Engineering Intelligence ![Engineering Intelligence Icon](https://cdn.sanity.io/images/3xg3qj5k/production/f3f0f6e4d144331e6e3160cc18c95520dde50cca-30x30.svg) # Identify what’s slowing delivery down Most engineering analytics stop at the repo. Skan AI doesn't. We observe how your work really flows, and turn it into one unified view of developer productivity. [Request a demo](/request-demo) Events processed 25B 25B Processes mapped 500+ 500+ Works with any application your team uses. ![Jira logo](https://cdn.sanity.io/images/3xg3qj5k/production/9f23d1ac4c24ca03b3dc113c2c2665ba05125e8e-49x49.svg) ![Jira logo](https://cdn.sanity.io/images/3xg3qj5k/production/9f23d1ac4c24ca03b3dc113c2c2665ba05125e8e-49x49.svg) ![Git Logo](https://cdn.sanity.io/images/3xg3qj5k/production/548375981c420cbaccb61490fceb45ab760e3769-49x49.svg) ![Git Logo](https://cdn.sanity.io/images/3xg3qj5k/production/548375981c420cbaccb61490fceb45ab760e3769-49x49.svg) ![Cursor Logo](https://cdn.sanity.io/images/3xg3qj5k/production/df334891e626fc60e1fe8dedf312c77422e7a15d-49x49.svg) ![Cursor Logo](https://cdn.sanity.io/images/3xg3qj5k/production/df334891e626fc60e1fe8dedf312c77422e7a15d-49x49.svg) ![Icon Box ](https://cdn.sanity.io/images/3xg3qj5k/production/e8605089e46c0bd0fdd99f3db3ab303bc9e316e6-45x46.svg) ### Understand the full delivery picture Git, Jira and surveys only tell part of the story.
Skan AI adds observed work patterns, giving engineering leaders a complete picture of how delivery actually happens. ![Icon Box 2](https://cdn.sanity.io/images/3xg3qj5k/production/e8605089e46c0bd0fdd99f3db3ab303bc9e316e6-45x46.svg) ### Measure what matters Engineering performance is shaped by more than tickets and commits. Skan AI captures the patterns, frictions and focus times that traditional engineering metrics often miss. ![Icon Box 3](https://cdn.sanity.io/images/3xg3qj5k/production/e8605089e46c0bd0fdd99f3db3ab303bc9e316e6-45x46.svg) ### See the impact of AI coding tools AI coding tools are everywhere, but you can’t see if they’re working. Skan AI shows the real impact of AI assistants on throughput. ## The observation-first method for engineering ![Observe](https://cdn.sanity.io/images/3xg3qj5k/production/72d0421360dc09b5b0d1075dee1c2b4dd9201a16-759x700.svg) Observe A lightweight sensor captures how developers actually work. It collects granular, real-time behavioral signals directly from the workflow. Every application interaction, collaboration touchpoint, AI-assisted action, and context switch. 1. Observe A lightweight sensor captures how developers actually work. It collects granular, real-time behavioral signals directly from the workflow. Every application interaction, collaboration touchpoint, AI-assisted action, and context switch. 2. Distill Observed work is translated into productivity signals. SDLC phases, context switching, focus time and the patterns no single tool sees. 3. Identify The patterns driving (and dragging) delivery, surfaced automatically. For true root cause analysis, not just a score. 4. Optimize Act on what you find. Workloads rebalanced, friction reduced, delivery improved, and the evidence to back every call. Get the full picture of how engineering ships ## Get the full picture of how engineering ships No integration projects required. Just immediate visibility. ![](/static/scans/cover-left.png) ## One dashboard for everything that shapes engineering performance Productivity index built from more data points than any other system Unified view across Jira, GitHub, and your existing engineering tools Insights into how developers use AI coding IDEs, and collaboration tools ![Monitor Every Workflow](https://cdn.sanity.io/images/3xg3qj5k/production/a7a54043120d216bfd9be7c1a7e1d40704bdafee-889x621.svg) ## Monitor every workflow and identify productivity bottlenecks Observation across every application developers use Application usage, switching, and copy/paste behavior tracking Separate collaboration activity from development work ## Discover the patterns driving delivery outcomes Activities grouped by SDLC phases for deeper analysis Root-cause analysis of high and low-performing teams Observation that scales across distributed engineering organizations ## The enterprises already scaling with proven ROI [See all stories](/case-studies) [ ![Definiti - Map the work. Then automate it | Client Success Story](https://cdn.sanity.io/images/3xg3qj5k/production/da847716989e8a92d985d7aeb057d4cc869e8c1b-984x549.jpg?q=80&fit=max&auto=format) 14,000+ 14,000+ hours/year of automation-ready effort Before, it was turn on AI and hope. Now we know exactly which processes to automate, what we'll save, and how to prove we got there. Jennifer Redden Head of Technology ![Definiti](https://cdn.sanity.io/images/3xg3qj5k/production/4bd9a3daea270bb97b789fc4d776f8a1d415d1e1-224x28.png?q=80&fit=max&auto=format) ](/case-studies/definiti-map-the-work-then-automate-it) ## Meet the Context Graph of Work. A structured, living model of how your enterprise actually operates, built from real observation. [How The Context Graph works](/context-graph-of-work) ## One platform. More to Skan. Blueprint [ ![Your AI masterplan](https://cdn.sanity.io/images/3xg3qj5k/production/33ba832e4a26326626c548e23e88a16d205e6896-536x464.svg) ](/blueprint) Your AI masterplan Recommendations, roadmaps and ROI. [Learn more](/blueprint) See how work really happens Maps, metrics and opportunities, built from real work. [Learn more](/process-intelligence) Deploy agents grounded in your reality Decisions, actions and outcomes, grounded in operational context. [Learn more](/agents) Your AI masterplan Recommendations, roadmaps and ROI. [Learn more](/blueprint) Frequently asked questions ## Frequently asked questions What is engineering intelligence? Engineering intelligence is the practice of understanding how software teams work across the software development lifecycle. It combines delivery data, workflow activity, collaboration patterns, and engineering behaviors to provide a more complete view of productivity and performance. How is engineering intelligence different from engineering analytics? Engineering analytics tools typically focus on repository, ticketing, or project management data. Engineering intelligence expands visibility beyond those systems to understand how work actually happens across tools, workflows, meetings, collaboration, and development activities. What factors influence engineering productivity? Engineering productivity is shaped by many factors, including focus time, collaboration patterns, context switching, workflow interruptions, delivery processes, technical dependencies, and organizational structures. Measuring productivity effectively requires understanding the full operating environment, not just output metrics. How can engineering leaders improve software delivery performance? Improving delivery performance starts with understanding how engineering work flows through teams and systems. Organizations can then identify bottlenecks, reduce unnecessary interruptions, improve coordination, and optimize workflows based on observed patterns rather than assumptions.