Over the past decade, artificial intelligence has evolved from experimental prototypes and early successes to mainstream enterprise use. And the recent advancements in generative AI have begun to change the way we create, connect, and collaborate. As Google CEO Sundar Pichai said in his keynote, every business and organization is thinking about how to drive transformation. That’s why we’re focused on making it easy and scalable for others to innovate with AI.

In March, we announced exciting new products that infuse generative AI into our Google Cloud offerings, empowering developers to responsibly build with enterprise-level safety, security, and privacy. They include Gen App Builder, which lets developers quickly and easily create generative chat and enterprise search applications, and Generative AI support in Vertex AI, which expands our machine learning development platform with access to foundation models from Google and others to quickly build, customize and deploy models. We also introduced our vision for Google Workspace, and delivered generative AI features to trusted testers in Gmail and Google Docs that help people write.

Last month we introduced Security AI Workbench, an industry-first extensible platform powered by our new LLM security model Sec-PaLM, which incorporates Google’s unique visibility into the evolving threat landscape and is fine-tuned for cybersecurity operations.

Today at Google I/O, we are excited to share the next steps not only in our own AI journey, but also those of our customers and partners as well. We’ve already seen a number of organizations begin to develop with and deploy our generative AI offerings. These organizations have been able to move their ideas from experimentation to enterprise-ready applications with the training models, security, compute infrastructure, and cost controls needed to provide their customers with transformative experiences. Our open ecosystem, which provides opportunities for every kind of partner, continues to grow as well. And we are also pleased to share new services and capabilities across Google Cloud and Workspace, including Duet AI—our AI-powered collaborator—to enable more users and developers to start seeing the impact AI can have on their organization.

Customers bringing ideas to life with generative AI

Leading companies in a variety of industries like eDreams ODIGEO, GitLab, Oxbotica, and more, are using our generative AI technologies to create engaging content, synthesize and organize information, automate business processes, and build amazing customer experiences. A few examples we showcased today include:

• Adore Me, a New York-based intimate apparel brand, is creating production-worthy copy with generative AI features in Docs and Gmail. This is accelerating projects and processes in ways that even surprised the company.
• Canva, the visual communication platform, uses Google Cloud’s rich generative AI capabilities in language translation to better support its non-English speaking users. Users can now easily translate presentations, posters, social media posts, and more into over a hundred languages. The company is also testing ways that Google’s PaLM technology can turn short video clips into longer, more compelling stories. The result will be a more seamless design experience while growing the Canva brand.
• Character.AI, a leading conversational AI platform, selected Google Cloud as its preferred cloud infrastructure provider because we offer the speed, security and flexibility required to meet the needs of its rapidly growing community of creators. We are enabling Character.AI to train and infer LLMs faster and more efficiently, and enhancing the customer experience by inspiring imagination, discovery, and understanding. 
• Deutsche Bank is testing Google’s generative AI and large language models (LLMs) at scale to provide new insights to financial analysts, driving operational efficiencies and execution velocity. There is an opportunity to significantly reduce the time it takes to perform banking operations and financial analysts’ tasks, empowering employees by increasing their productivity while helping to safeguard customer data privacy, data integrity, and system security.
• Instacart is always looking for opportunities to adopt the latest technological innovations, and by joining the Workspace Labs program, they have access to the new features and can discover how generative AI will make an impact for their teams.
• Orange is exploring a next-generation contact center with Google Cloud. With customers in 26 countries, the global telecommunications firm is testing generative AI to transcribe the call, summarize the exchange between the customer and service representatives, and suggest possible follow up actions to the agent based on the discussion. This experiment has the potential to dramatically improve both the efficiency and quality of customer interactions. Orange is working closely with Google to help ensure data protection and make sure that systematic employee review of Generative AI output and transparency can be implemented.
• Replit is developing a collaborative software development platform powered by AI. Developers using Replit’s Ghostwriter coding AI already have 30% of their code written by generative AI today. With real-time debugging of the code output and context awareness of the program’s files, Ghostwriter frees up developers’ time for more challenging and creative aspects of programming.
• Uber is creating generative AI for customer-service chatbots and agent assist capabilities, which handle a range of common service issues with human-like interactions with the aim of achieving greater customer satisfaction and cost efficiency. Additionally, Uber is working on using our synthetic data systems (a technique for improving the quality of LLMs) in areas like product development, fraud detection, and employee productivity.

Wendy’s is working with Google Cloud on a groundbreaking AI solution, Wendy’s FreshAI, designed to revolutionize the quick service restaurant industry. The technology is transforming Wendy’s drive-thru food ordering experience with Google Cloud’s generative AI and LLMs—with the ability to discern the billions of possible order combinations on the Wendy’s menu. In June, Wendy’s plans to launch its first pilot of the technology in a Columbus, Ohio-area restaurant, before expanding to more drive-thru locations.

Partnering creates a strong ecosystem of real-world options for customers

At Google Cloud, we are dedicated to being the most open hyperscale cloud provider, and that includes our AI ecosystem. Today, we are excited to expand upon the partnerships announced earlier this year for every layer of the AI stack—chipmakers, companies building foundation models and AI platforms, technology partners enabling companies to develop and deploy machine learning (ML) models, app-builders solving customer use cases with generative AI, and global services and consulting firms that help enterprise customers implement all of this technology at scale.

We announced new or expanded partnerships with SaaS companies like Box, Dialpad, Jasper, Salesforce, and UKG; and consultancies including Accenture, BCG, Cognizant, Deloitte, and KPMG. Together with our previous announcements with companies like AI21 Labs, Aible, Anthropic, Anyscale, Bending Spoons, Cohere, Faraday,  Glean, Gretel, Labelbox, Midjourney, Osmo, Replit, Snorkel AI, Tabnine, Weights & Biases, and many more, they provide the a wide range of options for businesses and governments looking to bring generative AI into their organizations.

Introducing new generative AI capabilities for Google Cloud

To help cloud users of all skill levels solve their everyday work challenges, we’re excited to announce Duet AI for Google Cloud, a new generative AI-powered collaborator. Duet AI serves as your expert pair programmer and assists cloud users with contextual code completion, offering suggestions tuned to your code base, generating entire functions in real-time, and assisting you with code reviews and inspections. It can fundamentally transform the way cloud users of all skill sets build new experiences and is embedded across Google Cloud interfaces—within the integrated development environment (IDE), Google Cloud Console, and even chat. 

For developers looking to create generative AI applications more simply and efficiently, we are also introducing new foundation models and capabilities across our Google Cloud AI products. And to continue to enable and inspire more customers and partners, we are opening up generative AI support in Vertex AI and expanding access to many of these new innovations to more organizations.

• New foundation models are now available in Vertex AI. Codey, our code generation foundation model, helps accelerate software development with code generation, code completion, and code chat. Imagen, our text-to-image foundation model, lets customers generate and customize studio-grade images. And Chirp, our state-of-the-art speech model, allows customers to more deeply engage with their customers and constituents inclusively in their native languages with captioning and voice assistance. They can each be accessed via APIs, tuned through our intuitive Generative AI Studio, and feature enterprise-grade security and reliability, including encryption, access control, content moderation, and recitation capabilities that let organizations see the sources behind model outputs. 
• Text Embeddings API is a new API endpoint that lets developers build recommendation engines, classifiers, question-answering systems, similarity matching, and other sophisticated applications based on semantic understanding of text or images. 
• Reinforcement Learning from Human Feedback (RLHF) allows organizations to incorporate human feedback to deeply customize and improve model performance. 

Underpinning all of these innovations is our AI-optimized infrastructure. We provide the widest choice of compute options among leading cloud providers and are excited to continue to build them out with the introduction of new A3 Virtual Machines based on NVIDIA’s H100 GPU. These VMs, alongside the recently announced G2 VMs, offer a comprehensive range of GPU power for training and serving AI models.

Extending generative AI across Google Workspace 

Earlier this year, we shared our vision for bringing generative AI to Workspace, and gave many users early access to features that helped them write in Gmail and Google Docs. Today, we are excited to announce Duet AI for Google Workspace, which brings together our powerful generative AI features and lets users collaborate with AI so they can get more done every day. We’re delivering the following features to trusted testers via Workspace Labs: 

• In Gmail, we’re adding the ability to draft responses that consider the context of your existing email thread—and making the experience available on mobile.
• In Google Slides and Meet, we’re enabling you to easily generate images from text descriptions. Custom images in slides can help bring your story to life, and in Meet they can be used to create custom backgrounds.
• In Google Sheets, we’re automating data classification and the creation of custom plans—helping you analyze and organize data faster than ever. 

Moving the industry forward, responsibly

Customers continue to amaze us with their ideas and creativity, and we look forward to continuing to help them discover their own paths forward with generative AI. While the potential for impact on business is great, we remain committed to taking a responsible approach, guided by our AI Principles. As we gather more feedback from our customers and users, we will continue to bring new innovations to market, with a goal to enable organizations of every size and industry to increase efficiency, connect with customers in new ways, and unlock entirely new revenue streams.

Editor’s note: The post is part of a series highlighting our awesome partners, and their solutions, that are Built with BigQuery.

What are Customer Data Platforms (CDPs) and why do we need them?

Today, customers utilize a wide array of devices when interacting with a brand. As an example, think about the last time you bought a shirt. You may start with a search on your phone as you take the subway to work. During that 20 minute ride, you narrow down the type of shirt . Later, as you take your lunch break, you spend a few more minutes refining your search on your work laptop and you are able to find two shirt models of interest. Pressed for time, you add both to your shopping cart at an online retailer to review at a later point. Finally, after you arrive back home and as you are checking your physical mail, you stumble across a sales advertisement for the type of shirt that you are looking for, available at your local brick and mortar store. The next day you visit that store during your lunch break and purchase the shirt.

Many marketers face the challenge of creating a consistent 360 customer view that captures the customer lifecycle, as illustrated in the example above – including their online/offline journey, interacting with multiple data points across multiple data sources.

The evolution of managing customer data reached a turning point in the late 90’s with CRM software that sought to match current and potential customers with their interactions. Later as a backbone of data-driven marketing, Data Management Platforms (DMPs) expanded the reach of data management to include second and third party datasets including anonymous IDs. A Customer Data Platform combines these two types of systems, creating a unified, persistent customer view across channels (mobile, web etc) that provide data visibility and granularity at individual level.

A new approach to empowering marketing heroes

Tinyclues is a company that specializes in empowering marketers to drive sustainable engagement from their customers and generate additional revenue, without damaging customer equity. The company was founded in 2010 on a simple hunch: B2C marketing databases contain sufficient amounts of implicit information (data unrelated to explicit actions) to transform the way marketers interact with customers, and a new class of algorithms based on Deep Learning (sophisticated machine learning that mimics the way humans learn) holds the power to unlock this data’s potential. Where other players in the space have historically relied – and continue to rely – on a handful of explicit past behaviors and more than a handful of assumptions, Tinyclues’ predictive engine uses all of the customer data that marketers have available in order to formulate deeply precise models, down even to the SKU level. Tinyclues’ algorithms are designed to detect changes in consumption patterns in real-time, and adapt predictions accordingly.

This technology allows marketers to find precisely the right audiences for any offer during any timeframe, increasing engagement with those offers and, ultimately, revenue; additionally, marketers are able to increase campaign volume while decreasing customer fatigue and opt-outs, knowing that audiences are receiving only the most relevant messages. Tinyclues’ technology also reduces time spent building and planning campaigns by upwards of 80%, as valuable internal resources can be diverted away from manual audience-building.

Google Cloud’s Data Platform, spearheaded by BigQuery, provides a serverless, highly scalable, and cost-effective foundation to build this next generation of CDPs.

Tinyclues Architecture:

To enable this scalable solution for clients, Tinyclues receives purchase and interaction logs from clients in addition to product and user tables. In most cases, this data is already in the client’s BigQuery instance, in which case they can be easily shared with Tinyclues utilizing BigQuery authorized views.

In cases where the data is not in BigQuery, flat files are sent to Tinyclues via GCS and are ingested in the client’s data set via a lightweight Cloud Function. The orchestration of all pipelines is implemented via Cloud Composer (Google’s managed Airflow). The transformation of data is accomplished by utilizing simple select statements in the Data Built Tool (DBT), which is wrapped inside an airflow DAG that powers all data normalization and transformations. There are several other DAGs to fulfill more functionalities, including:

• Indexing the product catalog on Elastic Cloud (Elasticsearch managed service) on GCP to provide auto-complete search capabilities to TCs clients as shown below:

• The export of Tinyclues-powered audiences to the clients’ activation channels, whether they are using SFMC, Braze, Adobe, GMP, or Meta.

Tinyclues AI/ML Pipeline powered by Google Vertex AI

TCs ML Training pipelines are used to train models that calculate propensity scores. They are composed using Airflow DAGs, powered by Tensorflow & Vertex AI Pipelines. BigQuery is used natively, without data movement, to perform as much feature engineering as possible in-place.

TC uses the TFX library to run ML Pipelines in Vertex AI. Building on top of Tensorflow as their main deep learning framework of choice due to its maturity, open source platform, scalability and support for complex data structures (Ragged and Sparse Tensors).

Below is a partial example of TC’s Vertex AI Pipeline graph, illustrating the workflow steps in the training pipeline. This pipeline allows for the modularization & standardization of functionality into easily manageable building blocks. These blocks are composed of TFX components (TC reuses most of the standard components in addition to customizing some such as a proprietary implementation of the Evaluator to compute both ML Metrics (which is part of the standard implementation) but also more Business Metrics like Overlap of clickers etc. The individual components/steps are chained with DSL to form a pipeline that is modular and easily orchestrated or updated as needed.

With the trained Tensorflow models available in GCS, TCs exposes these in BigQuery ML (BQML) to enable their clients to score millions of users for their propensity to buy X or Y within minutes. This would not be possible without the power of BigQuery and also frees TC from previously experienced scalability issues.

As an illustration, TC has the need to score thousands of topics among millions of users. This used to take north of 20 hours on their previous stack, and now takes less than 20 minutes thanks to the optimization work that TC has implemented in their custom algorithm and the sheer power of BQ to scale to any workload accordingly.

Data Gravity: Breaking the Paradigm – Bringing the Model to your Data

BQML enables TC to call pre-trained TensorFlow models within an SQL environment, thus avoiding exporting data in and out of BQ using already provisioned BQ serverless processing power. Using BQML removes the layers between the models and the data warehouse and allows them to express the entire inference pipe as a number of SQL requests. TC no longer has to export data to load it into their models. Instead, they are bringing their models to the data.

Avoiding the export of data in and out of BQ and the serverless provisioning and start of machines saves significant time. As an example, exporting an 11M lines campaign for a large client previously took 15 min or more to process. Deployed on BQML it now takes minutes with more than half of the processing time attributed to network transfers to our client system.

Inference times in BQML compared to TCs legacy stack:

As can be seen, using this approach enabled by BQML, the reduction in the number of steps leads to a 50% decrease in overall inference time, improving upon each step of the prediction.

The Proof is in the pudding

Tinyclues has consistently delivered on its promises of increased autonomy for CRM teams, rapid audience building, superior performance against in-house segmentation, identification of untapped messaging and revenue opportunities, fatigue management, and more, working with partners like Tiffany & Co, Rakuten, and Samsung, among many others.

Conclusion

Google’s data cloud provides a complete platform for building data-driven applications like the headless CDP solution developed by Tinyclues — from simplified data ingestion, processing, and storage to powerful analytics, AI, ML, and data sharing capabilities — all integrated with the open, secure, and sustainable Google Cloud platform. With a diverse partner ecosystem, open-source tools, and APIs, Google Cloud can provide technology companies the portability and differentiators they need to serve the next generation of marketing customers.

To learn more about Tinyclues on Google Cloud, visit Tinyclues. Click here to learn more about Google Cloud’s Built with BigQuery initiative.

We thank the many Google Cloud team members who contributed to this ongoing data platform collaboration and review, especially Dr. Ali Arsanjani in Partner Engineering.

The magnitude and direction of wind significantly impacts airport operations, and Lufthansa Group Airlines are no exception. A particularly troublesome kind is called BISE: it is a cold, dry wind that blows from the northeast to southwest in Switzerland, through the Swiss Plateau. Its effects on flight schedules can be severe, such as forcing planes to change runways, which can create a chain reaction of flight delays and possible cancellations. In Zurich Airport, in particular, BISE can potentially reduce capacity by up to 30%, leading to further flight delays and cancellations, and to millions in lost revenue for Lufthansa (as well as dissatisfaction among their passengers).

Being able to predict this kind of wind well in advance lets the Network Operations Control team schedule flight operations optimally across runways and timeslots, to minimize disruptions to the schedule. However, predicting speed and magnitude can be incredibly difficult to model and thus to predict— which is why Lufthansa reached out to Google Cloud.

Machine learning (ML) can help airports and airlines to better anticipate and manage these types of disruptive weather events. In this blog post, we’ll explore an experiment Lufthansa did together with Google Cloud and its Vertex AI Forecast service, accurately predicting BISE hours in advance, with more than 40% relative improvement in accuracy over internal heuristics, all within days instead of the months it often takes to do ML projects of this magnitude and performance.

“Being impressed with Google’s technology and prowess in the field of AI and machine learning, we were certain that my working together with their expert, to combine our technology with their domain expertise, we would achieve the best results possible,“ said Christian Most, Senior Director, Digital Operations Optimization at Lufthansa Group.

Collecting and preparing the dataset

The goal of Lufthansa and Google Cloud’s project was to forecast the BISE wind for Zurich’s Kloten Airport using deep learning-based ML approaches, then to see if the prediction surpasses internal heuristics-driven solutions and gauge the ease of use and practicality of the deep learning approach in production.

Since deep learning-based techniques require large datasets, the project relies on Meteoswiss simulation data, a dataset consisting of multiple meteorological sensor measurements collected from several weather stations across Switzerland over the past five years. By using this dataset, we obtained data on factors like wind direction, speed, pressure, temperature, humidity and more, at a 10 min resolution, along with some information about the location of the weather stations, such as altitude. These factors, which we hypothesized to be predictive of the BISE, ended up carrying valuable signals, as we would see later.

This collected data was next subjected to an extensive cleaning and feature engineering process using Vertex AI Workbench, in order to prepare the final dataset for training. The cleaning phase included steps to drop the features, or rows, that contained too many missing values, or failed statistical tests for entropy, etc. Since the direction of wind is a circular feature (between 0 and 360 degrees), this column/feature was replaced with two features: the corresponding sine and cosine embedding. The dataset was then flattened such that the columns contained all the relevant features and sensor measurements from all the weather stations at a particular 10-minute interval.

Since the target variable — i.e,. BISE — was not directly available, we engineered a proxy target variable for BISE called “tailwind speed around runway,” which above a certain threshold indicates the presence of BISE along the runway.

Forecasting wind in the Cloud

Once the dataset was ready, Lufthansa and Google Cloud evaluated several options before deciding to experiment and tune Vertex AI Forecast, Google’s AutoML-powered forecasting service, in order to achieve optimum results. Vertex Forecast is capable of the required feature engineering, neural architecture search, and hyper parameter tuning, and it is managed by Google Cloud to score in the top 2.5% in the M5 Forecasting Competition on Kaggle, in a completely automated fashion. These qualities made it an excellent choice for Lufthansa, to reduce the manual overhead of creating, deploying, and maintaining top performing deep learning models.

The raw data files were loaded from cloud storage, preprocessed on Vertex AI Workbench. Then, a training pipeline was initiated on Vertex AI Pipelines, which performed the following steps in sequence:

The .csv data file was loaded from Cloud Storage into a Vertex AI managed dataset.

A Vertex AI forecasting training job was initiated with the dataset, and it was also registered as a model in the Vertex AI Model Registry.

Upon completion, the model was evaluated on the test set, and the model’s predictions and the input features and ground truth of the test set, were stored in a user-defined table in BigQuery. Several test metrics were also available on the service and model dashboards.

One of the biggest challenges was the severe imbalance in the dataset, as measurements with BISE were very far and few in between. In order to account for this, instances where BISE occurred, as well the occurrences temporally close to them, were upweighted using weights calculated with methods including Inverse of Square Root of Number of Samples (ISNS), Effective Number of Samples (ENS), and Gaussian reweighting. The formulas for the methods are given below. These weights were supplied as separate columns in the dataset, and were iteratively used thereafter by the service as the “weight” column.

ISNS

ENS

Weighted gaussian

Results and next steps

In the above figures, the x-axis represents the forecast horizon and the Y-axis shows the respective metrics (Recall/F1-score). As shown after multiple experiments, we can see Vertex AI Forecast achieved higher recall and precision t (red bar), outperforming Lufthansa’s internal baseline heuristics, with the performance gap widening steadily as the forecast horizon extends further into the future. At the two-hour mark, our custom-configured Vertex AI Forecast model improved by 40% relative to the internal heuristics and 1700% compared to the random guess baseline. As we saw with other experiments, at a six-hour forecast horizon, the performance gap widens even more, with Vertex AI Forecast in the lead. Since forecasting BISE a few hours in advance is very beneficial to prevent flight delays for Lufthansa, this was a great solution for them.

“We are very excited to be able to not only do accurate long term forecasts for the BISE, but also that Vertex AI Forecasting makes training and deploying such models much easier and faster, allowing us to innovate rapidly to serve our customers and stakeholders in the best possible manner,” said Swiss Oliver Rueegg, Product Owner, Swiss International Airlines.

Lufthansa plans to explore productionizing this solution by integrating it into their Operations Decision Support Suite, which is used by the network controllers in the Operations Control Center in Kloten, as well as to work closely with Google’s specialists to integrate both Vertex AI Forecast and other of Google’s AI/ML offerings for their use cases.

At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data that businesses collect, process and store but do not utilize for purposes such as analytics, monetization, etc. These documents in pdf or image formats, often trigger complex processes that have historically relied on fragmented technology and manual steps. With compute solutions on Google Cloud and Document AI, you can create seamless integrations and easy to use applications for your users. Document AI is a platform and a family of solutions that help businesses to transform documents into structured data backed by machine learning. In this blog post we’ll walk you through how to use Serverless technology to process documents with Cloud Functions, and with workflows of business processes orchestrating microservices, API calls, and functions, thanks to Workflows.

At Cloud Next 2021, we presented how to build easy AI-powered applications with Google Cloud. We introduced a sample application for handling incoming expense reports, analyzing expense receipts with Procurement Document AI, a DocAI solution for automating procurement data capture from forms including invoices, utility statements and more. Then organizing the logic of a report approval process with Workflows, and used Cloud Functions as glue to invoke the workflow, and do analysis of the parsed document.

We also open sourced the code on this Github repository, if you’re interested in learning more about this application.

In the above diagram, there are two user journeys: the employee submitting an expense report where multiple receipts are processed at once, and the manager validating or rejecting the expense report. 

First, the employee goes to the website, powered by Vue.js for the frontend progressive JavaScript framework and Shoelace for the library of web components. The website is hosted via Firebase Hosting. The frontend invokes an HTTP function that triggers the execution of our business workflow, defined using the Workflows YAML syntax. 

Workflows is able to handle long-running operations without any additional code required, in our case we are asynchronously processing a set receipt files. Here, the Document AI connector directly calls the batch processing endpoint for service. This API returns a long-running operation: if you poll the API, the operation state will be “RUNNING” until it has reached a “SUCCEEDED” or “FAILED” state. You would have to wait for its completion. However, Workflows’ connectors handle such long-running operations, without you having to poll the API multiple times till the state changes. Here’s how we call the batch processing operation of the Document AI connector:

  - invoke_document_ai:
    call: googleapis.documentai.v1.projects.locations.processors.batchProcess
    args:
        name: ${"projects/" + project + "/locations/eu/processors/" + processorId}
        location: "eu"
        body:
            inputDocuments:
                gcsPrefix:
                    gcsUriPrefix: ${bucket_input + report_id}
            documentOutputConfig:
                gcsOutputConfig: 
                    gcsUri: ${bucket_output + report_id}
            skipHumanReview: true
    result: document_ai_response

Machine learning uses state of the art Vision and Natural Language Processing models to intelligently extract schematized data from documents with Document AI. As a developer, you don’t have to figure out how to fine tune or reframe the receipt pictures, or how to find the relevant field and information in the receipt. It’s Document AI’s job to help you here: it will return a JSON document whose fields are: line_item, currency, supplier_name, total_amount, etc. Document AI is capable of understanding standardized papers and forms, including invoices, lending documents, pay slips, driver licenses, and more.

A cloud function retrieves all the relevant fields of the receipts, and makes its own tallies, before submitting the expense report for approval to the manager. Another useful feature of Workflows is put to good use: Callbacks, that we introduced last year. In the workflow definition we create a callback endpoint, and the workflow execution will wait for the callback to be called to continue its flow, thanks to those two instructions:

  - create_callback:
    call: events.create_callback_endpoint
    args:
        http_callback_method: "POST"
    result: callback_details
...
- await_callback:
    try:
        call: events.await_callback
        args:
            callback: ${callback_details}
            timeout: 3600
        result: callback_request
    except:
        as: e
        steps:
            - update_status_to_error:
              ...

In this example application, we combined the intelligent capabilities of Document AI to transform complex image documents into usable structured data, with Cloud Functions for data transformation, process triggering, and callback handling logic, and Workflows enabled us to orchestrate the underlying business process and its service call logic.

Going further 

If you’re looking to make sense of your documents, turning dark data into structured information, be sure to check out what Document AI offers. You can also get your hands on a codelab to get started quickly, in which you’ll get a chance at processing handwritten forms. If you want to explore Workflows, quickstarts are available to guide you through your first steps, and likewise, another codelab explores the basics of Workflows. As mentioned earlier, for a concrete example, the source code of our smart expense application is available on Github. Don’t hesitate to reach out to us at @glaforge and @asrivas_dev to discuss smart scalable apps with us.