How Google Cloud Solutions Help Retail Firms to ABP(Always Be Pivoting)
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For years, retailers have been told that they must embrace a litany of new technologies, trends, and imperatives like online shopping, mobile apps, omnichannel, and digital transformation. In search of growth and stability, retailers adopted many of these, only to realize that for every box they ticked, there was another one waiting.
And then the pandemic hit, along with rising social movements and increasingly harsh weather. Some retailers were more prepared to take on these disruptions than others, which crystallized a new universal truth across the industry: the ability to adapt on the fly became the most important trait to survive and thrive.
Today’s retail landscape has surfaced both existing and new challenges for specialty and department store retailers. Approximately 88% of purchases previously occurred within a store environment. Now, it’s closer to 59%, with the remainder done online or through other omni methods.
With such constant change and upheaval, it can feel like the mantra now is ABP: always be pivoting.
The big question isn’t just how to maintain constant momentum and agility—it’s how to do it without sapping your workforce, your inventory, or your profits in the process. The pivot is now a given. What matters is how you do it.
Adapting requires a flexible base of technology that allows retailers to shift and scale seamlessly with the needs of the moment.
They need to be able to leverage real-time insights and enhance customer experiences rapidly, online and in the real world (not to mention the growing hybridization that’s AR and VR). They need to modernize their stores to power engaging consumer and associate experiences. They need to enhance operations for rapid scaling between full operations and digital-only offerings.
To help retailers achieve these goals and more, Google Cloud is honing a trio of essential innovations: demand forecasting that harnesses the power of data analytics and artificial intelligence; enhanced product discovery to improve conversion across channels; and the tools to help create the modern store experience.
In other words, here’s some of the biggest ways we’re ready to help you pivot.
Pivot point 1: Harnessing data and AI for demand forecasting with Vertex AI
One of the greatest challenges for retailers when building organizational flexibility is managing inventory and the supply chain.
We are in the midst of one of the worst global supply chain crises, stemming from soaring demand and logistics issues brought on by the pandemic. This crisis has only heightened the challenge retailers face when assessing demand and product availability. Even in normal times, mismanagement of inventory can add up to a trillion-dollar problem, according to IHL Group (costing $634 billion in lost sales worldwide each year, while overstocks result in $472 billion in lost revenues due to markdowns).
On the flipside, optimizing your supply chain can lead to greater profits. For instance, McKinsey predicts that a 10% to 20% improvement in retail supply chain forecasting accuracy is likely to produce a 5% reduction in inventory costs and a 2% to 3% increase in revenues.
Some of the challenges related to demand forecasting include:
- Low accuracy leads to excess inventory, missed sales, and pressure on fragile supply chains.
- Real drivers of product demand are not included, because large datasets are hard to model using traditional methods.
- Poor accuracy for new product launches and products that have sparse or intermittent demand.
- Complex models are hard to understand, leading to poor product allocation and low return on investment on promotions.
- Different departments use different methods, leading to miscommunication and costly reconciliation errors.
AI-based demand forecasting techniques can help. Vertex AI Forecast supports retailers in maintaining greater inventory flexibility by infusing machine learning into their existing systems. Machine learning and AI-based forecasting models like Vertex AI are able to digest large sets of disparate data, drive analytics and automatically adjust when provided with new information.
With these machine learning models, retailers can not only incorporate historical sales data, but also use close to real-time data such as marketing campaigns, web actions like a customer clicking the “add to cart” button on a website, local weather forecasts, and much more.
Pivot point 2: Enhanced product discovery through AI-powered search and recommendations
If customers can’t easily find what they are looking for, whether online or at the store, they will turn to someone else. That’s a simple statement, but one with profound impacts.
In research conducted by The Harris Poll and Google Cloud, we found that over a six month period, 95% of consumers received search results that were not relevant to what they were searching for on a retail website. And roughly 85% of consumers view a brand differently after an unsuccessful search, while 74% say they avoid websites where they’ve experienced search difficulties in the past.
Each year, retailers lose more than $300 billion dollars from search abandonment, or when a consumer searches for a product on a retailer’s website but does not find what they are looking for. Our product discovery solutions help you surface the right products, to the right customers, at the right time. These solutions include:
- Vision Product Search, which is like bringing the augmented reality of Google Lens to a retailer’s own branded mobile app experience. Both shoppers and retail store associates can search for products using an image they’ve photographed or found online and receive a ranked list of similar items.
- Recommendations AI, which enables retailers to deliver highly personalized recommendations at scale across channels.
- Retail Search, which provides Google-quality search results on a retailer’s own website and mobile applications.
All three are powered by Google Cloud, leveraging Google’s advanced understanding of user context and intent, utilizing technology to deliver a seamless experience to every shopper. With these combined capabilities, retailers are able to reduce search abandonment and improve conversions across their digital properties.
Pivot point 3: Building the modern store
Stores are no longer places for just browsing and buying. They must be flexible operation centers, ready to pivot to address changing circumstances. The modern store must be multiple things at once: a mini-fulfillment and return center, a recommendation engine, a shopping destination, a fun place to work, and more.
Just as retail companies had to embrace omnichannel, stores are now becoming omnichannel centers on their own, mixing the digital and physical into a single location. Retailers can use physical stores as a vehicle to deliver superior customer experiences. This will demand heightened levels of collaboration and cooperation between stores, digital, and tech infrastructure teams, building on the agile ways they have worked together.
In many ways, it’s about allowing our physical spaces to function more like digital ones. Google Cloud can help by bringing the scalability, security, and reliability of the cloud to the store, allowing physical locations to upgrade infrastructure and modernize their internal and customer-facing applications.
Think of it as when a new OS gets released for your phone. It’s the same small, hard box, but the experience can feel radically different. Now, extend that same idea to a digitally enabled store. With the right displays, interfaces, and tools at a given retail location, the team only needs to send an over-the-air update to create radically fresh experiences, ranging from sales displays to fulfillment or employee engagement.
Such an approach can enable streamlined experiences for both customers and store associates. For instance, when it comes to the modern store’s evolving role as a fulfillment or return center, cloud solutions can help drive efficiency in stores through automation of ordering, replenishment, and fulfillment of omnichannel order selection.
Similar tools for personalized product discovery online can be applied to customers in the store, helping them to browse and explore, or even create a tailored shopping experience.
The impact of store associates can be maximized by equipping them with technology to provide expertise that drives value-added customer service, as well as increasing productivity in stores by streamlining operations, thus lowering overhead cost. At the register, customers should be able to enjoy frictionless checkout while ensuring reliable, accurate, secure transactions.
Google Cloud can help retailers transform
The ability to adapt and pivot to meet today’s changing consumer needs requires that retailers rely on modern tools to obtain operational flexibility. We believe that every company can be a tech company. That every decision is data driven. That every store is physical and digital all at once. That every worker is a tech worker.
Google Cloud works with retailers to help them solve their most challenging problems. We have the unique ability to handle massive amounts of unstructured data, in addition to advanced capabilities in AI and ML. Our products and solutions help retailers focus on what’s most important—from improving operations to capturing digital and omnichannel revenue.
S4 Agtech Transforms Agriculture with Google Cloud
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Like countless other industries, farming is going digital and undergoing big changes—driven by access to more actionable information. The agriculture business can now gather and analyze georeferenced data from satellites, combined with data from IoT sensors in fields, crop rotation and yield histories, weather patterns, seed genotypes and soil composition to help increase the quantity and quality of crops.
This is essential for businesses in the agriculture industry, but it’s also critical to address growing food shortages around the world.
At S4, we create technology to de-risk crop production. We provide customers seeking agricultural risk management solutions with the tools to make better, data-driven decisions for their crop planning, based on machine learning and proprietary algorithms.
We interpret plant evolution on a global scale with predictive modeling and analytics, and offer super-efficient risk-transferring solutions. Our multi-cloud platform includes a petabyte-scale database, an open source stack, and—after 50 proof-of-concept evaluations—BigQuery for our data warehouse and the Cloud SQL database service to handle OLTP queries to our PostgreSQL database.
These PoCs included, among others, Microsoft Azure Data Lake Analytics, IBM Netezza, Postgres/PostGIS running on IBM bare-metal servers with SATA SSDs and on Google’s Compute Engine with NVMe disks, and on-premises memSQL, CitusData and Yandex ClickHouse.
Weeding out risk in an uncertain market
According to recent research, climate extreme events like drought, heat waves, and heavy precipitation are responsible for 18-43% of global variation in crop yields for maize, spring wheat, rice, and soybeans. This is a clear trend for other crops as well. Such variation poses risks of food shortages as well as large financial risks to farmers, insurers, and regions dependent on successful crop yields. Also, it creates vast humanitarian difficulties.
Our mission at S4 is to help de-risk crop production by matching the right data with analytics tools so farmers and other participants in the agricultural value chain can plan better, resulting in more reliable food supplies.
In a nutshell, we create indices out of biological assets. These indices measure yield losses on crops that are caused by the effects of weather and other factors, which are then used as underlying assets for products, such as swap/derivative contracts and parametric insurance policies, to transfer risk to the financial markets.
We enable insurers and lenders to buy and sell agricultural risks through the futures market. Also, our other products help farmers and seed and fertilizer companies provide customized genotype recommendations and fertilization requirements. This helps to optimize planting by geography, resources, and crop species, monitor phenological, pests and humidity evolution throughout the crop season, and estimate yields.
Local communities benefit from S4’s technology, as the ability to manage weather risks allows farmers to stabilize their cash flows, invest more to produce more with fewer risks, and develop in a more sustainable manner.
Growing data sources, reducing costs, accelerating performance
With the volume of diverse data sources and analytical complexity both growing at a very fast pace, we decided that using a major cloud services provider with a broad roadmap and global partnerships would be beneficial to S4’s future evolution.
At the same time, we wanted to bring our services to users faster and cut costs by consolidating our on-premises technology stack. When we started evaluating providers, our leading criteria included a powerful geospatial database and data analytics tools along with excellent support, all at a competitive price. GCP prevailed in nearly all criteria categories among the 50 companies we measured.
Our previous platform architecture included a hybrid relational database that used Compute Engine for virtual machines and Cloud Storage for database backup. The RDBMS was slow. Maintaining our own data warehouse was complex and expensive.
We wanted to use machine learning and neural networks, but couldn’t do so easily and affordably. The complexity of that system meant that products or services requiring small changes or additions to the data model translated to expensive expansions of infrastructure or project time.
Also, agronomical or product teams couldn’t test these changes by themselves, always requiring the intervention on no small part of the IT team, which led to further delays.
We added GCP services like BigQuery as S4’s cloud data warehouse and use BigQuery GIS for geospatial analysis, Cloud Dataflow for simplified stream and batch data processing, and Cloud SQL for queries to the S4 database platform, which have all made a huge impact on our services and bottom line.
Database and analytics costs have decreased by 40% and customers are receiving our analytical results 25% faster. In addition, we’ve eliminated the time-consuming downloading of images, reducing storage and processing costs by 80%, because we no longer need expensive tools licenses, and have greatly reduced classification processing times.
Our customers working in the agriculture industry are also benefiting from this infrastructure change. They are now able to speed up their data analytics using our GCP-based platform.
“S4 products and technologies unlock the full potential of satellite imagery for crop prescriptions, monitoring and yield estimates,” says Nicolás Loria, Manager of Marketing Services, Southern Cone, Corteva Agriscience.
“We’ve worked with S4 for the last three (and starting year number four) crop seasons as its team capabilities, data integration capacities, and analytics insights have allowed Corteva to perform an entire new solution. Thanks to S4’s customized 360° approach, fast response and delivery times, we have safely outsourced our remote crop analytic technical needs.”
Also, this new architecture has allowed us to scale our models and databases with almost no limits, at a fraction of the cost vs. the previous models.
We’ve saved a lot of time on executing processes and reduced work needed by our internal teams to do certain tasks, like preparing images, converting them, validating results, and more. Using Google Earth Engine has decreased the execution time of daily tasks anywhere from 50% to 90% of the previous time, going from an average time of 30 minutes to between four and 15 minutes, depending on the task.
In addition to saving money and time, we are able to focus on innovation with the GCP performance and features we’re using. We’re able to seamlessly add satellite data to analytics using both public datasets and our own private data, and deliver GIS data management, analytics, crop classification and monitoring in real time.
We can do semi-automatic crop classification and classification using spectral signatures with Google Earth Engine. Later this year, we’ll be using neural networks for pattern recognition and machine learning in new applications to improve crop yields and fine-tune risk models. And using GCP and Google Earth Engine infrastructure means we can run models for customers in South America and around the world, since Google Earth Engine has global satellite imagery available.
We’ve heard from our customer Indigo Argentina that they’re able to bring customers data insights faster.
“We are working with S4 in the development of two different applications for satellite crop monitoring and yield assessment,” says Carlos Becco, CEO, Indigo Argentina. “S4’s technology allowed us to manage and analyze multiple sources and layers of information in real time, letting us uncover valuable insights in Indigo’s own microbiome technologies, and at a very competitive cost.”
Analytical products and app development thrive with GCP
With GCP, we are updating and improving algorithms that we built manually with machine learning processes to develop drought indices for upcoming crop seasons. Algorithms can recognize specific phases of crop phenology (e.g., bud burst, flowering, fruiting, leaf fall) and correlate them with photosynthetic activity, light, water, temperature, radiation, and plant genetics factors. Other analytical products like crop monitoring, pre-planting recommendations, financial scoring, and yield estimation can now do a lot more for users by offering multiple layers and datasets, faster image processing, and real-time access via APIs.
We also replaced our bare-metal S4 app deployment with the App Engine serverless application platform. It provides tighter integration between the S4 platform and our BigQuery data warehouse for integration with marketplaces and third-party solutions.
We get all of these Google Cloud features with all the benefits of managed cloud services, from multiversioning and security to automatic backups and high availability.
At S4, we trust technology to decode plant growth and help protect farmers and their communities from climate change. With growing food shortages due to increasing populations and intensifying weather, data and analytics can have a huge impact in lowering financial risks and improving agricultural yields. It’s one sector where cloud, database, analytics, and other technologies are combining to improve business outcomes and affect the lives of billions of people. Learn more about S4’s work and learn more about data analytics on Google Cloud.
Upgrade Your Contact Center with Knowlarity’s AI-powered Speech Analytics for Higher CX
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Did you know, everyday about 56 million hours worth of phone conversations, equalling to 420 billion spoken words are handled by contact centers? Knowlarity, a renowned cloud business communication service provider with nearly 6,000 customers and over a million virtual users, leverages AI-powered speech analytics that offer insights to gauge customer preferences and emotions, campaign performance, agent’s effectiveness and much more. Knowlarity’s programmatic speech analytics platform is built with Google Cloud to optimize contact center performance by transcribing and analyzing millions of calls to impact savings, operations, CX, customer loyalty and retention, and revenue generation.
Download the e-Book to learn more about Knowlarity’s speech analytics for your business’ contact centers and elevate your agents’ performance by leveraging ML, natural language processing (NLP) and AI capabilities.
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Metro Consolidates 100 SAP Instances on Google Cloud
German retailer Metro, with operations in 25 countries across Europe and Asia and employing over 150,000 people, decided to consolidate its 100 SAP instances into one SAP S/4HANA system running on the Google Cloud Platform.
This helped the company lower costs, increase efficiency, and improve customer experience. In addition, the company is using Google BigQuery and advanced analytics to increase its sales.
Watch Timo Salzsieder, Metro CIO/CSO, explain the company’s journey with SAP on Google Cloud.
Quantum Metric Increases Business 10-fold
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At Quantum Metric, we’re in the business of bringing our customers business insights that are based on customer experience data and analytics for mid-market and Fortune 500 companies.
Our software, powered by big data, machine intelligence, and Google Cloud, helps our customers identify, quantify, prioritize and measure opportunities to improve digital experiences.
As companies move to a more agile product lifecycle, including continuous deployment and continuous integration, they’re finding that it’s critical to receive perpetual quantified feedback and insights from their data in real time to understand where the largest opportunities exist.
Each year, billions of customer interactions are captured through browsers or mobile apps on PCs, tablets, and mobile devices. This data, fed into the Quantum Metric platform, can show if a customer had a password problem they couldn’t solve or struggled when trying to purchase something and abandoned their cart.
It also can show if the customer tried in vain to complete an online change to their service provider’s subscription, to reach tech support, or couldn’t find the size or color they were looking for while shopping online.
Most importantly, the Quantum Metric platform quantifies the business value of the issue, helping organizations prioritize where they can make the largest impact to their business.
Success overwhelms our initial architecture
Initially, the Quantum Metric experience analytics software ran on a MySQL open source relational database management system (RDBMS). The MySQL RDBMS worked great for simple queries, when there was a specific question to ask of the data.
Soon, though, we knew we needed to offer more advanced data science capabilities. Our bigger customers wanted to ask questions across very large data sets—days, weeks, months, and years worth of data. They wanted to pose iterative questions using complex filters to answer their most challenging business questions.
With more complex queries across more data, response times from our RDBMS went from 100 to 500 milliseconds to as long as 20 minutes.
That delay was slowing down our ability and time to insights, which also reduced the value we could provide to our customers, since iterative exploration and analysis requires real-time query responses. Because of the need for real-time responses, there were certain questions that we just weren’t able to ask of the data. It became clear that we needed a much more robust data warehouse solution.
There were also operational challenges with MySQL and massive-scale data ingestion. We spent a lot of time into the wee hours of the night and morning handling errors and recovering databases.
We tried to address these challenges by sharding, partitioning, and indexing the data to optimize for the types of questions customers were asking. But the problems were escalating and happening more often, from once a month across the customer base to monthly for at least 20 different customers.
We could tune the platform for today and tomorrow’s workload, with good guesses at where indexes could be used, but we simply couldn’t continue to horizontally scale MySQL in a cost-efficient and operationally efficient manner.
Speed breeds innovation
Once we started exploring options that could better scale with our business, we looked at NoSQL technologies like Cassandra (a partitioned row-store database), MySQL’s Column Store (a columnar store database), and Vertica (a columnar store database)—each with unique ways of handling data storage and accessibility.
But with high volumes of complex queries across large data stores, all of these solutions began to fail, bogged down with multiple, simultaneous users. We could have solved the problems with more raw compute and storage, but it would have been prohibitively expensive to run and require a large team to operate.
We then decided to try BigQuery, and it was transformative.
We connected our front end to BigQuery via APIs. Once data is 15 minutes old, it is automatically extracted, loaded, and transformed (ETL) to BigQuery.
We continuously update the legacy MySQL RDBMS so its data is integrated with BigQuery data when queries require real-time data. Most query response times are within 100-200 milliseconds, matching what we initially experienced with MySQL.
When traffic from our customers scales up, we can now scale on-demand to accommodate it, thanks to BigQuery’s hundreds of thousands of CPUs. Our customers no longer run into slow response times, and we’ve gained confidence that we can offer them—and their users—advanced insights and better experiences without delay.
More importantly, with this scale of query power, we were able to build data science algorithms into the platform, which iteratively query BigQuery based on the results, and help quantify the impact of a specific issue to a specific segment of users. Adding these capabilities was possible because of the massive scale of BigQuery.
In addition to new insights and fast response times, we wanted our customers to be able to ask complex questions using very simple language.
For example: “Show me high-loyalty customers, located in specific geographic areas, who visited the web site at least five times, based on specific campaigns, and never booked a seat on a flight.”
This was exactly the kind of query that was used by a major U.S. airline to understand the multi-million dollar impact of a failure affecting their most valuable customers: their high-loyalty members.
And this was all done while maintaining the highest standard of care of customer data and privacy by default, using multiple layers of encryption of data in transit, at rest, and a unique military-grade encryption approach. This approach encrypts PII, including even session cookies, with a RSA-2048 key available only to a select few and used for use cases such as fraud analysis.
It’s no exaggeration to say that BigQuery has totally transformed our business. It provides the petabyte scale and speed we were missing, in addition to taking care of operational maintenance, a task that was burying our team with MySQL.
We’re now able to support some of the largest companies in the world that require real-time, petabyte-scale analytics. That lets them serve more customers faster with higher quality, and take advantage of BigQuery’s power and scale to innovate.
There are other cloud solutions that can address petabyte analytics, but the most unique value proposition of BigQuery was its on-demand scaling and operational management, with extremely cost-effective pay-as-you-go billing. While today we are at a scale where we have round-the-clock querying needs, our early days had very sporadic query loads where we needed instant scale, then a long lull of nothing. The unique business model of BigQuery’s pay-by-bytes-scanned allowed us to have access to a massive-scale querying platform without breaking the bank.
Using BigQuery powers better customer experience and reduces purchasing friction
Among the many features of Quantum Metric is the ability to replay online customer sessions. In the example below from a mobile e-commerce site, each action is displayed chronologically. Why did this customer’s transaction fail?
Diving deeper, the session replay shows that the user tried to change the item quantity in the checkout cart, which resulted in a failed API call. Powered by BigQuery, Quantum Metric can then show how many other end users had this issue, with a simple click of “Show More Errors Like This.”
With BigQuery’s massive scale, Quantum Metric will then quantify the impact of that issue, so companies can prioritize which issues need attention immediately. If this is the issue that’s impacting the business the most, our customer can use a single click to open a Jira ticket, forwarding the discovery to their product and engineering teams. Those teams can then re-engineer the experience in near-real time, addressing the failed API call and cutting out the frustrating time it takes for engineers to reproduce the issue.
Once we had a powerful back end in BigQuery, we realized that Quantum Metric’s platform could be used to ask complex questions from vast datasets. We built some of the processes that data scientists use to formulate those queries right into our product.
For example, we added one series of processes to our platform to help customers understand whether a suspect issue is really impacting end-user experience. Is it something that should be prioritized and fixed? Does this really affect the user experience? Does it have financial impact? These and other questions can be pre-defined as a complex query in Quantum Metric to let our customer quickly gain insight on how an issue is impacting the business. Customers were blown away when they heard this was possible. It was the holy grail for what they were looking for in data science. It really sets us apart from our competition.
Today, with every company heavily dependent on data, those companies that can uncover and act on insights fastest are the ones that will succeed. BigQuery gives us the data warehouse platform we need to provide our customers with fast, reliable technology tools. It frees us from having to deal with the minutiae of technology infrastructure operations, so we can focus on finding and extracting the magic in customer data. With the power and scale of BigQuery, combined with the real-time capture of every user experience with 100% fidelity, we’re able to offer a self-service analytics platform that provides insights into digital journey friction points and acts as the indisputable arbitrator of truth.
Startup Success Blueprint: Insights on Cloud Provider Selection from One AI
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From the newsroom to the boardroom, everywhere we turn these days the topic of conversation is artificial intelligence (AI). From the smallest startups to the largest enterprises, every business is looking for ways to incorporate generative AI technology into their products or services.
Generative AI is a new breed, able to not only discern patterns in data but generalize from them and create new data—and it’s moving fast. With new technologies rapidly emerging, there’s a lot to consider when choosing a tech stack that’s right for your startup.
Our mission is to empower startups to build generative AI applications quickly, efficiently, and responsibly. In addition to our technology, we offer a variety of fresh educational and consulting initiatives, as well as comprehensive plans tailored to specific industry applications.
At the recent Google Cloud Startup Summit, leaders from One AI and MongoDB weighed in on various decisions startups face when choosing a development platform to integrate AI into their products. (You can watch the full conversation here.) They shared key insights into the challenges startups face when applying AI technology to real-world business and product use cases.
In this blog, we’ll explore why One AI – a platform that empowers businesses to deploy tailored AI solutions – relies on MongoDB on Google Cloud for performance, scale, functionality, and TCO. We will also cover the things that startups should keep in mind when building their generative AI stack.
Fine-tuning generative AI for startups
First, let’s start with a little background.
AI-based capabilities have been around for decades now, but generative AI is distinct. It’s a more mature version of AI, powered by models pre-trained on very large datasets composed of massive quantities of images, text, and data. These models are known as foundation models, and they include large language models (LLMs), text-to-image models, multimodal models, and more. Foundation models let intelligent applications generate new images, text, and data based on queries or prompts. This in turn allows companies to deploy those capabilities in their products and services faster since they don’t have to retrain the whole AI from scratch.
Over the course of their diverse startup careers, Amit Ben, CEO at One AI and his team have built AI-based capabilities from the ground up for various products in various fields.
“And each time, we had to rebuild the tech stack over again,” Amit explains. “With the advent of generative AI, startups now have the ability to deploy much faster — with a lower TCO and higher confidence — and deliver the capabilities they need into their products and services. It finally makes sense for every company to have AI in its product portfolio.”
“For us to be able to focus on that,” Amit adds, “we need to make sure we have a rock-solid foundation that we can build on.”
That foundation is MongoDB on Google Cloud.
Helping startups build fast and with flexibility
Startups can scale from ideation to growth with Google Cloud’s global availability, market-leading sustainability, and the same zero-trust security model that Google itself depends on.
With MongoDB, startups can take advantage of iteration cycles that are 3-5X faster, reduce sprawl and complexity, and benefit from the scalable infrastructure and advanced analytics tools on Google Cloud.
With MongoDB on Google Cloud, Amit and his team are confident they can adapt to new schemas, to new data, and to the scale they need for both writing and reading, all while operating on a scalable platform and infrastructure they can rely on for the long haul.
A common mistake for startups is turning to niche, single-point solutions. But this can backfire when they realize their solution doesn’t provide the security, scalability, and performance they need to grow.
Additionally, startups tend to have tight iteration cycles as they find their ideal product market fit. The ability to build fast and with improved flexibility is a key differentiator in a startup environment.
From cutting-edge automation to rock-solid redundancy and performance, there are many reasons why startups choose MongoDB on Google Cloud. And now, a dedicated partnership helps startups like One AI scale more quickly, more securely, and more successfully.
Google Cloud and MongoDB for startups
Choosing the right technology to accelerate time to market is critical to a startup’s success. Not only is it easy to get started, but Google Cloud and MongoDB also provide the foundation for users to scale without limits — so startups can focus on innovating and growing their businesses.
Learn more about the powerful startup programs available from Google Cloud and MongoDB.
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