Call ChatGPT/GPT-3 from Power Apps and Power Automate via Azure OpenAI

Anyone in technology will know the buzz caused by ChatGPT since its launch, and beyond fooling schoolteachers with human-like essays and passing law exams we’ve seen many interesting real-world use cases already. Of course, the point isn’t that it’s just an interesting plaything - generative AI using large language models are powerful technology to integrate into apps, tools, and automated processes. The potential is almost limitless and entirely new categories of digital experience are opened up. As we all know, Microsoft were quick to identify this and their $11 billion investment in GPT creator OpenAI means cutting-edge AI is becoming integrated into their products and services.

So we know that Teams, SharePoint, Viva, Dynamics etc. will evolve quite quickly and with features like Power Apps Ideas and Power Automate AI Copilot, Microsoft are starting to include GPT-3 and Codex capabilities into the maker experience within Power Platform. However, alongside that we want to build GPT into *our* apps and solutions! In this post we’ll explore how to integrate GPT-3 into what you build with the Power Platform – this is significant because being able to call into GPT capabilities from Power Apps, Power Automate, Power Virtual Agent etc. can be hugely powerful. I won’t go into use cases too much here, but perhaps you want to generate content for new products in development or automate contact centre responses. Maybe you want to use it as a classic chatbot able to answer questions or be a digital assistant of some kind. Or maybe you want to analyse, summarise, translate, or classify some content. In this post I’m going to keep us focused on how to implement rather than usage, but the limit might just your imagination.

The (not particularly) bad news – some implementation work is required, there’s no magic switch in the Power Platform
The good news – it’s as simple as deploying some services in Azure and creating a Power Platform custom connector (which you, or someone in your organisation, may have done already)

What you'll need

Here are the ingredients for calling GPT from the Power Platform:

• The ability to create an Azure OpenAI instance – note that this requires an application and is currently only available for managed customers and partners. Microsoft assess your use case in line with their responsible AI commitment
• The ability to create and use a custom connector in the Power Platform
• A paid Power Platform license which allows you to use a custom connector (e.g. Power Apps/Power Automate per user or per app plan)

Overall here’s what the process looks like:

If you have experience of connectors in the Power Platform things are reasonably straightforward, though there are some snags in the process. This article presents a step-by-step process as well as some things that will hopefully accelerate you in getting started.

Azure OpenAI – the service that makes it happen

In my approach I’m using Azure OpenAI rather than the API hosted by the OpenAI organisation, because this is the way I expect the vast vast majority of businesses to tap into the capabilities. For calling GPT or other OpenAI models to be workable in the real world, it can’t just be another service on the internet. Azure OpenAI is essentially Microsoft-hosted GPT – providing all the security, compliance, trust, scalability, reliability, and responsible AI governance that a mature organisation would look for. Unsurprisingly, the OpenAI service folds under Azure Cognitive Services within Azure, and this means that working with the service and integrating it into solutions is familiar. Microsoft are the exclusive cloud provider behind OpenAI’s models, meaning you won’t find them in AWS, GCP or another cloud provider.

Azure OpenAI is a gated service

To work with Azure OpenAI you need to apply for access, which involves completing a form to describe how you plan to use the service and providing some other details. You’ll also provide the ID of the Azure subscription you plan to use the service from – if your application is approved, this is the subscription where the service will be enabled for you. Note also that the service is only available in three Microsoft regions for now. Details about the application process can be found in the How do I get access to Azure OpenAI section in the Microsoft docs.

Link – form to apply for OpenAI: https://aka.ms/oai/access

Once you’ve received notification that your application for Azure OpenAI has been approved, you’re ready to start building.

Step 1 – create your Azure OpenAI instance

Firstly, in the Azure portal navigate into the Azure subscription where your usage of OpenAI has been approved. Start the process by creating a new resource and searching for “Azure OpenAI”:

Click the ‘Create’ link. We’ll now provide the details to create the resource in your chosen region and resource group – in my case I already have a resource group ready to go:

There’s only one pricing tier and currently you’ll be selecting from one of three regions where the OpenAI service is available:

• West Europe
• East US
• South Central US

Hit the ‘Review + create’ button and you should see validation has passed:

Now finish off by clicking the ‘Create’ button:

Creation will now start:

Once complete, your OpenAI resource will be available. The main elements you’ll use are highlighted below:

Exploring the Azure OpenAI Studio

For the next steps you’ll use the link highlighted above to navigate into the Azure OpenAI Studio – this is the area where OpenAI models can be deployed for use. Like many other Azure AI capabilities, configuration isn't done in a normal blade in the Azure portal - instead there's a more complete experience in a sub-portal. Here’s what the Studio looks like:

Azure OpenAI Studio has a GPT-3 playground, similar to the openai.com site where you may have played with ChatGPT or the main playground. In the Azure OpenAI Studio the playground can be used once you have a model deployed (which we’ll get to in a second), and it’s exactly like OpenAI’s own GPT-3 playground with some ready-to-play examples and tuning options. In the image below I’m summarising some longer text using the text-davinci-003 model:

The playground gives you the ability to test various prompts and the AI-generated results, also giving you info on how many tokens were used in the operations. Pricing is consumption-based and will depend on how many tokens you use overall.

To give a further sense of what’s available in the Azure OpenAI Studio, here’s the navigation:

To move forward with integrating GPT-3 into our apps, we need to deploy one of the AI models and make it available for use.

Step 2 – choose an OpenAI model and deploy it

Using the navigation above, go into the Models area. Here you’ll find all the OpenAI models available for selection, and for production use you’ll want to spend time in the OpenAI model documentation to establish the best model for your needs. Here’s a sample of the models you can choose from:

Each model has a different blend of functional performance from the model for the cost and speed of operations. The Finding the right model area in the OpenAI documentation has the information you need and here’s a quick extract to give you a sense:

Model (latest version)	Description	Training data
text-davinci-003	Most capable GPT-3 model. Can do any task the other models can do, often with higher quality, longer output and better instruction-following. Also supports inserting completions within text.	Up to Jun 2021
text-curie-001	Very capable, but faster and lower cost than Davinci.	Up to Oct 2019
text-babbage-001	Capable of straightforward tasks, very fast, and lower cost.	Up to Oct 2019
text-ada-001	Capable of very simple tasks, usually the fastest model in the GPT-3 series, and lowest cost.	Up to Oct 2019

Text-davinci-003 is the model most of us have been exposed to the most as we’ve been playing with GPT-3, and as noted above it’s the most powerful. It’s worth noting that if your use case relates to GPT’s code capabilities (e.g. debugging, finding errors, translating from natural language to code etc.) then you’ll need one of the Codex models such as code-davinci-002 or code-cushman-001. Again, more details in the documentation. Most likely you just want to get started experimenting, so we’ll deploy an instance of the text-davinci-003 model.

To move forward with this step, go into the Deployments area in the navigation:

From there, hit the ‘Create new deployment’ button:

In the dlalog which appears, select the text-davinci-003 model (or another you choose) and give it a name – I’m simply giving mine a prefix:

Hit the ‘Create’ button and model creates instantly:

The model is now ready for use – you can test this in the playground, ensuring your deployment is selected in the dropdown. The next step is to start work on making it available in the Power Platform.

Step 3.1 – create the custom connector in the Power Platform

Background:

Azure OpenAI provides a REST API, and in this step we’ll create the custom connector to the API endpoint of your Azure OpenAI service and a connection instance to use. From the REST APIs provided, it’s the Completions API which is the one we’ll use – this is the primary service to provide responses to a given prompt. A call to this endpoint looks like this:

POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/completions?api-version={api-version}

In a JSON body, you’ll pass parameters for the prompt (i.e. your question/request) and various optional tokens to instruct GPT on how to operate, including max_tokens and temperature to use. All this is documented and the good news is you can stay simple and use defaults.

You can authenticate to the API using either AAD (which you should do for production use) or with an API key. In my example below I’m using the API key to keep things simple, and this approach is fine as you get started. A header named “api-key” is expected and you pass one of the keys from your Azure OpenAI endpoint in this parameter.

Process:

If you’ve ever created a Power Platform connector before you’ll know there are various approaches to this – the API docs link to Swagger definitions on Github, however these will not work for the Power Platform because they’re in OpenAPI 3.0 format and the Power Platform currently needs OpenAPI 2.0. To work around this I created my connector from scratch and used the Swagger editor to craft the method calls to match the API docs. To save you some of this hassle you can copy/paste my Swagger definition below, and that should simplify the process substantially.

Before we get started, you’re going to need the details of your Azure OpenAI endpoint, so collect those now and store them safely (remembering that your API key is a sensitive credential). To do this, go back into the main Azure portal (not the OpenAI Studio) and head into the ‘Keys and Endpoint’ area:

Once there, use the screen to collect one of your keys and the endpoint URI:

Once you have those details, we’re ready to create the connector in the Power Platform. To do this, go to https://make.powerautomate.com/ and ensure you’re in the Power Platform environment to use.

From there, go to the Custom connectors area:

Hit the ‘New custom connector’ button and select ‘from blank’:

Give your connector a name:

We now start defining the connector – you can define each individual step if you like but you should find it easier to import from my definition below. Before that step, you can add a custom logo if you like in the ‘General’ area – this will make the connector easier to identify in Power Automate. 

The next step is to download the Swagger definition and save it locally. If you choose to use my Swagger definition, you can get it from the embed below (NOTE: if you're reading on a phone this will not be shown!). Copy the definition and save it locally:

Make the following changes to the file and save:

• Replace ‘[YOUR ENDPOINT PREFIX HERE] in line 6 with your prefix – the final line should be something like cob-openai-instance1.openai.azure.com
• Replace ‘[YOUR DEPLOYMENT ID HERE]’ with your AI model deployment name from the Azure OpenAI Studio – in the steps above, my model name was cob-textdavinci-003 for example

In the connector definition screens, toggle the Swagger Editor toggle so it’s on:

The editor UI will now show on the left - paste your amended Swagger definition in here. The image below shows my host (prefixed ‘COB’) but you should see your equivalent:

Once this is done, simply hit the ‘Create connector’ button:

Your custom connector has now been created using the details from the Swagger definition.

Step 3.2 – create connection instance

Once the custom connector exists we create a connection from it – if you haven’t done this before, this is where credentials are specified. In the Power Automate portal, if you’re not there already go back into the Data > Custom connectors area and find the custom connector we created in the previous step – when you find it click the plus icon (+):

Paste in the API key previously retrieved for your Azure endpoint and hit ‘Create connection’:

The connection is now created and should appear in your list:

We’re now ready to test calling GPT from a Flow or Power App – in the next step we’ll use a Flow.

Step 4 – test calling GPT-3 from a Flow

Follow the normal process in Power Automate to create a Flow, deciding which flavour is most appropriate for your test. As a quick initial test you may want to simply create a manually-triggered Flow. Once in there, use the normal approach to add a new step:

In the add step, choose the ‘Custom’ area and find the connection created in the previous step:

If you used my YAML, the connector definition will give you a Flow action which exposes the things you might need to vary with some elements auto-populated – remember to make sure your model deployment ID is specified in the first parameter (it will be if you overwrote mine in the earlier step):

Let’s now add some details for the call – choose a prompt that you like (red box below) and specify the number of tokens and temperature you’d like the model to use (green box below):

If you now run the Flow, you should get a successful response with the generated text returned in the body:

If we expand this, here’s what our call to GPT-3 created in full for these parameters:

Power Platform Governance is an important component of any organization’s digital transformation effort that allows customers to stay in control of the data, applications, and processes. It is an integrated system of policies, standards, and procedures that ensure the correct use and management of technology. This governance framework provides organizations the assurance, confidence, and trust that their technology investments are secure, optimized, and compliant with the industry standards.

Power Platform Governance consists of three main components: Governance Policies, Governance Model, and Governance Reporting. Governance Policies are the foundation of the framework and define the standards, procedures, and rules that organizations must adhere to. The Governance Model is a set of tools, processes, and best practices that organizations use to implement and enforce the governance policies. Lastly, Governance Reporting is the process of monitoring, analyzing, and reporting on the adherence to the governance policies.

Overall, Power Platform Governance is an important component of any digital transformation effort, as it helps organizations to stay in control of their investments in technology, making sure that it is secure, optimized and compliant with the industry standards. It consists of three main components: governance policies, governance model, and governance reporting. This framework helps organizations to create a reliable and secure environment for their technology investments, ensuring that they are in compliance with the industry standards.

SUCCESS! You now have the full power of GPT-3 and other OpenAI models at your disposal in the Power Platform.

I recommend playing around with the max_tokens and temperature parameters to see different length responses and greater/lesser experimentation in the generation. Again, spend time in the OpenAI documentation to gain more understanding of the effect of these parameters.

GREAT! But how much will some experimentation cost?

The text-davinci-003 model used in this example costs $0.02 per 1000 tokens used, so you'll need to do a lot of playing to generate significant costs. Since the max_tokens parameter is exposed in the Swagger definition above, you can easily control how large or small the responses should be to allow you to control things as you're playing. Since this is just Azure consumption, you can also set up budgets and notifications in Azure Cost Analysis to help you monitor usage. I'll cover this in an upcoming article.

Summary

Being able to call into powerful generative AI from the Power Platform provides a huge range of possibilities - imagine integrating GPT-3 capabilities into your apps and processes. You can automate responses to queries, generate content, or even translate, summarise, classify or moderate something coming into a process. With the Codex model, scenarios around code generation and checking are also open. Most organisations invested in Microsoft tech will want to use the Azure-hosted version of the OpenAI models, and this is the Azure OpenAI service as part of Azure Cognitive Services. The service requires an application to be approved before you can access it, but it's as simple as completing a form in most cases. The process outlined above allows you to connect to the service through a Power Platform custom connector, and this can be shared across your organisation and/or controlled as you need.

The connection essentially gives you all the standard GPT-3 and Codex possibilities, meaning everything you may have experienced or read about online. Going beyond that, in many use cases you'll want to train a custom model - for example, to bring in industry or organisation-specific data and decision-making from your own prompts. This guides the AI into understanding your particular domain much more than the default. That's beyond the scope of this article but is just one of the many possibilities for going further with generative AI in your apps. 

Happy building!

My talks on Viva Topics at the European Collaboration Summit 2023, Düsseldorf and Modern Workplace Conference 2023, Paris

Along with Microsoft Syntex, Viva Topics is a technology close to my heart these days. I championed it's deployment at my employer (Content+Cloud) because, like every organisation, we're generating a huge volume of new documents and content each month and without new approaches and AI, it's becoming more difficult to find things and get questions answered, not easier. Microsoft's statistic that 1.6 billion documents per day are added to Microsoft 365 is eye-opening, and without some investment in content governance and tools that help you move beyond navigation and search, it's hard to ensure employees can continually find what they need and get their work done efficiently. 

I'm happy to say that I'm sharing our experience of Viva Topics, along with learnings and recommendations in a couple of talks at upcoming conferences. The larger event is the European Collaboration Summit in held in Germany in May (easily one of Europe's best Microsoft conferences), and I'm also honoured to be speaking at the Modern Workplace Conference in Paris in March which looks great too. If you're looking for learning and personal development events in Europe this spring and/or are interested in Viva Topics, some details of the conferences and my talks below. 

European Collaboration Summit 2023

When: 22-24 May 2023

Where: Düsseldorf, Germany

What: A top-tier Microsoft conference covering everything Microsoft 365, including the Power Platform, Teams, SharePoint, Viva, security, development and more. Speakers include 30+ Microsoft folks like Dan Holme, Vesa Juvonen, Stephen Sciliano and Laurie Pottmeyer along with a stellar line up of MVPs and community contributors. The event is held over 3 days with full day tutorials on day 1, over 120 sessions overall and lots of networking.      

Link: https://www.collabsummit.eu

NOTE: For a limited time you can also get a 15% discount using the code "COLLABSPEAKER2023"

My talk: Real world Viva Topics - benefits, mistakes and learnings

Abstract: If your Microsoft 365 tenant could understand the most valuable topics in your organisation and safely use the combination of AI and humans to signpost to the golden information, would you use it? Viva Topics has been available for some time and organisations are waking up to what AI can bring - particularly in helping side-step challenges of a complex tenant landscape, thousands of Teams and sites, and imperfect search and navigation. This session will include demos of Viva Topics in our production environment so you can see the experience in action. With the benefit of two years of experience and membership in Microsoft's Content AI partner program, this session will unpack Viva Topics value - sharing details of our journey and lessons learnt from implementing the technology for clients. How does the AI establish what's valuable? What happens about our sensitive content? Who will curate topics? What happens in a multi-lingual environment? This session will answer these questions and more, providing you with best practice guidance to move forward with confidence.

Modern Workplace Conference 2023

When: 27-28 March 2023

Where: Paris, France

What: A great 2-day community event with lots of MVPs and community speakers focused on everything Modern Work related. Over 60 sessions with 1000+ participants expected. Sessions are held in English.

Link: https://modern-workplace.pro

My talk: Viva Topics 18 months later - what did we get?

Finding information and expertise is far too time-consuming in the vast majority of organisations. Poorly configured search, the sprawl of repositories and sites, unceasing content growth and difficulties recognising authoritative content all conspire against the information worker. No wonder McKinsey and IDC report that the average knowledge worker spends 20-30% of their time just looking for things. Viva Topics is Microsoft's answer to this challenge. After being part of the Project Cortex private preview, we've had 18 months of Viva Topics being live in our business and have implemented the technology for several clients. This session covers the benefits we've seen (both the expected and unexpected), and shares best practice guidance on how to plan, implement, and build on Viva Topics. We'll demo the technology in our production environment so you can see the experience in action. Implementing Viva Topics at scale can be a big investment and it's important to know what's coming in the future. We'll end with a discussion on Microsoft's future roadmap and capabilities, so you can plan ahead with confidence.

Summary

We're coming into conference season and there are some great events happening around Europe. I hope to be speaking at a couple of other events in addition to those listed here and it would be great to see you somewhere - now is the time to start planning your learning and pitching to your boss!

Syntex Pay As You Go - how it helps and how to use it

In previous articles I've covered many of the capabilities of Syntex and how it can be used, and from my perspective organisations are becoming very interested in Syntex to automate processes involving documents. In a notable development Syntex now has a Pay As You Go (PAYG) model for pricing - in preview until February/March 2023 with no costs charged. In the same approach used for Power Platform PAYG, Azure billing is used and charges appear in Azure Cost Management within the subscription linked to your Microsoft 365 tenant. In this article we'll consider cases where the PAYG may be more appropriate than the 'per seat' licensing model, how to configure it, and considerations for using it. A big factor here is precisely which Syntex features are available in the PAYG model - it's not all of them, and Microsoft are starting in some places and expanding over time.

Why use PAYG for Syntex?

In short, many processes you might automate with Syntex don't align well with a small, specific group of employees who will consistently use the capability (suited to the per seat licensing model). Some do of course - the finance team who are using Syntex to automate invoice processing or receipt analysis, or the research analysts who need automated tagging and powerful search across articles they create. However, sometimes we want all employees to participate in a process where Syntex is used despite the fact this only happens occasionally. Examples could include:

• Processes and documents related to an event - a webinar, or month-end/year-end processes, company results time etc.
• An organisational CV store where any employee can upload and maintain their CV
• Using Syntex for governance i.e. the AI detecting sensitive data in documents in order to automatically apply a sensitivity label in Microsoft 365 - to drive encryption, conditional access, or other security measures
• Using Syntex for compliance - driving automated retention labels to ensure content isn't retained for longer than permitted

These examples all relate to Syntex content understanding capabilities i.e. Syntex analysing and/or extracting contents of your documents to do something with that. 

On the content creation side, Syntex Content Assembly might be used to generate new documents in an automated (or semi-automated) way by many people but only occasionally. Perhaps it's a large team of engineers who occasionally create risk assessments or safety reports, or a finance or HR process which creates a document but can be triggered by a large number of people.

Other areas where it will often make sense to pay as you go relates to Syntex content management capabilities coming later in 2023, such as eSignatures. PAYG will be very relevant here - see my list lower down.

Which Syntex capabilities can be used with PAYG?

Let's break it down into what's here today (January 2023) and what's coming tomorrow.

TODAY:

• NEW - Unstructured document processing
• Structured document processing (via AI Builder)
• Freeform document processing (via AI Builder)

In short, all document understanding capabilities can now be used via PAYG (subject to preview conditions until Febrary/March 2023 - more on that later). The fact this now includes arguably the most powerful type, unstructured document processing, is significant.

TOMORROW:

Note: This is my list, not Microsoft's. While Microsoft have not announced that Syntex PAYG will support all the big features coming this year, given the nature of the 'content transactions' in these areas I think it's a reasonable bet that Syntex PAYG will support all of these in time:    

• Content Assembly
• Image tagging
• Translations 
• Summarisations
• Backup/restore
• Archiving
• Syntex eSignatures (forthcoming alternative to DocuSign and Adobe Sign) 

How much does Syntex cost under PAYG?

Each type of operation (e.g. a document being understood by Syntex, or a document being created by Syntex Content Assembly) will have it's own cost - and Microsoft have not yet announced pricing as of January 2023. Logically, I'd expect pricing models to look something like this:

Capability	Charged by
Content assembly	Per document generated
Syntex eSignatures	Per signature
Syntex backup/restore	Volume of data (and perhaps number of restores)
Syntex archiving	Volume of data (and perhaps access frequency or volume of data accessed)
Syntex document summarisation/translation/image tagging etc.	Per item processed

Regardless of the specifics, this will give 'pay per use' consumption pricing which should make Syntex appealing to many organisations. Simpler than 3rd party products which compete with some of these features, no wastage, and for orgs bought into Syntex no complex planning for exactly who should get a Syntex license and how to force processes around that.

A note on the PAYG preview (running until Febrary/March 2023):

For the preview period, unstructured document processing (the key new item in the 'today' category above) is 100% free - and no predicted costs show on the bill, because pricing hasn't been announced. What you do get to see is how many documents are being processed and in which sites in your tenant. The idea is for organisations already using Syntex with licensed users to have a method of measuring consumption and therefore having a means of calculating costs based on real usage when PAYG becomes available. However, I'm not sure how valuable this personally since relatively few businesses are in production with fully-enabled Syntex processes today - seeing consumption across a bunch of POC test cases isn't too helpful.

Configuring Syntex PAYG

You need the following:

• An Azure subscription in the same tenant
• A resource group in Azure to use for the Syntex billing resources
• An Azure storage account - used to store Excel exports showing Syntex billing

Step 1 - associate Syntex billing in M365 to Azure

To set up we start in the Microsoft 365 admin portal. Head to the 'Setup' area followed by the Syntex configuration option within the 'Files and content' section:

Here you'll see a new item to set up billing: 

In here you need to select the Azure subscription, resource group and region to use for billing storage:

Once that's done the initial configuration starts to happen:

Step 2 - configure Syntex billing exports to Azure storage

So far we've just told Syntex that we do want to use PAYG - this makes it available in the tenant (i.e. to non-licensed users), but what's needed now is the ability to monitor costs. Since this is done in Azure, head into your subscription. The first thing we need is a storage container to hold the files, so let's create that first - you can have the Azure wizard create the storage container for you, but generally better to do quick one-off steps manually so you truly know where things are. I named my container "syntex" (needs to be lower case):

We now configure the export. This ensures the Excel billing files showing your Syntex consumption will be exported regularly from Microsoft 365 to Azure so you can monitor and analyse. To do this, go to the Cost Management blade and then into the 'Exports' area:

Now we select the options for the export. You'll generally want to see actual costs (the alternative is an ammortised view) and I chose the  'daily' option to get a new file each day - in production you may be happy with weekly or monthly:

The storage container is also chosen at this point (select the 'use existing' option if you created it already):

Once this is done the export is set up:

 

Syntex billing reports

Once the export has been processed files will start to appear in your selected Azure container, with a folder for each export configured:

Drilling into the containers will take you to the Excel files. Once downloaded, you'll see data for all your Azure operations so if it's a busy subscription there'll be a lot in there. To isolate the Syntex rows, add an Excel filter on a column such as:

• meterCategory = Syntex

This will give you a view of your Syntex PAYG transactions:

Considerations:

• Today you'll only see 'Document Understanding' items, but in the future you'll see transactions for Syntex eSignatures, Content Assembly, backup/restore actions etc. A number of fields reflect this, including 'ProductName'
• The 'tags' field - this gives details of the  SharePoint sites and libraries where the Syntex AI model processed the document, helping you understand the actions your users are taking and in which
• The 'quantity' field - this relates to the number of pages processed by Syntex AI, across all processed documents 
• At the moment, some useful details such as the specific Syntex AI model used and precisely who is triggering consumption does NOT come through to the logs. This is a shame because being able to see 'invoice processing' and 'contracts model' etc. would simplify understanding how Syntex is being used a lot. Let's hope additional detail like this comes through in the future  
• Preview note - since Syntex PAYG is free in the preview period the 'effectivePrice' is 0 - so as described above, the preview doesn't help you fully predict costs at the moment, but does convey usage - which you can use when pricing is announced

How do I predict and stay in control of Syntex PAYG costs?

As usual with Azure billing, the answer is by configuring budgets and alerts. 

Using the metadata for Syntex operations which comes through to Azure billing, we can create a budget within Azure Cost Management to alert an operations team that consumption is higher than expected - at which point some investigation and/or intervention action can occur. In common with Azure Cost Management in general, we can't set an absolute limit at which point consumption is blocked - and indeed, most organisations wouldn't this for production processes. The last thing you want at month end is to discover invoices are no longer being processed and for it to take a day to find out why. But Azure budgets and alerts give you the foundations to implement the control processes you need.

Let's look at this in the next section.

Configuring an Azure budget to stay in control of Syntex PAYG costs

To configure budgets and alerts for Syntex consumption, head into the Azure Cost Management and then into 'Budgets'. 

The key step is to add a filter for one of the Syntex fields - for example 'MeterCategory = Syntex':

As more Syntex capabilities arrive which support PAYG, you could choose to be more specific e.g. monitor spending on document understanding specifically:

The next step is to configure the alert so that someone is notified at the right point (e.g. 80% of the forecast OR actual spend has been reached):

Once you have the budgets configured as you need, admins can track and forecast Syntex PAYG costs as we progress through a month or quarter, and you're in control of your spend. Job done.

Summary

Being able to pay only for what you use opens the door to Syntex adoption without an extensive business case and/or complex licensing decisions. As of January 2023 the capability is only in preview, and only for Syntex unstructured document understanding - the other two document processing flavours (unstructured and freeform) are already covered since they are charged through AI Builder credits in the Power Platform. However, this is the first truly native Syntex capability to get PAYG - and in the future we can expect Content Assembly, eSignatures, backup/restore, archiving and other Syntex capabilities to be charged in this way or at least have a PAYG option. Having this done through Azure Cost Management provides an existing model which admins in most organisations will already be very familiar with. 

Microsoft Syntex - December 2022 update and compiled articles

Microsoft Syntex has been one of Microsoft's biggest announcements in 2022 - which can be somewhat confusing because it existed previously as SharePoint Syntex since 2020 - but Syntex is expanding massively from "AI that understands your documents so you can automate processes" to an entire suite of advanced capabilities related not just to your documents, but also your images and videos. Some of the bigger recent or forthcoming features include document eSignatures, annotations, image recognition, automated document summaries and translations, auto video transcription and much more - many of which were announced at Microsoft's Ignite conference in October 2022. I'm hearing Microsoft folks say that "Syntex could be as big or bigger than the Power Platform in time", which is an interesting thought given the impact that has had.

Over the last 2 years I've been writing a lot about Syntex on this blog and thought it would be good time to do two things:

• Provide a 'Syntex on a page' round-up of the current and future capabilities    
• Provide links to my Syntex articles from one place

This article provides both of those.

Syntex on a page - December 2022

If you're confused about what's in Syntex and what's coming, I like to put things into these buckets:

• Content understanding and processing - using AI to understand your documents and automate something 
• Example - find all risk assessments missing a start date and contact the supplier organisation
• Example - read an insurance contract and save the policy details to a database
• Content assembly - automated creation of new documents
• Example - generate a new contract for every new starter
• Content management and governance - premium document management capabilities 
• Example - send a completed contract for eSignature (like DocuSign or Adobe Sign but fully integrated into Microsoft 365) and move location once complete
• Example - automated detection of pay review documents so specific security or policies can be applied without manual tagging 

The image below (click to enlarge) shows what's in Syntex today and what's in the roadmap:

Hopefully that helps position the today/tomorrow capabilities somewhat.

 

Compilation of my articles

I’ve been slowly creating a back catalogue of Syntex articles as I research, learn, and write about the technology. I’ve covered concepts such as training Syntex to read and understand documents, extracting data from forms, automating the creation of new documents, using Syntex in a fully automated process (Straight Through Processing) and various hints and tips articles. Here's a list of Syntex articles which might be useful, starting with the fundamentals and moving into more advanced topics:

• Microsoft Syntex - training Syntex to read your documents like a human
• https://cob-sp.com/Cortex-Syntex-Classifier
• Microsoft Syntex - training Syntex to read your documents like a human - part 2 (entity extractors
• https://cob-sp.com/Cortex-Syntex-Extractor
• Microsoft Syntex - teaching AI to extract contents of structured documents with Form Processing
• https://cob-sp.com/Syntex-Forms-Processing
• Microsoft Syntex AI - tips for choosing between document understanding and form processing models
• https://cob-sp.com/Syntex-model-tips
• Microsoft Syntex AI - my top 5 real-world tips
• https://cob-sp.com/Syntex-Tips
• Automate creation of new documents with Microsoft Syntex Content Assembly
• https://cob-sp.com/Syntex-Content-Assembly
• Microsoft Syntex Content Assembly - hints and tips
• https://cob-sp.com/Syntex-Content-Assembly-tips
• Identifying Syntex use cases - how the Microsoft Syntex assessment tool can help
• https://cob-sp.com/Syntex-Assessment-Tool
• Microsoft Syntex - new support for full document automation scenarios with Power Automate 
• https://cob-sp.com/Syntex-Power-Automate
• Syntex Pay As You Go - how it helps and how to use it
• https://cob-sp.com/Syntex-PAYG  

Note that there have been some renames along the way, and some of those articles might contain the old names. Here are some examples:

• SharePoint Syntex -> Microsoft Syntex
• Document understanding -> Unstructured document processing
• Forms Processing -> Structured document processing

Hopefully the links above are useful on your Syntex learning journey.

Reminder - why is Syntex important?

One way or another, automation will always be a theme of many of the I.T. projects undertaken in the next few years. The trend is increasing, with analysts predicting a $30b market by 2024 (IDC) and Gartner saying 60% of organisations are pursuing four or more automation initiatives. There are many technologies in the space, but Microsoft Syntex changes the game because advanced AI and document automation tools are now baked into the core productivity platform used by 91% the world’s top businesses (i.e. Microsoft 365) - inexpensive, readily available and democratised for every business.

 

It's no surprise Microsoft are investing heavily here. Every organisation has thousands of processes needing human input. If you consider Financial Services as just one industry, banks deal with applications for loans, mortgages, credit cards, loyalty schemes and many other products. Insurance companies quote, sell, and renew policies for home, car, travel, pet cover and more – and those are just the obvious products and services. Zooming out, every industry you can think of has an entire ecosystem of processes that can be optimised.

 

As Syntex powers are amplified and more capabilities are added, spending time evaluating what Syntex can unlock is likely to be valuable for many organisations.