Practical tools for choosing the right AI approach.

Decide whether your use case is better served by automation, a chatbot, RAG, a copilot, or a more capable agent.

Assess whether the workflow, data, access, and risk controls are mature enough for a real pilot.

Get a practical recommendation for how to start based on team size, skill, urgency, and compliance pressure.

Estimate how much build and operational complexity a proposed AI system is likely to create.

Estimate time savings, cost impact, and likely business value before committing to a build.

Turn a broad AI idea into a tighter first version with clearer users, inputs, outputs, and guardrails.

Choose between patterns like RAG assistant, workflow agent, approval-gated agent, or multi-agent setup.

Figure out whether a given failure or task needs retrieval, memory, workflow state, or a clarifying question.

Check whether your documents, freshness rules, and ownership model are strong enough for retrieval to work.

Decide whether the problem belongs in prompts, code, or a hybrid approach with both.

Get a recommended starting architecture based on autonomy, data shape, action model, and team profile.

Define what the system should remember, what it should avoid storing, and how that memory is corrected.

Choose a starter stack based on whether you need retrieval, workflow orchestration, and observability.

Decide where approvals, review points, and escalation paths belong in the workflow.

Evaluate completeness, consistency, recency, access control, and ownership in the data behind the system.

Build a first evaluation plan for answer quality, action safety, human review, monitoring, and rollback.

Identify where privacy, compliance, auditability, and action controls need to show up before rollout.