From a defined use case to repeatable evaluation
Data analysis and enterprise AI for a clearly evaluable problem, with data-readiness assessment, access controls, and defined human review.
For: Product, operations, data, and transformation leaders seeking an evidence-based decision on an AI use case or fragmented business data.
Data remains scattered across systems, while finding document answers or preparing reports depends on manual work. AI demonstrations can enter decision-making without quality criteria, data permission, or cost evaluation. The core task is identifying work that data and models can support at an acceptable level of risk.
We select one decision or bounded task and establish its current baseline, data sources, and consequences of error. Analytics, deterministic rules, or AI are assessed against that need. A prototype uses permitted data and representative evaluations; production depends on quality, cost, and required controls.
Evidence to continue, revise, or stop a use case, alongside shared metric definitions and visible data limitations. The aim is a considered production decision with quality and cost that can be evaluated after release.
Engagement scope
We start with the simplest approach that can be evaluated. Analytics prioritizes metric definitions and source quality; model work separates evaluation data from development. Model access must respect user permissions, and sensitive actions require appropriate human approval and event records.
How we work
Define a bounded task, user, baseline, and quality threshold; consider rules or analytics as alternatives.
Establish quality, permission, access, and processing location, then prepare representative evaluation examples.
Build with permitted data and assess quality, material errors, cost, and latency on the evaluation set.
Use evidence to continue, revise, or stop; define human review and monitoring when proceeding.
Deliverables
Engagement prerequisites
Scope boundaries
Final scope, acceptance criteria, timing, and responsibilities are defined in the proposal and agreement.
Documented experience
Assess relevant experience
No approved case study is published for this service. In an initial discussion, focus on evidence relevant to your own problem.
Examine the defined use case, data permission, baseline, evaluation set, and observed errors, with clear human-review responsibilities and model limitations.
Before deciding
No. Stable rules and exact outputs often favor simpler, more predictable deterministic automation. Models are considered for language, documents, or patterns that conventional approaches handle poorly and that can be evaluated meaningfully.
Quality, access, ownership, and usage-rights gaps are documented first. Data preparation, metric definitions, or source integration may be prerequisites for a pilot. Starting a prototype does not commit the project to production.
Using representative examples and criteria such as correctness, completeness, source traceability, behavior when no answer is available, and error consequences. Cost and latency are measured separately. The owner agrees acceptance thresholds; a demonstration does not replace independent evaluation.
That is not assumed. Sensitivity, usage permission, processing location, provider terms, and deployment options are reviewed. Real-data use requires suitable access and approval from the data owner within the agreed scope.
Make an informed decision
Enterprise processes
Before buying or building ERP or BPMS, clarify process and data ownership, exceptions, permissions, and pilot success criteria with a practical readiness checklist.
Read the guideAI and data
Choose between rules, analytics, and AI with business examples, an evaluation table, access controls, and criteria for continuing or stopping a bounded pilot.
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Learn moreStart a conversation
Describe the task, available data, and consequences of error. Keep confidential data out of the initial request.
Request a use-case and data-readiness reviewNext-step objective: Assess fit for AI or analytics and outline prerequisites and criteria for a potential pilot.