TargetBoard connects planning, code, delivery, quality, and AI data into a trusted operating view. See what drives engineering performance, whether AI investments deliver measurable returns, and where risk is emerging. Domain-expert agents help you investigate and act.
Built for CTOs, VPs of Engineering, and Delivery Leaders.
Cycle time is rising for the Checkout initiative. Coding time is stable, but pull requests are waiting longer for review.
Your teams have data on tickets, pull requests, deployments, incidents, and AI usage. Understanding how those signals affect one another takes more context. A shorter coding phase may still lead to longer reviews. Higher throughput may come with more rework. AI usage may rise while delivery stays flat.
Engineering intelligence connects the work, the people and teams doing it, and the outcomes. It gives leaders a way to answer the questions behind the numbers:
AI adoption and generated code show how much a tool is used. Engineering intelligence shows what changes as a result. TargetBoard connects AI-assisted work to the engineering outcomes leadership cares about and helps you evaluate the return on your investment.
See who is using AI tools, where adoption is growing, and how usage varies by team and workflow.
Connect AI-assisted activity to code changes, pull requests, and completed work using available tool and engineering metadata.
Compare throughput, cycle time, review effort, rework, defects, and delivery predictability with each team's own baseline. See where AI helps and where faster code production shifts work downstream.
Bring license, token, and agent costs together with delivered outcomes. Evaluate trends such as cost per delivered change and capacity recovered, with quality and rework in view. Build an ROI case using your own cost data and agreed definitions.
Are we getting more valuable work delivered for the money we spend on AI, without increasing quality risk?
TargetBoard's metadata-first approach can measure AI-assisted engineering work without requiring source-code access, prompt inspection, or software on developer machines.
Explore AI Adoption, Impact & ROIExplore the metrics that matter, with the context needed to interpret them and decide what to change.
See which commitments are likely to slip and why. Connect throughput, cycle time, lead time, planned versus actual work, scope changes, dependencies, and initiative risk.
Understand where capacity goes and where work gets stuck. Examine queues, review time, rework, and unplanned work across teams and workflows, with context for differences in the work they do.
See whether faster development leads to better releases. Connect changes to tests, defects, deployments, and incidents to find sources of downstream rework and recurring production issues.
See where AI improves engineering outcomes and where it increases cost or risk. Read AI-assisted work and spend alongside throughput, cycle time, quality, and each team's own baseline.
Know where engineering effort is going. Connect roadmap work, maintenance, incidents, and other demands to investment and outcomes, with a shared view for Engineering, Product, and Finance.
Engineering leaders cannot investigate every metric change by hand. TargetBoard's domain-expert agents monitor the connected engineering system and bring material changes to your attention with the context to investigate them.
Risk and predictability agents surface slipping initiatives, changing scope, and dependencies before a commitment is missed.
Velocity and quality agents help identify bottlenecks, rework patterns, and quality issues behind performance shifts.
AI Impact Agent looks for adoption or spend that is rising without a corresponding improvement in delivery, productivity, or quality.
Analyst and reporting agents help answer follow-up questions and turn findings into useful updates for teams and leadership.
Open any metric's Improve tab to generate an analysis and suggestions informed by your company context. Set alerts for the changes that matter to your organization.
Cycle time is rising for the Checkout initiative. Coding time is stable, but pull requests are waiting longer for review. Unplanned production work has also increased for the two teams on the critical path.
Detect: An agent flags the change in cycle time and the initiative it affects.
Understand: Drill into review wait, unplanned work, and the teams on the critical path.
Act: Rebalance review ownership, address the production interruptions, and revise the forecast with the evidence behind it.
Measure: Watch whether review wait falls and the initiative returns to plan without a decline in quality.
Illustrative example. Findings and recommended actions depend on your connected data and workflows.
Engineering data rarely arrives clean or consistent. Teams use different tools, custom fields, workflow states, estimation methods, and definitions of “done.” Work can span repositories, releases, support tickets, and organizational changes. Acquisitions add yet another set of systems and conventions.
TargetBoard connects the underlying operational data and models the relationships that matter to your business. Our team helps map source fields, reconcile definitions, validate calculations, and maintain the model as processes evolve. You get metrics and AI context grounded in the way your organization actually works, including complex and unsanitized data.
Bring together work tracking, source control, CI/CD, testing, incident management, support, and AI-tool data. Your teams keep their existing workflows.
TargetBoard's semantic layer aligns work items, teams, initiatives, changes, and outcomes. Start with established engineering KPIs, then adapt calculations, workflow stages, ownership, and reporting to your business.
Explore dashboards and reports, ask questions in natural language, set alerts, and use agents to investigate performance. Your teams can build on the same governed context through TargetBoard's MCP server.
The platform provides a ready foundation. Our team works with you on integrations, definitions, validation, and ongoing refinement so the intelligence stays useful as your organization changes.
If you want to create an internal engineering intelligence solution, you do not have to start by building and maintaining every connector, metric definition, data model, and agent capability yourself.
Start with TargetBoard's integrations, engineering metrics, dashboards, alerts, and agents. Adapt them to your teams and reporting needs.
Use the governed data layer and MCP server as the foundation for your own dashboards, workflows, and internal AI experiences. Your team focuses on the parts that are unique to your business while TargetBoard supports the underlying context and ongoing maintenance.
Either way, you can move from fragmented source data to usable engineering intelligence without taking on a long-term internal data infrastructure project.
Explore Build With TargetBoardFollow work across planning, development, release, and production in one operational model.
Use proven KPIs as a starting point and adapt them to complex workflows, organizational structures, and unsanitized source data.
Domain-expert agents, alerts, and contextual analysis help leaders understand changes, investigate causes, and choose the next step.
Read AI adoption and spend against delivery, quality, and productivity outcomes and build a defensible ROI case.
Use the ready platform or build your own intelligence experiences on its governed data layer and MCP server, with our team helping the solution evolve.
“TargetBoard hones in on the most critical metrics for my team without the mess of stitching multiple platforms.”
Bring us your toughest questions about delivery, productivity, quality, or AI ROI. See how TargetBoard connects your data, explains what is changing, and helps your team improve the result.
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