Source.ag acquires Optimal to put greenhouse climate control on autopilot
Source.ag acquires London AI startup Optimal, whose greenhouse control delivered 13% more yield with 27% less energy. AgTech news on the deal.
Source.ag, the Amsterdam artificial intelligence company for greenhouse growers, announced on 7 October 2026 that it has acquired Optimal, a London startup that runs greenhouse climate control autonomously. Financial terms were not disclosed. Optimal founder and chief executive Dave Hunter joins Source.ag as VP Autonomous Control, together with Optimal's engineering team. HortiDaily reported the deal on 9 October 2026.
Optimal's software takes direct control of heating, ventilation, screens, lighting, CO2 and irrigation through the grower's existing hardware. It combines physics models with machine learning to plan the growing environment several days ahead, then re-optimises in real time as the weather moves. In a commercial scale Dutch greenhouse the system delivered 13 per cent more yield with 27 per cent less energy than the benchmark Dutch grower. Optimal has run commercial operations in the Netherlands and Canada. It was founded in 2016 through the Entrepreneurs First programme, and its backers include Founders Fund, Backed VC, 2050 and Acequia Capital. HortiDaily reports that Hunter previously headed quantitative trading at Deutsche Bank and was a member of the machine learning group at Oxford.
Source.ag was founded in 2020 by Rien Kamman and Ernst van Bruggen. Its software monitors crops, forecasts harvests weeks ahead and automates irrigation. It runs in more than 500 greenhouses across 25 countries, supporting 2.5 billion euros of tomato, pepper and cucumber production a year, and the company says it works with 13 of the world's 25 largest high-tech growers. Source.ag has raised 50 million euros from investors including Astanor Ventures, Acre Venture Partners, Enza Zaden and Harvest House.
The stated logic is a closed loop in which Source.ag observes and predicts while Optimal acts. Kamman framed the deal as the company putting money behind the industry's talk of physical AI rather than only discussing it. Hunter argued that applied AI can work around the global shortage of skilled growers by running houses autonomously. Kees van Veen, chief executive of Agro Care, one of Europe's largest greenhouse tomato producers and a Source.ag customer, said growers need tools that hold climate, energy and quality steady rather than more hype, and that his company intends to be among the first to use autonomous control. The first autonomous deployments at Source.ag customers in Europe and North America go live this winter.
Why it matters for AgTech founders: this is an acquisition that buys the hard half of a product, not a customer list. Source.ag already had the data relationship, the installed base and the forecasting layer. What it did not have was the willingness to take the actuators. Optimal had the control layer and a published number from a commercial house, and roughly nine years of company life spent getting there. Three points travel out of this deal. First, sensing and forecasting sell far more easily than control, because a wrong forecast costs an argument and a wrong valve costs a crop, which is why the monitoring layer consolidates first and the control layer tends to get bought rather than built. Second, 13 and 27 per cent came from one commercial house measured against a benchmark grower. That is a credible pilot result and not yet a fleet statistic, and the winter deployments across Europe and North America are where the distinction gets settled. Third, if you are building autonomy for a physical agricultural asset, your realistic buyer is whoever already owns the grower relationship, so the question worth asking in year one rather than year eight is whether you are building the thing they will have to buy or the thing they can copy. Nine years from an accelerator cohort to an undisclosed trade sale is the honest price of the control layer.
Source.ag acquires Optimal to put greenhouse climate control on autopilot
The Amsterdam greenhouse AI company buys the London startup whose control software delivered 13 per cent more yield with 27 per cent less energy in a commercial Dutch house.
Source.ag, the Amsterdam artificial intelligence company for greenhouse growers, announced on 7 October 2026 that it has acquired Optimal, a London startup that runs greenhouse climate control autonomously. Financial terms were not disclosed. Optimal founder and chief executive Dave Hunter joins Source.ag as VP Autonomous Control, together with Optimal's engineering team. HortiDaily reported the deal on 9 October 2026.
Optimal's software takes direct control of heating, ventilation, screens, lighting, CO2 and irrigation through the grower's existing hardware. It combines physics models with machine learning to plan the growing environment several days ahead, then re-optimises in real time as the weather moves. In a commercial scale Dutch greenhouse the system delivered 13 per cent more yield with 27 per cent less energy than the benchmark Dutch grower. Optimal has run commercial operations in the Netherlands and Canada. It was founded in 2016 through the Entrepreneurs First programme, and its backers include Founders Fund, Backed VC, 2050 and Acequia Capital. HortiDaily reports that Hunter previously headed quantitative trading at Deutsche Bank and was a member of the machine learning group at Oxford.
Source.ag was founded in 2020 by Rien Kamman and Ernst van Bruggen. Its software monitors crops, forecasts harvests weeks ahead and automates irrigation. It runs in more than 500 greenhouses across 25 countries, supporting 2.5 billion euros of tomato, pepper and cucumber production a year, and the company says it works with 13 of the world's 25 largest high-tech growers. Source.ag has raised 50 million euros from investors including Astanor Ventures, Acre Venture Partners, Enza Zaden and Harvest House.
The stated logic is a closed loop in which Source.ag observes and predicts while Optimal acts. Kamman framed the deal as the company putting money behind the industry's talk of physical AI rather than only discussing it. Hunter argued that applied AI can work around the global shortage of skilled growers by running houses autonomously. Kees van Veen, chief executive of Agro Care, one of Europe's largest greenhouse tomato producers and a Source.ag customer, said growers need tools that hold climate, energy and quality steady rather than more hype, and that his company intends to be among the first to use autonomous control. The first autonomous deployments at Source.ag customers in Europe and North America go live this winter.
Why it matters for AgTech founders: this is an acquisition that buys the hard half of a product, not a customer list. Source.ag already had the data relationship, the installed base and the forecasting layer. What it did not have was the willingness to take the actuators. Optimal had the control layer and a published number from a commercial house, and roughly nine years of company life spent getting there. Three points travel out of this deal. First, sensing and forecasting sell far more easily than control, because a wrong forecast costs an argument and a wrong valve costs a crop, which is why the monitoring layer consolidates first and the control layer tends to get bought rather than built. Second, 13 and 27 per cent came from one commercial house measured against a benchmark grower. That is a credible pilot result and not yet a fleet statistic, and the winter deployments across Europe and North America are where the distinction gets settled. Third, if you are building autonomy for a physical agricultural asset, your realistic buyer is whoever already owns the grower relationship, so the question worth asking in year one rather than year eight is whether you are building the thing they will have to buy or the thing they can copy. Nine years from an accelerator cohort to an undisclosed trade sale is the honest price of the control layer.
