Agile metering services: metrics, dashboards, forecasts

AGILE DELIVERY INTELLIGENCE

Making Agile Delivery
measurable & predictable

measuring, forecasting & decision-making systems inside your existing tool stack

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Where 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?

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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. Layers 1–3 of the Framework below.

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. Layers 4–5 of the Framework below.

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

THE FRAMEWORK

Agile Delivery Intelligence — five layers you will own

A working system installed inside your existing Jira and tool stack. We build, train, and hand over — no proprietary tooling, no consultant dependency. Each layer is sellable on its own, but designed to compound when installed together.

Demand & Requirements Engineering

The work that ensures everything downstream operates on a clean, well-structured input rather than a noisy backlog.

  • Structured demand intake and prioritisation logic (portfolio & roadmap)
  • Engineered epic / feature / story architecture and hierarchy
  • Traceability across portfolio, program and team levels & artifacts hierarchy
  • Refinement governance, gates, and Definition of Ready / Done design
  • IREB-CPRE-grade requirements rigor applied to agile development

    • Clean data foundation

    The plumbing work that makes Jira or other operational system a reliable source of truth — without which dashboards lie and forecasts mislead.

  • Data, workflow, fields consistency across teams
  • Data clean-up, analytics & forecasts foundation (field hygiene and data governance)
  • Single, organisation-wide definition of done
  • Integration with adjacent tools (Confluence, GitHub, CI/CD)
  • Configuration of advanced roadmaps, structures, JQL

    • Multi-level Analytics & Insights

    Delivery made legible to the business — at team, program, and portfolio levels.

  • Real-time dashboards across team / program / portfolio
  • Integration into analytics Power BI, SAP SAC, Tableau, Qlik, etc & customizing
  • Executive scorecards and board-ready views
  • Performance, scope, velocity, throughput and waste tracking for current increments & future projection
  • The Agile Balanced Scorecard — requirements, delivery, QA, UAT, Ops, customer value & voice

    • Forecasting & Scenario Simulation

    ML-based and probabilistic forecasting, plus the ability to answer “what if?” before the decision is made.

  • ML-based PI completion forecasting (models training and selection, model accuracy evaluation)
  • Scope-at-risk detection — flagged from sprint 2 of the PI (roll-over prediction, scope completion commit)
  • The multi-Horizon Capacity Simulation Model
  • Scenario simulation: “what if we add a team / descope this / shift the date”
  • The Agile Delivery Forecasting Model

    • Operationalised Decision-Making

    Insights integrated into the planning rhythm — so Jira becomes the system of decision, not merely the system of record.

  • Forecast integration into refinement, PI planning, capacity & resource utilization optimization
  • Data-driven nomination and prioritisation
  • Mid-PI rescoping enabled by live trajectory data
  • Decision making on scenario simulation of the upcoming increment
  • Preciser roadmap planning due to combination of forecasts with adopted estimation techniques 
  • Closing the loop: from dashboards-nobody-acts-on to a delivery organisation that operates on its own data
    • The full Framework runs in approximately two months, structured in four phases. Modular entry points are available for clients who only need one or two layers.
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    Engagement model

    Delivery intelligence models for data-driven decisions

    HOW WE WORK

    ENGAGEMENT MODEL

    Each phase installs one or more layers of the Framework. Part-time engagements are accepted; the four-phase logic remains the same

    Goal settings & discovery (1-3w)

    We start by understanding your current setup, processes, Jira hygiene, requirement quality, and the decision-making gaps that hurt most.

    • Audit of current setup, processes, tools
    • Align on business goals, priorities & target outcomes
    • Define metrics, measurement points, planning horizons
    • Identify data sources & gaps
    • Plan the layer installation sequence

    Output: A one-page measurement charter you agree to

    Foundation & Analytics (2-4w)

    We engineer the requirements layer, clean and structure Jira, and stand up multi-level analytics across team, program, and portfolio

    • Demand & requirements engineering setup
    • Jira foundation — workflows, fields, governance
    • Multi-level dashboards (Power BI, SAP SAC, Tableau, jira, etc.)
    • Reports, scorecards, and executive views
    • ntegration into your existing BI tooling

    Output: A live, trusted measurement layer running on engineered requirements.

    Forecasts & Simulation (3-6w)

    We add the ML-based forecasting and scenario simulation that turn the measurement layer into a forecasting system.

    • Data cleanup and validation
    • Selection and training of forecast models
    • Multi-horizon Capacity Simulation Model
    • Scenario simulation for "what if?" planning
    • Data driven capacity & scope planning

    Output: Working forecasts and scenario simulations, ready for planning use.

    Enable & improve (ad-hoc)

    We integrate forecasts and insights into your planning rhythm, train your team to run the system, hand over ownership & improve on demand.

    • Integrate insights into PI planning, refinement, prioritisation
    • Train teams and stakeholders to run the system
    • Enablement & change management
    • Monitor and improve continuously
    • Measure impact & value

    Output: Jira operating as a decision-making system. Your team owns it.

    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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