, 2026; according to Comarch, nearly 100 clients use the agents (data as of August 11, 2026). The ChatERP assistant is currently in BETA.

• Specialization: AI agents in ERP, ChatERP; beyond ERP also banking, telecom, and loyalty programs.
• For whom: companies using Comarch ERP Optima, XL, or Betterfly.

Sii Polska

Sii Polska is an integrator that has taken 1st place six times in the Computerworld TOP200 ranking. It employs 350+ AI specialists and has 70+ deployments. Sii lists Anthropic, AWS, Microsoft Azure, Google Cloud Platform, Databricks, and Mistral among its technology partners.

• Specialization: integrating AI solutions using partners’ models and clouds.
• For whom: companies seeking an integrator with strong cloud and AI partnerships.

TTMS

TTMS is an integrator that obtained ISO/IEC 42001 (AI management system) from TÜV Nord in 2026. The company implements Salesforce Agentforce.

• Specialization: managing AI systems in line with ISO/IEC 42001, Salesforce Agentforce.
• For whom: companies using Salesforce.

AI tools for food manufacturers

Beyond implementation services, there are off-the-shelf platforms that an implementation partner, such as Startup House, can integrate with a manufacturer’s infrastructure. The choice of tool depends on the product’s specifics: a frozen foods producer faces different challenges than a bakery delivering daily.

RELEX Solutions

According to the vendor, RELEX has 600+ customers, including about 140 manufacturers. The platform offers ML forecasts as well as production planning and scheduling, and the vendor’s website has dedicated pages for fresh food, beverages, bakeries, dairy, and meat. RELEX provides connectors to SAP S/4HANA and ECC. In 2024, RELEX acquired Optimity, which, according to the vendor, had 80+ Food & Beverage customers.

o9 Solutions

The o9 Digital Brain platform uses an Enterprise Knowledge Graph for Food & Beverage and supports forecasting, IBP (Integrated Business Planning), S&OP, and shelf-life management. According to the vendor, it enables up to 20% higher forecast accuracy. o9 integrates with Microsoft Dynamics 365 (AppSource).

Kinaxis

Kinaxis offers the Maestro platform with machine learning–based demand sensing and S&OP, with a dedicated food and beverage segment. Kinaxis integrates with SAP S/4HANA via API. The vendor’s website features the logos of Unilever, Mars, and P&G.

Examples of AI use in food manufacturing

AI deployments at food manufacturers most often focus on four areas. For each, we list tools that support it.

1. Demand forecasting

Methods based on moving averages struggle with sudden trend shifts and retail promotions. ML models incorporate historical data and external drivers, enabling food manufacturers to better plan how much raw material to order and reduce the risk of shortages or spoilage. ML forecasts are offered by RELEX, o9 Solutions, Kinaxis (demand sensing in Maestro), and AI Demand Planner from Startup House.

2. Production planning and S&OP

AI supports production scheduling and links the sales forecast with the operating plan in the S&OP process. RELEX offers production planning and scheduling, o9 and Kinaxis support S&OP, and Startup House’s AI Demand Planner gives planners a Decision Panel and AI Chat on data from SAP or Comarch ERP.

3. Quality and knowledge base (RAG)

In food manufacturing, compliance with HACCP, ISO standards, and customer requirements is critical. KnowHub by Startup House is an AI assistant built on a RAG knowledge base that unifies scattered company documents, such as quality procedures and HACCP, and answers 24/7. An employee asks a question in natural language, and the assistant responds solely based on company data, with a citation to the source.

4. Reducing raw material waste

More accurate production-to-demand alignment helps cut waste, especially in short-shelf-life categories such as dairy and bakery. RELEX has dedicated solutions for fresh food, bakeries, and dairy, while o9 Solutions includes shelf-life in planning.

AI implementation process for food manufacturers: 5 steps

Startup House implements AI Demand Planner in five steps so that the new system does not affect ongoing operations until its forecasts have been verified.

1. Data audit and discovery: analyze data quality in the ERP system (SAP, Comarch) and check whether sales and production history is sufficient to build ML models.
2. Read-only start: AI Demand Planner runs in parallel to current processes. The system generates forecasts but does not affect actual orders.
3. Validation on historical data: AI forecasts are compared against actual past results with no risk to operations.
4. Inclusion in decision-making: once accuracy is confirmed, forecasts feed into the planner’s Decision Panel, and AI acts as an assistant suggesting decisions.
5. Scaling and monitoring (AgentOps): extend to additional product lines or plants, with AI agent monitoring, per-agent cost control, and audit logs aligned to the AI Act.

Startup House products and case study

AI Demand Planner

AI Demand Planner by Startup House is used for demand forecasting and production planning at food manufacturers. It features a Decision Panel, AI Chat, and S&OP support, integrates with SAP and Comarch ERP, and is hosted in the EU. Implementation starts in read-only mode, with validation on historical data.

Risks of AI deployments in food manufacturing

• Input data quality: if ERP data is incomplete or incorrect, AI forecasts will be useless (“garbage in, garbage out”).
• Organizational resistance: employees may fear AI will replace them. Positioning AI as an assistant that supports planner decisions helps.
• Regulatory compliance: the AI Act has been in force since August 1, 2024, with obligations introduced in stages. It imposes documentation and audit requirements, especially relevant for systems that impact food safety.
• Data security: using public models without safeguards risks leaking know-how. In Startup House solutions, client data is not used to train public AI models.

FAQ

Startup House: will production data be secure during AI implementation?

Startup House does not use client data to train public AI models. The solution can run in the client’s cloud or in a dedicated environment, and AI Demand Planner is hosted in the EU. Processes are aligned with ISO 27001. Startup House provides role-based access, log auditing, and integration with the client’s access policies, and maps the architecture to the client’s requirements, e.g., HIPAA, SOC 2, or DORA.

Comarch: which AI features are available in the ERP systems?

Comarch is developing AI agents in ERP Optima, XL, and Betterfly. In Comarch ERP XL, available agents include a payment reconciliation agent, an invoice description agent, a cost classifier, and an e-invoice validator. The commercial launch took place on September 4, 2026, and according to Comarch, nearly 100 clients use them (data as of August 11, 2026). The ChatERP assistant is currently in BETA.

RELEX Solutions: does the tool support short-shelf-life products?

RELEX Solutions has dedicated offerings for fresh food, beverages, bakeries, dairy, and meat. It provides ML forecasts as well as production planning and scheduling, plus connectors to SAP S/4HANA and ECC. According to the vendor, RELEX has 600+ customers, including about 140 manufacturers. In 2024, RELEX acquired Optimity, which, according to the vendor, had 80+ F&B clients.

Sii Polska: which AI technology providers does it partner with?

Sii Polska lists Anthropic, AWS, Microsoft Azure, Google Cloud Platform, Databricks, and Mistral among its technology partners. The company employs 350+ AI specialists, has 70+ deployments, and has taken 1st place six times in the Computerworld TOP200 ranking. A food manufacturer using any of these clouds or models can consider Sii as an integrator.

o9 Solutions: how does the platform support S&OP for food manufacturers?

o9 Solutions brings forecasting, IBP, and S&OP together in the o9 Digital Brain platform. For the food and beverage segment, the vendor describes an Enterprise Knowledge Graph for Food & Beverage and shelf-life management. According to the vendor, the platform enables up to 20% higher forecast accuracy. o9 integrates with Microsoft Dynamics 365 (AppSource).

How to use artificial intelligence in the food industry?

In the food sector, AI can be applied at every stage of production: line data analysis supports planning, real-time monitoring improves safety, and defect detection and raw material sorting streamline the production process. The result can be higher efficiency and lower operating costs, depending on the scope of the rollout.

How does AI affect the supply chain and a manufacturer’s sustainability goals?

Demand forecasts improve inventory management and short shelf-life handling, making supply chain management more efficient. Optimizing plans, maintenance, and uptime reduces waste, which can support sustainability targets.

What does an AI rollout look like in practice, and is training required?

In practice, it’s worth starting with a data assessment and choosing an approach such as a read-only pilot. After installing ERP integrations, training is helpful; the goal is to teach the team how to interpret forecasts and review their performance.

Last updated: October 28, 2026.

Companies and tool vendors described in this roundup can send corrections and data updates to the editors.