Modernization and solution accelerators
A SnapAI accelerator is a bounded set of architecture patterns, delivery assets, and evaluation structures. It can reduce repeated setup work, but it is not a finished product and does not predetermine whether a solution is viable.
Each foundation is adapted through assessment, integration, security, governance, testing, and accountable human decisions before anything is approved for use.
Delivery lifecycle
The same foundation can contribute across a delivery lifecycle. At each stage, evidence and project constraints determine whether to continue, revise the approach, or stop.
Decision question
Is there a defined problem, accountable owner, and credible reason to proceed?
Reusable contribution
Readiness prompts, workflow-mapping formats, dependency checks, and initial risk questions.
Required output
A bounded problem statement, known gaps, decision criteria, and explicit no-go conditions.
Decision question
How should the capability fit the target environment and operating model?
Reusable contribution
Reference boundaries, interface patterns, data-flow templates, and control checklists.
Required output
A target architecture with integration, identity, security, data, and ownership decisions recorded.
Decision question
What should be retained, wrapped, replaced, or retired, and in what sequence?
Reusable contribution
Dependency-mapping formats, migration-slice patterns, compatibility checks, and rollback prompts.
Required output
A sequenced modernization path that respects business continuity and change constraints.
Decision question
What must be configured, integrated, secured, and tested for this environment?
Reusable contribution
Project scaffolds, adapter interfaces, observability hooks, test structures, and delivery templates.
Required output
An integrated increment with traceable assumptions, controls, tests, and unresolved issues.
Decision question
What evidence is sufficient for the intended use and the cost of error?
Reusable contribution
Evaluation-set structures, acceptance-test patterns, review workflows, and reporting formats.
Required output
Findings, limitations, remediation actions, and an accountable go, revise, or stop decision.
Decision question
Who will own, operate, monitor, change, and support the delivered capability?
Reusable contribution
Runbook outlines, decision logs, training formats, monitoring baselines, and change-control prompts.
Required output
Named ownership, operational documentation, knowledge transfer, and an agreed support boundary.
Foundation register
These are reusable delivery foundations, not a menu of off-the-shelf AI products. A foundation may be combined with another, narrowed substantially, or set aside when assessment shows that a different approach is more appropriate.
Foundation A
For bounded, multi-step work where software may coordinate tools, information, approvals, and exceptions.
Foundation B
For finding, extracting, summarizing, or routing information from approved organizational sources.
Foundation C
For forecasting, prioritization, anomaly detection, or risk signals that inform rather than replace accountable decisions.
Foundation D
For introducing new capabilities around legacy platforms without assuming that full replacement is the right answer.
Delivery boundary
Reuse should make assumptions easier to inspect and routine work easier to repeat. It cannot make an unready process, unsuitable dataset, weak control, or unresolved ownership question safe by default.
Start with the constraint
We can assess whether a reusable foundation fits, what would still need to be designed and verified, and what a responsible first delivery boundary could be.
