Where Anthropic may fit
Claude may support assistants, agents, content and knowledge workflows, analysis, decision support and other AI-enabled experiences. Its safe architectural role depends on the context supplied, tools and data it can access, the decisions it may influence, evaluation evidence, human controls, monitoring and a clear fallback when model output is uncertain or unavailable.
Decisions the architecture must make explicit
- 01
Which use cases are suitable for a language model, and which must remain deterministic or human-led?
- 02
What customer, content and policy context may the model access, and under what permissions?
- 03
How will quality, safety, latency, cost, privacy and failure behaviour be evaluated before scale?
- 04
Where are human review, auditability, escalation and recovery required?
- 05
How much provider portability is required, and which application responsibilities should remain outside the model layer?
Where independent advice can help
- Frame a bounded Claude use-case, model-provider or build-versus-buy decision.
- Assess AI and decisioning readiness, including context, data access, evaluation, governance and accountable ownership.
- Define the boundary between probabilistic model behaviour, deterministic controls and human approval.
- Review implementation architecture and delivery decisions without promising model engineering or application build.
Boundaries and non-fit
- Prompt production, model training or production application development
- Security, privacy, legal or AI-safety certification
- Guaranteed model accuracy, performance, cost or availability
- A predetermined recommendation that must select Anthropic