Syncware 2022: A Pivoting Case Study
Syncware had a truly transformative year in 2022. Depending on what time in the year asked us "what does Syncware do", we might give you different answers: 'making robots of different brands collaborate', 'saving electricity using sensors' or 'making warehouse pickers walk fewer kilometers'. Below we summarize the journey.
- Attracted investment from Y Combinator W22 batch - world’s most prestigious startup accelerator
- Pivoted our core business twice, and now we have found solid ground in the optimization of warehouses using Artificial Intelligence.
- Built an amazing new product from scratch under 6 months, with a crazy talented team <3
Why we pivoted away from offering system integration software for warehouse robots
Simply put, we could not manage the complexity of the sale. Too many stakeholders were involved in making a sale close, including:
- All departments of the end-customer needed to buy into the solution (Business, IT and Operations), and be educated about robots.
- Robot vendors needed to allow access to their robot systems and their ongoing deals.
- System integrators were ultimately in charge of whole project because customers want turn-key solutions that include all hardware, software and maintenance.
In our experience, each individual one could be overcome, but together they killed the sale. As two technical founders, inexperienced in enterprise sales, we could not pull this off.
While we still think that a middleware which can make robots of different brands collaborate is desperately needed, selling system integration software for robots without being in charge of the whole system integration process was one order too tall for us.
Why we pivoted away from using sensors for electricity savings
If you can connect a robot, you can also connect a few sensors. Yay! we could re-use our product. With energy saving being top of mind in Europe, we signed 3 LOIs for energy saving projects within 1 week after pivoting.
Heating, refrigeration, and compressed air are the three most energy consuming applications in factories. Saving electricity on anything else is futile compared to solving those sources first.
Heating is often done using natural gas, which leaves only refrigeration and air compressors for electricity saving. Broken air compressors are more energy guzzling than broken fridges.
Because air compressors consume so much power, they are already inspected within 6-12 months. This puts a cap on the potential savings of that can be achieved with monitoring.
Leading manufacturers of air compressors already started to produce models with sensors built in, as well as monitoring software, which shrinks the market for retrofitting existing machines.
Still, many factories have multiple brands of air compressors on site, and when they are connected in non-standard configurations, they are difficult to diagnose for faults.
Thus, unifying maintenance APIs and applying intelligence is an open opportunity in this space. Yet, it is a problem that is already being tackled by a few good maintenance API companies such as Makini.io.
We did not want to ape other companies.
Why we pivoted towards AI optimization of warehouses (and instant data analytics)
While biting our teeth on the energy saving business, a contact in the warehousing space reached out and mentioned that their company were interested in acquiring our company for the AI.
AI had always part of the game plan, as it built on my expertise from PhD. In our original plan of Syncware, we would first connect the robots, and then optimize the robot fleet using AI. Development of the fleet optimization AI had always been happening in the background.
Our AI was not yet ready, but it made us realize that we had never actively marketed our AI, but that there might be great demand! Perhaps we had overlooked a critical part of our solution.
We used June to validate this hypothesis with lead our current direction:
Finetuning our Business: AI + Data Analysis
Before implementing any AI solutions, it is crucial to first conduct thorough data analysis. This has two main reasons:
- AI solutions can often compete with other "low hanging fruit" optimizations that can be easily identified and fixed through data analysis.
- It is important to validate that the outcomes of the AI make sense and align with the data.
If you have any needs for:
- Instant data analytics
- Slotting optimization
- Order batching
- Warehouse layout design
Don't hesitate to reach out to us. Or, consider trying out our product!
We offer cash-back for unhappy customers too.