Patient data systems, compliance reporting, and workflow automation for regulated environments.
Real-time tracking, route optimization, and inventory visibility across your distribution network.
Scale your product infrastructure, integrate third-party tools, and ship features faster with reliable ops.
Secure transaction processing, regulatory reporting, and customer-facing portals for financial services.
The azure devops time tracker problem hit us in the middle of a sprint review. We were sitting with a client, looking at velocity data that did not make sense, and the answer turned out to be simple: developers had been logging actual hours in a shared Excel file instead of inside Azure DevOps work items. Half the data was missing. The sprint metrics were guesswork.
We had seen this pattern before at QServices. Across dozens of delivery engagements in our azure devops consulting services practice, the same thing repeated: teams do their work in ADO, but log time somewhere else, or not at all. Work items show story points. They do not show the 12 hours a developer spent debugging a deployment issue that derailed the entire sprint.
That gap is what we built TimeTrack to close. It is a free Azure DevOps extension that logs developer hours directly inside ADO work items, with no additional tools required. This post covers the full build story: the problem we kept running into, the architecture we chose, the reason we made it free, and what it actually delivers for teams that need accurate velocity data without changing their workflow. You can install it today at https://timetrack.qservicesit.net.
Spreadsheet-based time tracking is a compromise most engineering teams accept because the alternatives either require changing tools or paying for a SaaS product. Neither is great when your team is already inside Azure DevOps all day.
The honest truth about time logging in software teams is that it almost never happens in real time. Developers log time at the end of the sprint, at end of week, or when a project manager asks for a report. By then, the data is reconstructed memory, not actual hours. That is not useful for velocity calculations, client billing, or any planning that depends on understanding where effort actually went.
The spreadsheet problem is fundamentally a data quality problem. When time data lives outside ADO, it is disconnected from the work items it describes. You cannot query it alongside task completion data. You cannot filter by developer, sprint, or epic without manual joins. You cannot surface it in ADO dashboards without building a separate integration that someone has to maintain.
We ran into this most painfully when clients asked for time-boxed delivery reports. "How many hours did the team spend on the authentication module in Q2?" is a simple question. When the answer requires cross-referencing two separate systems, it becomes a half-day exercise that nobody wants to do.
"Ghosted hours" is the term we use internally for time that was worked but never recorded. Across our microsoft azure consulting company engagements, ghosted hours typically represent 15-25% of actual developer time. They inflate perceived velocity (the work is counted as completed without recording the effort it cost), make sprint planning inaccurate, and create gaps in compliance documentation.
Key Insight Across our microsoft azure consulting company engagements, ghosted hours typically represent 15-25% of actual developer time.
For teams in regulated industries like healthcare or financial services, untracked hours are not just inconvenient. They become a liability when auditors come asking for delivery records tied to specific tasks and dates.
An azure devops time tracker is a tool that lets team members log hours directly against ADO work items, making time data queryable, reportable, and linked to the tasks that generated it. It differs from general project time tracking software in one key way: the data lives inside ADO rather than in a separate application that requires its own login and reporting workflow.
TimeTrack is QServices' free implementation of this concept. It installs from the Visual Studio Marketplace in under five minutes and adds a time logging panel to every work item in your ADO project, with no configuration beyond the initial install.
Azure DevOps ships with two native time fields: Original Estimate and Remaining Work. Neither is sufficient for actual time tracking. Original Estimate is set once at task creation. Remaining Work is updated as work proceeds. Neither field gives you a log of actual hours worked per session, per developer, or per date.
Without actual time entries, you cannot answer the questions that matter most for planning and billing: who logged the most hours on this feature, how much time did we spend on bug fixes versus new development this sprint, and how does our actual effort compare to our estimates over time.
The paid Azure DevOps time tracking extensions in the marketplace are solid products, but they add a per-user monthly cost on top of existing ADO licensing. For teams of 20-50 developers, that compounds quickly. Some also store data in external servers, which creates data sovereignty concerns for enterprise clients and teams with strict azure security assessment requirements.
Our view: basic time logging against work items should not cost extra. It is a fundamental delivery metric, not a premium feature.
TimeTrack adds a time entry tab directly to the ADO work item form. When a developer opens a User Story, Task, or Bug, they see a panel for logging hours alongside the existing fields. There is no context switching and no additional login.
Installation is a one-time admin action. You install the extension for your ADO organization, grant it access to the projects you want active, and it appears automatically in all work items in those projects. There are no configuration scripts, no database setup, and no service endpoints to configure. This matters for teams that have had bad experiences with ADO extensions that require days of onboarding before they become usable.
The logging interface is intentionally minimal: date, hours, optional description. A developer opens their Task or User Story, clicks the TimeTrack tab, and creates a time entry. The entry is associated with the work item ID and the developer's ADO identity automatically, so there is no manual attribution required.
Multiple developers can log against the same work item independently. TimeTrack aggregates all entries so you can see both the total hours per work item and each contributor's individual time, all within the standard ADO work item view.
Beyond individual work items, TimeTrack surfaces aggregated time data at the sprint and epic level. A dashboard widget shows total logged hours for the current sprint broken down by work item type, developer, or date range.
This is the view that makes sprint retrospectives genuinely useful. You can see exactly where time went, not just which story points were marked done. For teams already using ADO alongside Power Automate workflows for process automation, this data feeds naturally into broader reporting pipelines without any additional integration work.
TimeTrack is built as a standard Azure DevOps extension using the Azure DevOps Extension SDK. It runs entirely within the ADO context, which means no external servers, no separate databases, and no authentication to manage beyond ADO itself.
For enterprise teams with strict security requirements, this architecture answers the most common questions upfront: where does the data go, who can access it, and does anything leave the tenant.
The extension manifest defines two contribution points: a work item form group (the time logging panel visible on each work item) and a dashboard widget (the sprint summary view). Both use the standard ADO contribution model, which means they receive the ADO identity context automatically without custom authentication logic.
The work item form group uses the ms.vss-work-web.work-item-form contribution point, which renders inside the standard work item form alongside fields like Assigned To and Iteration Path. This placement is what makes the experience feel native rather than bolted on.
Time entries are stored in ADO's extension data service, a key-value store scoped to your collection and project. Each time entry is a JSON document keyed by work item ID, containing an array of entries with timestamp, user, hours, and optional description. No personally identifiable data leaves your ADO organization.
This follows the same data handling model as other approved enterprise ADO extensions, which is why it tends to clear security reviews quickly. For organizations building hybrid cloud azure setup policies or evaluating tools as part of an azure infrastructure assessment, this architecture is easier to approve than extensions that depend on external data stores.
The honest answer: we built it for ourselves first. The initial version of TimeTrack was an internal tool QServices developers used on client projects to solve a problem we kept running into. Once it was stable enough, making it free for the broader ADO community was the obvious decision.
There is also a practical benefit for QServices. Teams that use TimeTrack and later need help with broader work, whether as an azure migration partner for a cloud transition, an azure managed services provider for ongoing infrastructure operations, or a partner for azure app modernization work, tend to reach out to us because they have already seen how we think about tooling problems. The extension is credibility before the first conversation.
We do not capture your data or monetize your usage. TimeTrack runs entirely within your ADO organization. Our cost to maintain it is low, which makes free sustainable long-term. The marketplace listing shows exactly what permissions the extension requests before you install, so there is nothing hidden or unexpected.
A solid azure devops time tracker does more than tell you where hours went. It changes how you plan, estimate, and communicate about software delivery.
Story points estimate complexity. Hours measure effort. When you have both, velocity calculations become more reliable and sprint planning becomes less dependent on intuition. You can identify which types of work consistently take longer than estimated and which parts of the codebase are expensive to touch.
This connects directly to a problem we wrote about in depth: why Azure DevOps time estimates are often wrong. Without historical time data per work item type, estimation is mostly institutional memory. With it, you can build data-driven estimates that hold up across teams and quarters.
For organizations in regulated industries, time records tied to specific work items and developer identities provide meaningful audit trail data. Whether you need to show a client that a fixed-price engagement was delivered within scope, or demonstrate to an auditor that a specific security task was completed on a particular date, ADO-linked time records are substantially more credible than spreadsheet exports.
Teams pursuing SOC 2, ISO 27001, or project-level financial audits will find this directly useful. It also aligns well with Power Platform governance requirements in environments where auditability across the Microsoft stack is a standing requirement.
TimeTrack works as a standalone tool, but it fits naturally into how we think about azure devops consulting services more broadly. Accurate time data is useful on its own. It becomes substantially more useful when paired with well-structured ADO projects, sensible branching strategies, and automated pipelines.
Teams that start with TimeTrack often come to us asking about the next layer. Some need an azure migration partner to plan a migrate on premise to azure transition, and they want time tracking in place before the migration starts so they have a clean baseline for comparing pre-migration and post-migration developer effort. Others are running azure cloud migration services projects where they need accurate effort tracking across migration phases, including azure landing zone implementation work, early infrastructure builds, and lift and shift to azure phases for legacy workloads.
QServices is a Microsoft Certified Solutions Partner specializing in Azure, and we have completed 500+ Azure and Microsoft platform projects since 2014. Some clients engage us for Power BI consulting services to visualize TimeTrack data alongside other delivery metrics in executive dashboards. Others want a full azure architecture review before scaling their ADO setup or starting a power platform development company engagement to build internal tooling on top of their Azure investment.
The delivery patterns we see across those engagements informed every design decision in TimeTrack. It is not a side project. It is the tool our own developers use on the projects that pay our bills.
The azure devops time tracker problem is solvable without additional spend or workflow disruption. TimeTrack logs developer hours directly inside ADO work items, which means time data is where it belongs: linked to the work that generated it, queryable alongside ADO reports, and accessible to every team member without a separate login.
We built it because we were tired of spreadsheets and ghosted hours on real client engagements. It installs in under 5 minutes, all data stays inside your ADO organization, and it costs nothing to use.
Install TimeTrack free at https://timetrack.qservicesit.net and see how much cleaner your next sprint retrospective looks when you have actual hour data to work from.
If you want to go further, whether that is azure cost optimization consulting, a full azure architecture review, or migrating legacy workloads as part of a broader azure app modernization program, the QServices team is ready to help. Start with the free tool. See where the data points next.
Written by Rohit Dabra
Co-Founder and CTO, QServices IT Solutions Pvt Ltd
Rohit Dabra is the Co-Founder and Chief Technology Officer at QServices, a software development company focused on building practical digital solutions for businesses. At QServices, Rohit works closely with startups and growing businesses to design and develop web platforms, mobile applications, and scalable cloud systems. He is particularly interested in automation and artificial intelligence, building systems that automate routine tasks for teams and organizations.
Talk to Our ExpertsYes. TimeTrack by QServices is a free extension that logs developer hours directly inside Azure DevOps work items. It installs from the Visual Studio Marketplace in under 5 minutes, requires no external database or server, and stores all time data within your ADO organization’s built-in extension data service. There is no per-user fee and no subscription required.
An ADO organization admin installs TimeTrack from the Visual Studio Marketplace. After installation, you grant the extension access to the projects you want it active in. It then appears automatically in all work items in those projects with no additional configuration, scripts, or database setup needed.
TimeTrack uses Azure DevOps’s built-in extension data service, a key-value store scoped to your ADO collection and project. No data is sent to or stored on QServices infrastructure. All time entries remain within your ADO organization, which makes the extension compatible with enterprise data sovereignty and security review requirements.
TimeTrack captures actual hours worked per work item alongside existing story point estimates. With both metrics available, teams can identify which work types consistently take longer than estimated, spot patterns in over-running tasks, and build data-driven estimates over time. This addresses a root cause of inaccurate velocity: estimates based on memory rather than historical effort data.
Yes. Because each time entry is linked to a specific ADO work item ID and ADO user identity with a timestamp, the data provides a traceable audit trail showing who worked on what deliverable and when. This is directly useful for SOC 2 compliance, ISO 27001 audits, fixed-price project scope verification, and any regulated-industry audit that requires time records tied to specific tasks.
Azure Integration Services Explained: Logic Apps, Service Bus, API Management, and Event Grid Rohit Dabra | June 30, 2026 Summarize
Power BI Embedded is Microsoft’s developer-focused API for embedding interactive analytics directly inside third-party apps, customer portals, and SaaS products. If you are building software and want customers to see live dashboards without logging into the Power BI service, this is where that journey starts. The question is not whether you can embed Power BI reports, you almost certainly can. The real question is whether it makes financial and architectural sense for your specific situation. This guide covers the when, the how, and the cost math that most tutorials skip.
Power apps portals sit at an interesting crossroads for IT leaders: they’re fast, deeply integrated with the Microsoft stack, and manageable without a dedicated development team. But they’re also constrained in ways that matter when your business needs a portal that handles complex UI logic, third-party integrations outside the Microsoft ecosystem, or pixel-perfect UX design.
This guide gives you a straight comparison so you can make the right call without spending three months in discovery. We’ll cover what each option actually delivers, where each breaks down, and the governance questions that need answers before you commit either way.
If you’re evaluating your Microsoft stack more broadly, our breakdown of Power Platform vs Custom .NET Development provides useful parallel context.
Azure AI Foundry is reshaping how enterprise teams build, deploy, and govern AI at scale, and the comparison with AWS Bedrock has become one of the defining platform decisions of 2025. If your organization runs on Microsoft 365, Teams, or Dynamics 365, or if you’re planning azure cloud migration services in the near term, the platform you choose here will affect every AI workload you build for the next five years.
This post cuts through the marketing to compare both platforms on model selection, developer tooling, enterprise security, cost, and real-world fit for Microsoft-ecosystem businesses. We’ll also answer the PAA questions that IT leaders keep searching for, including whether Azure is cheaper than AWS for enterprise and what an Azure managed services provider actually does.
React Native is a cross-platform framework built by Meta that allows development teams to write a shared JavaScript codebase and deploy to both iOS and Android. For enterprise architects evaluating mobile strategy in 2025, the choice between react native development, Flutter, and Xamarin goes well beyond which syntax your team prefers. It touches deployment timelines, maintenance costs, existing skill sets, and how tightly the front end needs to connect to your backend infrastructure.
This post breaks down all three frameworks across performance, developer experience, enterprise support, and Azure cloud integration. By the end, you’ll have a clear picture of which framework fits your organization, and when alternatives like Power Apps make more sense than a custom mobile build.
AI agent governance is the practice of establishing policies, controls, and human oversight mechanisms that determine how AI agents operate, make decisions, and interact with business systems. For enterprises deploying AI today, this isn’t optional paperwork. It’s the difference between AI that delivers measurable value and AI that creates liability.
The pressure to ship AI quickly is real. Microsoft Copilot, Azure OpenAI, and Power Platform’s AI Builder have made it easier than ever to wire autonomous agents into workflows. But “easy to deploy” doesn’t mean “safe to leave unsupervised.” Every enterprise that skipped governance in the rush to launch has eventually paid for it, whether through data leaks, compliance failures, or decisions no one can explain to an auditor.
This post covers why human-in-the-loop (HITL) oversight is non-negotiable for enterprise AI, what a real governance framework looks like, and how QServices approaches this with clients across healthcare, banking, and logistics.
Power BI Embedded is Microsoft’s developer-focused API for embedding interactive analytics directly inside third-party apps, customer portals, and SaaS products. If you are building software and want customers to see live dashboards without logging into the Power BI service, this is where that journey starts. The question is not whether you can embed Power BI reports, you almost certainly can. The real question is whether it makes financial and architectural sense for your specific situation. This guide covers the when, the how, and the cost math that most tutorials skip.
Power apps portals sit at an interesting crossroads for IT leaders: they’re fast, deeply integrated with the Microsoft stack, and manageable without a dedicated development team. But they’re also constrained in ways that matter when your business needs a portal that handles complex UI logic, third-party integrations outside the Microsoft ecosystem, or pixel-perfect UX design.
This guide gives you a straight comparison so you can make the right call without spending three months in discovery. We’ll cover what each option actually delivers, where each breaks down, and the governance questions that need answers before you commit either way.
If you’re evaluating your Microsoft stack more broadly, our breakdown of Power Platform vs Custom .NET Development provides useful parallel context.
Azure AI Foundry is reshaping how enterprise teams build, deploy, and govern AI at scale, and the comparison with AWS Bedrock has become one of the defining platform decisions of 2025. If your organization runs on Microsoft 365, Teams, or Dynamics 365, or if you’re planning azure cloud migration services in the near term, the platform you choose here will affect every AI workload you build for the next five years.
This post cuts through the marketing to compare both platforms on model selection, developer tooling, enterprise security, cost, and real-world fit for Microsoft-ecosystem businesses. We’ll also answer the PAA questions that IT leaders keep searching for, including whether Azure is cheaper than AWS for enterprise and what an Azure managed services provider actually does.
React Native is a cross-platform framework built by Meta that allows development teams to write a shared JavaScript codebase and deploy to both iOS and Android. For enterprise architects evaluating mobile strategy in 2025, the choice between react native development, Flutter, and Xamarin goes well beyond which syntax your team prefers. It touches deployment timelines, maintenance costs, existing skill sets, and how tightly the front end needs to connect to your backend infrastructure.
This post breaks down all three frameworks across performance, developer experience, enterprise support, and Azure cloud integration. By the end, you’ll have a clear picture of which framework fits your organization, and when alternatives like Power Apps make more sense than a custom mobile build.
AI agent governance is the practice of establishing policies, controls, and human oversight mechanisms that determine how AI agents operate, make decisions, and interact with business systems. For enterprises deploying AI today, this isn’t optional paperwork. It’s the difference between AI that delivers measurable value and AI that creates liability.
The pressure to ship AI quickly is real. Microsoft Copilot, Azure OpenAI, and Power Platform’s AI Builder have made it easier than ever to wire autonomous agents into workflows. But “easy to deploy” doesn’t mean “safe to leave unsupervised.” Every enterprise that skipped governance in the rush to launch has eventually paid for it, whether through data leaks, compliance failures, or decisions no one can explain to an auditor.
This post covers why human-in-the-loop (HITL) oversight is non-negotiable for enterprise AI, what a real governance framework looks like, and how QServices approaches this with clients across healthcare, banking, and logistics.
Earning Global Recognition: A Testament to Quality Work and Client Satisfaction. Our Business Thrives on Customer Partnership
