Peak Reviews

We have benefited a lot from the AI platform designed by Peak to meet our demands. Some of the noteworthy modules include the pre-built applications of AI for inventory management and customer segmentation.

The flexibility for such applications seems to be relatively constrained as well. The automation of the algorithms is the biggest issue when it comes to making them as specific as our particular business requirements and data assets.

AI applications developed by Peak is already pre-built to enhance every business processes with automation and optimization. The inventory optimization application has helped in reducing cases of stockout and also enhance the cash flow.

They are a great company to work with and have some very talented individuals. They are very eager to please and have been very good at dealing with some very difficult requests with tight deadlines.

They understand data extremely well, but at times have maybe just not quite had the business knowledge to go along with the data expertise, but this has improved a lot over time as they have grown.

We are looking at various modules with Peak, asset optimisation, cross selling/basket analysis, customer segmentation, logistics optimisation all to try and improve revenues, return on capital and provide some cost savings.

Peak have been full of ideas and suggestions in our project and have been consistently helpful and supportive. They've understood our ambitions very well and have aligned their priorities to them, so it feels like we're all part of one team.

I don't dislike anything about working with Peak. It'd be nice to meet more of the team there.

Try to work out which areas of operation you think you've got room for improvement on before you engage Peak (or any AIaaS vendor) - with so much possible in this area one of the biggest problems you'll face is deciding where to start. You need an area with rich data ideally, and where you can find incremental improvements that have a definite, measurable ROI.

Worked with PeakAI on logistics tools - they were able to make sense of large amounts of diverse data, and analyse it to generate powerful insights. They worked well with the client to refine the analysis and then turn a one-off analysis into an ongoing business tool, which many users can access to drill down into detail, and take action to address inefficiencies highlighted.

PeakAI are totally unphased by large amounts of complex data. They can quickly make sense of it, and have creative and helpful ways to visualise results. They understand the underlying business processes and questions, and so can make the results relevant and actionable.

Over the months, we refined our way of working together, to make sure we reviewed frequently how work was progressing - early on, we were sometimes slightly at cross-purposes, where PeakAI produced results which didn't directly answer the business's questions. We're now working together very effectively, to get the best out of Peak's technical expertise and the client's knowledge of their business and its issues.

Ensure you define the questions/requirements clearly. Meet/WebEx regularly, to ensure work-in-progress is reviewed and discussed frequently.

As a business we know we want to be at the leading edge of our market and know AI is part of that journey at a top level. Our aim was to experiment with AI to better understand the value from a commercial operational sense and how we can apply AI as part of our working practice rather than just a singular solution. AI as a service allows us to leverage AI as part of a solution to business goals.

The accessibility and adaptability of AI. Being able to access AI as a service allows us to concentrate on how AI can be used without creating the infrastructure, skill sets and knowledge from scratch. It allows us to bring senior business contacts on the journey to see the value of AI at an earlier stage.

The main challenges revolve around adapting our data and creating understanding between our commercial model and standard sales practices. Again this is a journey, and AI is not a silver bullet solution. The gulf between those who are new to working with data and processes and those who live it day to day takes some effort in bridging.

Have members of the team that are comfortable with data, data models and how to apply data to business solutions. Have patience whilst the data model is being calibrated. Bring in senior team members by showcasing the outputs of the data.