Comparison

Modularity is not free, and AI systems have specific boundaries worth drawing. This guide covers which separations pay and which add coordination for nothing.

Batch is cheaper and simpler; streaming is necessary when someone is waiting. This guide covers choosing per workload rather than per architecture.

Central governance is consistent and slow; federated governance is fast and inconsistent. This guide covers the hybrid that most organisations end up needing.

Many small tools give agents flexibility and more chances to go wrong. Fewer, larger tools are more reliable and less adaptable. This guide covers the trade.

A platform team builds shared capability; embedded engineers build domain knowledge. This guide covers the trade and the rotation that keeps both honest.

Multi-agent architectures are frequently chosen for elegance rather than need. This guide covers what separation actually buys and what it costs in coordination.

If a task takes longer than a user will wait, holding a connection open is the wrong design. This guide covers where the threshold sits and what async requires.

Specialist AI firms and large integrators solve different problems. This guide covers where each is stronger and which suits which stage of a programme.

Startups move faster and may not exist in three years. Enterprise vendors are durable and slower. This guide covers reducing the risk either way.

Document extraction looks buildable until the document variety arrives. This guide covers what platforms solve, what stays yours, and where the cost really is.

RAG platforms handle ingestion and search competently. What they cannot handle is your corpus quality, your permissions, and your definition of a good answer.

The components of a voice agent are available to anyone. Coordinating them in real time, with interruptions and telephony, is the part worth buying.

The choice is about where capability ends up living, not only about who writes the code. This guide covers speed, cost, and what each arrangement leaves behind.

A single model is simpler; routing between several saves cost and latency. This guide covers when the saving justifies the complexity and what it requires.

The build-versus-buy line for agents falls in an unusual place, because the hard part is your process and your permissions rather than the agent mechanics.

Finance AI operates where errors have direct consequence. This guide covers accuracy, control preservation, auditability, and the reconciliation that catches drift.

Healthcare AI sits under specific regulation and affects patient care. This guide covers classification, validation evidence, and where clinical judgement must remain.

HR AI makes decisions about people, which puts it under specific regulatory attention. This guide covers fairness, explainability, and where decisions must stay human.

Legal AI is judged on whether citations are real and current. This guide covers verification, jurisdiction coverage, confidentiality, and the review that cannot be skipped.

Security AI is judged on whether it reduces analyst load without missing what matters. This guide covers detection quality, false positives, and response limits.

Chatbot platforms are judged on escalation and context access more than on conversation quality. This guide covers what to compare — and whether chat fits.

Your CRM vendor has added AI features. This guide covers evaluating them honestly against standalone tools, and the governance questions nobody asks.

ERP AI features operate on data with financial and regulatory consequence. This guide covers verifying accuracy, controls, and auditability before enabling them.

ITSM AI features promise deflection and triage. This guide covers measuring them honestly and the knowledge quality that decides whether they work.