Describe the goal, current systems and constraints. We will review the scope and identify the next evaluation step.
Expected value, implementation cost, and the measurement method are agreed before development begins.
Milestones are set around data readiness, integrations, security review, and the production environment.
Current cost, cycle time, quality, capacity, or risk is established before improvement is measured.
Results are reported against approved source data, a defined system boundary, and an agreed measurement period.
ALLTIPLY builds business cases and ROI models for AI initiatives before money is committed. We gather the evidence, verify the inputs, and build a scenario model where every number is tagged by where it came from: client data, a stakeholder statement, an external source or an assumption.
The model, dashboard, narrative and deck all draw on the same numbers, and the assumptions that move the result most are shown first. The business-case evidence review in the production-gap report describes how we checked draft claims against meeting transcripts, corrected inputs and kept speculative value separate. It documents a built model, not a measured return on investment.
These firsthand reports distinguish production use from proof environments and prototypes.
Explore our work and evidence status or read the research library directly.
Four phases from evidence gathering to a business case leadership can act on.
We collect internal data, stakeholder input and external research into a sourced evidence ledger, with each figure tied to where it came from.
We check the inputs that drive the result against the original records and replace untraceable figures with stated ranges.
We build scenarios, value layers and sensitivity analysis in a workbook with built-in checks, and review it with your finance team.
We deliver the model, an interactive dashboard, an executive narrative and a deck, all built from the same verified numbers.
