The app economy has enabled a huge range of unique business models to flourish. One such model is online food ordering and delivery services, in which apps leverage geo-location data to aggregate local food choices and offer personalized options to consumers.

A leading company in this space is Just Eat. Launched in the UK in 2001 with a vision of ‘serving the world’s greatest menu. Brilliantly.’ The company has capitalized on the popularity of online food delivery and grown its presence across 12 markets. 

Just Eat acts as an intermediary between take-out food outlets and hungry customers, giving local restaurants access to a broader base of potential diners, while providing consumers with an easy and secure way to order and pay for food from their favourite restaurants.

Today the company helps 27 million customers find food from more than 112,000 restaurants—everything from homemade Italian pasta, to Chinese noodle bowls, to fish-and-chips. 

Data is the fuel of Just Eat’s rapid growth, but it wasn’t always looked at that way. In its early days, Just Eat struggled with the deluge of information and faced fragmentation across its systems. In fact, the company realized its legacy data vendor wasn’t capable of ingesting 90 percent of the data produced by its food platform. This was incredibly frustrating for Just Eat’s analysts and data scientists, who had to waste time cleaning up sources instead of leveraging the data to create a better user experience. 

Just Eat turned to Google Cloud, and now uses machine learning (ML) to power sophisticated consumer recommendations on both its app and website. It also makes heavy use of features offered by Google Cloud Platform, including BigQuery for running analytics on its customer data set and Cloud Pub/Sub for messaging app users with relevant offers in real-time. 

Having all of Just Eat’s data in one platform has translated into real value for its customers. With Google Cloud tools, Just Eat has created its own proprietary Customer Ontology framework, which today contains 5.5 billion features that better understand consumers’ behavior and food habits, and provides insights into previous visits.

Just Eat recently created an “Adventurous Index” to map its customers according to their ordering habits, enabling them to tailor their marketing and user experiences. For example, mid-adventurous customers are shown a choice of restaurants that serve their most ordered cuisine, while adventurous customers can choose from restaurants that serve a wider variety. This not only has prompted consumers to be more adventurous with their choices, but also has led to more business at a more diverse set of restaurants.

Matt Cresswell, Director of Customer Platforms at Just Eat said that Google Cloud has become integral to its product delivery: “Consumer food choice is a hugely nuanced topic. We know that individuals have their own unique journeys when they use Just Eat. We’ve sought to create a truly one-to-one relationship with every customer. The changes we’ve made to the platform mean they can access the dishes they enjoy at the touch of a fingertip, and find inspiration to discover new dishes they’ll love. We’re grateful to Google Cloud for helping us support our customers on their culinary explorations.”

“Cloud Wisdom Weekly: for tech companies and startups” is a new blog series we’re running this fall to answer common questions our tech and startup customers ask us about how to build apps faster, smarter, and cheaper. In this installment, we explore how to leverage artificial intelligence (AI) and machine learning (ML) for faster innovation and efficient operational growth.

Whether they’re trying to extract insights from data, create faster and more efficient workflows via intelligent automation, or build innovative customer experiences, leaders at today’s tech companies and startups know that proficiency in AI and ML is more important than ever.

AI and ML technologies are often expensive and time-consuming to develop, and the demand for AI and ML experts still largely outpaces the existing talent pool. These factors put pressure on tech companies and startups to allocate resources carefully when considering bringing AI/ML into their business strategy. In this article, we’ll explore four tips to help tech companies and startups accelerate innovation and reduce costs with AI and ML.

4 tips to accelerate innovation and reduces costs with AI and ML

Many of today’s most innovative companies are creating services or products that couldn’t exist without AI—but that doesn’t mean they’re building their AI and ML infrastructure and pipelines from scratch. Even for startups whose businesses don’t directly revolve around AI, injecting AI into operational processes can help manage costs as the company grows. By relying on a cloud provider for AI services, organizations can unlock opportunities to energize development, automate processes, and reduce costs.

1. Leverage pre-trained ML APIs to jumpstart product development

Tech companies and startups want their technical talent focused on proprietary projects that will make a difference to the business. This often involves the development of new applications for an AI technology, but not necessarily the development of the AI technology itself. In such scenarios, pre-trained APIs help organizations quickly and cost-effectively establish a foundation on which higher-value, more differentiated work can be layered.

For example, many companies building conversational AI into their products and services leverage Google Cloud APIs such as Speech-to-Text and Natural Language. With these APIs, developers can easily integrate capabilities like transcription, sentiment analysis, content classification, profanity filtering, speaker diarization, and more. These powerful technologies help organizations focus on creating products rather than having to build the base technologies.

See this article for examples of why tech companies and startups have chosen Google Cloud’s Speech APIs for use cases that range from deriving customer insights to giving robots empathetic personalities. For an even deeper dive, see

• our AI product page to explore other APIs, including Translation, Vision, and more;
• and the Google Cloud Skills Boost for ML APIs.

2. Use managed services to scale ML development and accelerate deployment of models to production

Pre-trained models are extremely useful, but in many cases, tech companies and startups need to create custom models to either derive insights from their own data or to apply new use cases to public data. Regardless of whether they’re building data-driven products or generating forecasting models from customer data, companies need ways to accelerate the building and deployment of models into their production environments.

A data scientist typically starts a new ML project in a notebook, experimenting with data stored on the local machine. Moving these efforts into a production environment requires additional tooling and resources, including more complicated infrastructure management. This is one reason many organizations struggle to bring models into production and burn through time and resources without moving the revenue needle.

Managed cloud platforms can help organizations transition from projects to automated experimentation at scale or the routine deployment and retraining of production models. Strong platforms offer flexible frameworks, fewer lines of code required for model training, unified environments across tools and datasets, and user-friendly infrastructure management and deployment pipelines.

At Google Cloud, we’ve seen customers with these needs embrace Vertex AI, our platform for accelerating ML development, in increasing numbers since it launched last year. Accelerating time to production by up to 80% compared to competing approaches, Vertex AI provides advanced end-to-end ML Ops capabilities so that data scientists, ML engineers, and developers can contribute to ML acceleration. It includes low-code features, like AutoML, that make it possible to train high performing models without ML expertise.

Over the first half of 2022, our performance tests found that the number of customers utilizing AI Workbench increased by 25x. It’s exciting to see the impact and value customers are gaining with Vertex AI Workbench, including seeing it help companies speed up large model training jobs by 10x and helping data science teams improve modeling precision from the 70-80% range to 98%.

If you are new to Vertex AI, check out this video series to learn how to take models from prototype to production. For deeper dives, see

• this article about Vertex AI’s role in an ambitious project to measure climate change with AI;

• BigQuery has built-in Machine Learning (ML) and Analytics that you can use to create no-code predictions using just SQL queries.
• this blog about how Vertex AI and BigQuery work together to make data analysis easier and more powerful;
• and this blog about Example-based explanations, one of our most recent updates to make model iteration more intuitive and efficient.

3. Harness the cloud to match hardware to use cases while minimizing costs and management overhead

ML infrastructure is generally expensive to build, and depending on the use case, specific hardware requirements and software integrations can make projects costly and complicated at scale. To solve for this, many tech companies and startups look to cloud services for compute and storage needs, attracted by the ability to pay only for resources they use while scaling up and down according to changing business needs.

At Google Cloud, customers share that they need the ability to optimize around a variety of infrastructure approaches for diverse ML workloads. Some use Central Processing Units (CPUs) for flexible prototyping. Others leverage our support for NVIDIA Graphics Processing Units (GPUs) for image-oriented projects and larger models, especially those with custom TensorFlow operations that must run partially on CPUs. Some choose to run on the same custom ML processors that power Google applications—Tensor Processing Units (TPUs). And many use different combinations of all of the preceding.

Beyond matching use cases to the right hardware and benefiting from the scale and operational simplicity of a managed service, tech companies and startups should explore configuration features that help further control costs. For example, Google Cloud features like time-sharing and multi-instance capabilities for GPUs — as well as features like Vertex AI Training Reduction Server — are built to optimize GPU costs and usage.

Vertex AI Workbench also integrates with the NVIDIA NGC catalog for deploying frameworks, software development kits and Jupyter Notebooks with a single click—another feature that, like Reduction Server, speaks to the ways organizations can make AI more efficient and less costly via managed services.

4. Implement AI for operations

Besides using pre-trained APIs and ML model development to develop and deliver products, startup and tech companies can improve operational efficiency, especially as they scale, by leveraging AI solutions built for specific business and operational needs, like contract processing or customer service.

Google Cloud’s DocumentAI products, for instance, apply ML to text for use cases ranging from contract lifecycle management to mortgage processing. For businesses whose customer support needs are growing, there’s Contact Center AI, which helps organizations build intelligent virtual agents, facilitate handoffs as appropriate between virtual agents and human agents, and generate insights from call center interactions. By leveraging AI to help manage operational processes, startups and tech companies can allocate more resources to innovation and growth.

Next steps toward an intelligent future

The tips in this article can help any tech company or startup find ways to save money and boost efficiency with AI and ML. You can learn more about these topics by registering for Google Cloud Next, kicking off October 11, where you’ll hear Google Cloud’s latest AI news, discussions, and perspectives—in the meantime, you can also dive into our Vertex AI quickstarts and BigQuery ML tutorials. And for the latest on our work with tech companies and startups, be sure to visit our Startups page.

Many people start a small business that aligns with their passions, but soon discover the day-to-day running of a business is very different than anticipated. Dealing with accounting, finance, and other daily activities can quickly overwhelm even the most promising of new businesses.

Recognizing the unique challenges facing small businesses, we founded Beany. With our personalized accounting platform, small business owners can relax knowing that we will look after all their compliance and advisory needs. Business owners who use Beany also benefit because they end up freeing more time to focus on what they enjoy and do best—likely the main reasons they started their business in the first place. We prepare financial statements, minimize tax and keep an eye that our clients are paying the right amount at the right time. We also advise on business purchase and sale, valuations and forecasting for banks. In fact, we do everything that a business owner needs from their accountant, including a help desk filled with domain experts to provide free & unlimited advice and support. 

As we expand our team, we’ll continue to introduce new solutions, services, and integrations that make it even easier for small businesses to prioritize business planning, balance budgets, and keep up with ever-changing tax laws. We also plan to launch Beany in new markets and extend our global footprint beyond New Zealand, Australia, and the UK.    

Migrating and scaling Beany

Beany was initially developed and hosted on a virtual private server (VPS) with limited capabilities. As our subscriber base grew, we realized we needed a more scalable, secure solution to eliminate downtime and deliver a reliable customer experience. We also understood it would be challenging for our small engineering team to cost-effectively test and deploy new features without a more agile development environment. 

With that in mind, we chose the secure-by-design infrastructure of Google Cloud to power Beany and help us scale our innovative accounting platform. We now use Compute Engine to cost-effectively create virtual machines (VMs) that automatically select optimal amounts of processing and memory. 

In addition, we encrypt, store, and archive everything on highly secure Cloud Storage, leveraging a combination of solid-state drives (SSDs) and hard disk drives (HDDs) for hot, nearline, and coldline data. With Cloud Storage, we can replicate data between regions in under 15 minutes to enable rapid recovery and business continuity. This has been really useful moving images to data centers on the other side of the planet quickly as we primarily operate out of Sydney and London. 

We also connect, create, and collaborate on Google Workspace using Google Gmail, Docs, Calendar, and Meet. Beany smoothly integrates with Google Sheets to provide live reporting from our application direct to our sales, marketing and support staff. It also makes sharing account data with our accounting clients easy and secure.

Analyzing financial data with Google Cloud AI and machine learning

Joining the Google for Startups Cloud Program gave us immediate access to Google Cloud credits which we use to cost-effectively trial and deploy additional Google Cloud solutions. We continue to evaluate Cloud Code which will help our developers more efficiently create, deploy, and integrate applications directly on Google Cloud. We’re also exploring how Google Cloud AI and machine learning products such as Vertex AI and AutoML can further revolutionize accounting with advanced financial analyses and end-to-end automation of business processes.

We can’t wait to see what we accomplish next as we grow our team and introduce new services and solutions that enable small businesses to streamline operations, lower costs, and increase profits. It’s exciting to help small business owners manage accounting and financial planning so they can spend more time doing what they love and realize their business vision.

Beany team members

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

San José is one of the most diverse U.S. cities, with residents speaking more than 100 languages. Several years ago, we set out to improve city community interactions through more equitable service management and delivery. This demanded a new approach to automating the intake of requests from a majority population whose first language is not English.

We first created the 311 portal and app, which was an important step as we effectively separated resident service requests from emergencies. The way we describe it to our citizens, you call 311 for a burning question, and 911 for a burning building. In the last fiscal year, San José 311 received nearly 210,000 contacts by phone and an additional 211,000 service requests through the SJ 311 app. 

Through the portal and app, we gave citizens an omnichannel experience enabling them to interact with the city to request improvements, access useful information, and get emergency help when they need it. 

In order to truly serve our diverse communities, we recognized language translation services would be required to offer truly equitable services to everyone. That’s when we started working closely with SpringML, Google Cloud, and other partners with involvement from our Mayor and City CIO.

Building public services with community engagement in mind

When we first rolled out the My San José website and mobile app, we used an out-of-the-box translation service that ended up not working. It had poor accuracy and did not meet our needs to provide all citizens with coherent services. After looking at many other options, we decided to partner with SpringML and Google Cloud to leverage the AutoML Translation with other technologies such as our virtual agent.

SpringML was selected through an open RFP process, and helped us to build and optimize our integrations, interfaces, and more between several systems, making the app and website more intuitive to manage. SpringML delivered the product we needed on time and up to specifications, and additional value came from the training sessions they provided to our team. This enabled us to understand everything we could do with AutoML and opened the door to other enhancements such as simplifying the vernacular used with our residents, making government access easier to navigate regardless of natural language spoken. 

After establishing the My San José app’s translation capabilities using AutoML, SpringML also helped us incorporate Dialogflow virtual agents. Dialogflow also positions us to make modifications with our own staffing practices – something that has become increasingly important amid the frequent changes in service levels from COVID-19 response in the past year.

Responding to community needs

With the app up-and-running, our next step was to bring in community members to help with testing, improvements, and more. We wanted the app and the website to not just be something we provided to the community, but rather something they helped us build so they would readily adopt it.

Thanks to the greater accuracy of translation supported by Google Cloud services, we were able to leverage the expertise of a small pool of community members to evaluate translations. AutoML Translation and Glossary proved to be a powerful combination that pushed us closer to our goals. 

Our primary targets were Spanish and Vietnamese translations. We are now seeing 90 percent accuracy in automated Spanish translations while Vietnamese translations continue to improve. We continue to work to simplify the language used in these services, which makes a big difference in terms of ensuring optimal language accessibility.

This work includes best serving our community members who primarily use phones to get in touch with us through 311 services. Using Google Cloud Contact Center AI, we have been able to effectively manage the calls we receive 24×7 and communicate with residents who speak Spanish as well as English. No matter which channel one of our residents choose to use to reach out, we can serve them efficiently. 

A well-timed release

We’re proud of the work we’ve done. We’ve made many government services available to our community 24 hours a day, 7 days a week — accessible through many channels. Regardless of a person’s native language, the consistency of experiences enjoyed by everyone is improving every day thanks to AI. We’re also actively incorporating more language translation capabilities to better serve more people.

While we began this process several years ago, the recent integration of machine learning language translation with our customer relationship management system in late 2020 was very well-timed because we were able to incorporate this into our COVID-19 pandemic response. 

We’re also beginning to work with other municipalities across the U.S. to share some of the lessons we’ve learned and success we’ve seen in hopes of furthering more equitable citizen services far beyond our City limits. 

We are excited to continue working with SpringML, Google Cloud, and other partners to improve our city and the equity and quality of services that our residents enjoy.

Learn more about how you can work with a Google Cloud Partner here.