TrueContext 2026 Product Roadmap: AI-Augmented Field Service Today

So let’s talk about that. The first part of this. The first part of this is the technician. Okay? What I’m going to share with you is industry research and from conversations with customers, the challenges are showing up consistently, okay, and across geographies, across industries that we serve, and experience levels. So what’s the first one? This technician, as you see it in that picture, I think describes it in a great way. Technicians can’t get to the information they need when they need it. They’re on a mission, it’s hard to prepare for a job, it’s very hard to have all the pieces clear. The knowledge exists, that’s not the issue. It exists somewhere in a manual, in a past service record, in a colleague’s head, but it is not at their fingertips. That leads to delays, to phone calls to people taking them off the job, and ultimately frustration. It shows over and over again, I mean, after many years of service council and other data providers and industry analysts, it keeps on showing, it’s very disappointing. So the second one is that after they finish that great mission, they have to document it. And they have to document it for customers, compliance, for audit readiness. It’s not optional. Still today, those processes are manual and or slow, and is one of the top complaints we hear. The third one is after they’re done, guess what? They gather a lot of knowledge. And we’re trying to extract that knowledge and put it into a way that can affect the next mission to the same asset or other missions. And here’s the thing that ties it together. The tools that most technicians are using today were not designed for this reality. And they were designed for capture checkboxes, readings. I mean, we’re very good at designing those. However, they’re not ready for observations, for insights, for experiences that are actually easy to capture and share. So the next part of the challenge, it comes down to your life, guys, the back office. The back office and are, I mean, struggling. It’s struggling mainly through, I hear three main things, okay? You want to build a new service because guess what? Fix and repair is not going to do it for all of us together. So you want to start a new revenue line, you want to start a new workflow, a new capability, and your back office system don’t give you an easy way of doing that. The next one, I think is even more challenging. You want to improve the productivity. We’re all on the same page, but it’s not easy. AI is a great tool, but it’s not easy to understand how can it help you. And the best way to apply it, we believe is on the field technicians. That’s where the higher productivity can be achieved. And I mean, multiply that by the number of technicians you have, and it’s definitely the right place to do it. The last part is that as we gather more information in the field, everybody guess what, they want more. Rightfully so, product managers, I mean, the R and D groups, everybody wants to sense everything that is in the field. Not easy. Where do you put a video that you bring from the field right now? How do you process it? How do you extract what is meaningful? Overall, there is a sense that we’re going and we’re in the middle of an innovation opportunity and it is stalling. So we have a vision and we have a clear approach to this. And I’m going to invite Glenn to come and share with us, Glenn, how is our product team? How are you realizing that vision? And how are we helping and be helping our customers to address some of the pains that I just described?

One of the best feelings that we have at TrueContext is when, experienced technicians tell us that they enjoy using the product. They’ve, and that they find it it deploys well and really helps them out on on their job. So let me show you how we’re looking to make this real. So what does context look like in true context? We’re gonna talk a lot about context and its importance today. You probably heard about it in the thinking about, you know, context engineering and other things in AI. For us, let’s think about the technician’s mission. Alvaro touched on this earlier, thinking about the jobs that are getting done as being missions to the technicians. So to start with, they’ll often get dispatched or assigned work, or maybe it’s up to the technician to discover or figure out what’s the best thing to do next and make the decision on what to do. In any of those cases, they have an objective. And often, they start with incomplete information. Maybe it’s just an address or a work order number, an observation, or or due date when when some kind of service needs to be, completed. And in any of those cases, their success depends on what they know and what they can find out along the way. So thinking about the core of that context, it’s what the technician starts with. So it’s the form workflow itself, prepopulated with job info, asset data, customer details, perhaps. It’s a structured, dynamic, and visual workflow. It’s the baseline context that they have to work with when they’re on that job. This has always been true context strength. It continues to be as we continue to support multiplatform offline and key aspects of the product. That mean that we are built for the field, and we’re uniquely qualified to be in the hands of technicians. From there, we talk about expanding context. So expand is what they what the technician can look up, what they can access when they need it, when they’re on that mission within true context. So work history, which is a new set of features that we’re working on, gives them what happened on similar missions in the past. So past service events, prior readings, what’s worked before, what was tried. In addition to that, our resource library gives access to reference materials, manuals, guides, troubleshooting docs. When the tech hits unfamiliar territory, the answers are at their fingertips coming from those sources. Extend is what they can reach out for from the TrueContext workflow. So on demand integrations, let them call out to external systems mid workflow. So this is also this is also new coming to the TrueContext platform. We’re talking about access to live inventory through ERP or parts systems, customer and service data from CRMs, any any number of systems that that might be useful for the technician as they’re working through those missions. More importantly, every day, outputs from AI services and and computer vision services that our customers are enabling, that they’re building internally that we can prompt directly from the TrueContext workflow out in the field with the latest information collected in that workflow to get great responses from those services that are coming online now. When technicians need something that isn’t already in their context, we can get at it without leaving the workflow, and the workflow at work adapts based on those responses. Each layer expands what’s possible with with the the right context available, the technician you know, the the odds of the probability of them succeeding, getting through the the work with velocity and with good good quality increases.

Work history is launching in February. This is the first major piece of the expand layer that I just showed you. There’s two parts to it. Record history gives technicians access to recent service records, not just their own but from other records that were worked on by others in their team. It gives them access to important aspects like what was done on this asset in the last three visits. What did that technician find? What did they recommend for follow-up? What did they try that maybe didn’t work? That context is now at the technician’s fingertips right inside of the current workflow. Data history lets the tech see how specific values have trended over time. So things like pressure readings, inspection scores, measurements, any measurement or metric that matters. So instead of seeing a single data point, they can see a pattern. Is this getting worse? Is it stable? What kind of trend visibility that can affect how technicians approach a job and how effective they can be? It could impact things like maintenance plans. Does this asset need to be maintained sooner than planned or maybe it can go a little bit longer before the next maintenance? Works on images as well. It gives you the ability to capture images consistently over time and and visually show you how something has been changing over time. So you can choose to link to record and data history by asset, site, project ID, customer, and other ways. So this technician sees what’s relevant to the mission that they’re on right now relevant to the project that they’re on, the asset that they’re looking at, the customer whose equipment they’re working on. So again, this is really foundational. It becomes more valuable the longer you’ve been capturing data with TrueContext and makes that data accessible in the moment for the technician.

Looking further ahead to the second half of this year and later in the first half of the year, we’re investing in advanced data capture capabilities. This is about making it easier for technicians to capture richer information, not just checkboxes and and readings, but narratives, observations, and insights, making that data more readily available and usable to your internal stakeholders and your customers. Give a quick preview of what we’re thinking of in this area and what we’re starting to build. Smart text capture, lets technicians speak or type quickly, and AI cleans it up. Proofreading, formatting, applying style standards that you may have within your organization. The technician focuses on the content, not the polish. Smart photo profiling allows the capture of multiple photos very rapidly, and the technician can describe them all in a single voice note, will sort through the note, and apply the observations know, very quickly to and and effortlessly to those photos so that you have you’re building a much richer, well described repository of photos, what’s happened in the field within your true context environment. Video capture and analysis enables short videos with, narration transcribed and and searchable and analyzed according to, what your business requirements are and what your use case requires. So think about capturing a sound, a vibration, a process that’s hard to describe in text along with, a narrative from the from the technician that explains why this is important and why they captured it. Last in this category, the voice to form functionality can take spoken narratives and automatically extract structured data from those to populate form fields. The technician talks through what they’re seeing, the system captures it in a usable format within the true context form workflow as you know it today. So the direction here overall is clear. We wanna help technicians capture what they actually know, not just what what fits in a checkbox. We want to use technicians’ time as effectively as we possibly can and streamline that data collection, give you more options to collect more data that’s gonna help you that help benefit your organization. So here’s how all of this connects together and why it comp compounds over time. Looking at workflow in the field, starting with preparation, This is dispatch and preloading of data as the technician launches into the work with a true context workflow already populated with data. They they typically don’t start from zero or they don’t stay at zero for very long. Moving to AI assisted service execution, as they work through the job, they can draw on work history to see what’s happened before, easily access and search your supporting content through the resource library, and reach out through on demand connections to pull data from external systems, catching the benefit of everything that’s in work history and the other systems that you have made available through on those on demand connections and through traditional data sources, of course. With intuitive data capture, as they complete the work, we’re giving them better tools to capture not just structured answers, but narratives, thought processes, observations, allowing for richer data flowing back into the system. And that produces new insights on the asset, the customer, and the project. These feed into the what we call the field intelligence core that is your true context service. Every job completed adds to that repository, every narrative, every photo, it’s all compounding. AI services enriching and connecting this data together, making it all available for the, you know, for the next mission that’s gonna be taken on. So the flywheel, it the more it turns, the more valuable it becomes. Every service call, every form submission makes the flywheel stronger. So we’re not just building features here, we’re building a context layer that’s going to power the connected worker and really improve your performance over time.

Here’s something that’s true even for the most advanced AI systems in the world. If you ask an AI assistant, what’s the weather in my city right now? It doesn’t know. It has to call out to a weather service to get that answer. Large language models that are available at the moment weren’t trained on this morning’s weather. It’s unlikely that any model ever will be. So when we’re calling out to something like a weather service from within an AI assistant, This is referred to as tool calling. The AI recognizes I need information that I don’t have, but I do know how to get it. I’m going to reach out and get that and bring it into the context to help the user out. The same principle applies to your field operations. Your true context data is incredibly valuable, lots of service history, asset data, manuals and supporting documentation that you can get access to. But it’s not going to contain everything a technician might need in a given moment as they’re working through a work order. That’s what on demand connections is going to enable. And this is the extend layer of our context that we talked about a moment ago. Mid workflow, a technician can call out to an external system and bring live information back. This is not data that’s coming from an offline cache. So let’s take a look at how that works. On the left you have a technician collecting data in a TrueContext workflow: asset information, readings, photos at some point in the workflow they need information that doesn’t yet exist in TrueContext. The workflow calls out to the TrueContext cloud, which connects to one of two types of on demand connections. On demand data sources tend to connect to your systems of record. So your relationship management systems, your service systems, asset management, health and safety, etcetera. These return live data sets using dynamic queries. These are queries that are going to tend to be constructed using data that was just collected in that workflow in the field. For Salesforce, we have a dedicated connector for other systems like Maximo, Dataverse, and Oracle Fusion apps. We have configurable connectors that work with a wide range of standard APIs. The on demand data sources bring data back into standard true context lookup controls. So these are things that you can easily configure that you may be familiar with in our form builder today. This allows the technician to browse, filter, sort and select from that data that is returned from that remote system, and allows the pushing of that data upon selection into the current workflow. On demand webhooks require additional configuration but give you even more flexibility. So these are great for connecting to AI agents, web services, including AI capabilities that your organization is building internally. So this is where you can plug in computer vision services, diagnostic services, other custom enterprise specific services that aren’t available in public LLMs. Importantly, it also gives the ability to connect with LLMs that you may be running from your own cloud environment. A scenario where you’re maybe running a GPT model from OpenAI inside of Microsoft Azure because you want to control the LLMs that a technician can access maybe for compliance reasons. I want to emphasize here that we’re really putting focus on composing prompts and queries for your users dynamically, so they don’t have to worry about the details, and enabling your workflow designers to control those prompts themselves so they can make them specific to your workflow and specific to your objectives. The technician is not going to have to type in things like what you see on the left in the device. Things like asset IDs or reading levels because those have already been collected in the form or been harvested from another system out in the field. So again what comes back can either be presented to the technician for reference or actually integrated into the workflow, changing what happens next based on the response. And importantly, once that data is retrieved from the remote system, offline functionality is preserved. The technician can continue working even if connectivity drops. And with the end result, the workflow is submitted, of course, and includes the enriched data that’s been added by the on demand callouts, including the details on the remote systems that were used. So richer context produced better decisions in the field.