Agile metering services: metrics, dashboards, forecasts

AGILE DELIVERY INTELLIGENCE

Making Agile Delivery
measurable & predictable

measuring, forecasting & decision-making systems inside your processes & tools

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When delivery loses its predictability

Numerous software organisations ask versions of these four questions

Will we hit the date?

The board asks. The customer asks. The sales team asks. You answer with your gut. Engineering reality is somewhere else, and you find out at the next retrospective.

How much we can commit?

Teams over-commit. Capacity is "vibes." Working harder isn't shipping faster, and you can't tell whether team performance is improving or just feeling that way.

Can we trust our sources?

Reports exist. Insights are produced. But the org doesn't act on them. Forecasts are made in spreadsheets, monthly, by someone — and nobody trusts them.

Are we performing better?

Comparing to earlier Increments (PIs) - are we better in refining, developing and testing the scope, are we better scope focus, bugs reduction and fixing performance.

These aren't process problems. They're measurement problems. And measurement problems are solvable.

our approach

Predictable delivery rests on two pillars. We engineer both

Most organisations have one or the other. They have data, but they can't trust it. Or they have dashboards, but the org doesn't act on them. Agile Delivery Intelligence is the practice of engineering both halves so they work together

Trustworthy data

Engineered requirements, clean Jira foundation, and multi-level actionable analytics — so the numbers you look at are numbers you can defend in a board meeting.

Actionable decisions

ML-based forecasting and scenario simulation, integrated into the planning cycle — so the data becomes decisions you act on, weeks before the deadline.

Both halves. Installed in your stack. Owned by your team.

THE FRAMEWORK

Agile Delivery Intelligence — four areas where you benefit most

A working system installed inside your existing  operational environment. We build, train, and hand over — no proprietary tooling, no consultant dependency.

Portfolio & Roadmap

How feasible is our product roadmap and what is next prio?

  • Data-driven roadmaps, traceable from portfolio to team
  • Early effort and milestone estimates
  • Structured demand intake and prioritisation
  • Reliable roadmap commitments backed by forecasts
  • Delivery forecasts for high-level epics
  • What-if scenarios: add a team, descope, shift the date

    • Demand intake & planning

    What scope can we commit realistically next to?

  • Early estimates on structured / refined requirements
  • Data-driven increment planning and smart team balancing
  • Multi-horizon capacity simulation (cone of uncertainty)
  • Forecast-driven capacity estimates
  • Prioritisation based on forecasts and capacity
  • Scenario simulation to avoid overcommitment

    • Agile Delivery

    Are we on track with current delivery, detect scope-at-risk

  • Real-time, multi-level view of where we stand
  • Clear view whether teams and release scope are on track
  • Velocity, predictability and waste tracking with projection
  • Early warning of delays and topics at risk
  • ML-based forecasts for release and PI completion
  • Scope-at-risk flagged from sprint 2, mid-PI rescoping

    • Release & customer feedback

    Will scope be delivered in time & rolled out for customers.

  • Real-time testing and QA tracking & UAT coverage
  • Bug trends and early detection of quality risks
  • Release readiness and customer-voice visibility
  • Release-readiness forecast against the planned date
  • Escaped-defect risk forecast by product area

    • Delivery intelligence models for data-driven decisions

    HOW WE WORK

    ENGAGEMENT MODEL

    Our full framework is structured in four phases and several layers built in a modular way to meet exactly customer needs 

    Goals & Alignment

    We start by understanding current processes, data, and gaps which hurt most and drafting to-be set-up with metering & decision points

    • Understand most urgent issues
    • Align on priorities & target outcomes
    • Select analytical & forecasting cases
    • Fix scope, plan & commitment

    Output: Project goals, timelines and objectives. Fixed scope.

    Design & Foundation

    We discover and engineer the data layer, clean, enhance, structure and validate data & models, building data flows for needed metrics and insights

    • Check existing processes, data & tools
    • Identify data sources & gaps
    • Clean, enhance & validate data
    • Build data model, flows & transformations

    Output: A live, trusted, clean data foundation needed for analytics and forecasting.

    Build & Test

    We build multi-level analytics by reports, dashboards across teams, levels, select & train forecast models for multi-horizon plans & simulations

    • Build metrics, reports & dashboards
    • Build & Train forecast models
    • Testing & bugfixing
    • Integrate analytics & forecast into processes

    Output: Analytics & forecasts integrated into operational, planning and decision processes.

    Enable & Improve

    After integrating forecasts & insights into operations, we train your teams to run the system, hand over ownership & improve on demand

    • Train teams and stakeholders
    • Enablement & change management
    • Monitor and improve continuously
    • Measure impact & value

    Output: Operational & analytical tools handed-over to customer ready for further improvements.

    WHAT THE FRAMEWORK PRODUCES

    Tangible outcomes from recent engagements

    Numbers below are from recent engagements at large & midsize companies.

    PI Predictability
    PI Throughput increase
    Work in scope increase
    Rollover reduction




    frameworks we support

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