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Start with the simplest approach that meets quality bars—complexity adds cost and maintenance.
API-only suits structured tasks with small context. RAG suits document Q&A with citations. Fine-tuning suits stable style/format with enough labeled data and ops budget.
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Vendor proposes fine-tuning when RAG would suffice—or vice versa.
A structured delivery path—not vague promises.
Classification, Q&A, generation, agent.
Volume, freshness, sensitivity.
Measure quality and cost.
RAG layers, then fine-tune if justified.
Balanced guidance—not one-size-fits-all answers.
Agents orchestrate tools; still often use RAG/API underneath.
Proof aligned to this topic—not generic filler.
Primary capability pages for this topic.
Internal AI assistants over your approved documents—with citations, permissions, refusal rules, and update workflows.
Production AI features for live mobile and web products—audits, prioritization, guardrails, and integration without breaking your release cadence.
What drives AI project budgets—API vs. RAG vs. agents, data prep, evaluation, guardrails, and ongoing model costs.
API or RAG first; fine-tune when evaluation proves need.
See private RAG assistant solution.
See AI agent development solution.
AI app development cost resource.
Strict data boundaries—usually RAG with permissions.
AI integration assessment on contact.
