Below is a pragmatic, build-ready map of AI APIs by capability, with selection tips, integration patterns, and a 30–60–90 day plan. Focus on evidence‑grounded outputs, predictable latency/cost, and governance from day one.
How to choose AI APIs (fast checklist)
- Capability fit: Do they cover the exact tasks needed (RAG, tool‑calling, JSON‑mode, streaming, batch)?
- Latency and throughput: Sub‑second for inline hints; 2–5 s for drafts; batch for heavy jobs. Check regional endpoints.
- Cost control: Clear pricing, token usage visibility, caching options, and budgets/quotas; track cost per successful action.
- Governance: “No training on customer data” options, PII redaction, residency/VPC paths, audit logs, model/version registry.
- Reliability: SLA, fallbacks, multi‑provider routing, and well‑documented failure modes (timeouts, rate limits).
- Tooling: SDKs, structured outputs (JSON), function/tool‑calling, eval suites, and observability hooks.
Core categories and strong options to shortlist
- Large language models (text + reasoning)
- Use cases: chat, summarization, structured extraction, planning, code assist, JSON tool‑calling.
- What to require:
- JSON‑constrained output, system prompts, function/tool‑calling, streaming, batch jobs, latency tiers, eval examples.
- Safety controls, refusal/grounding modes, token/latency budgets.
- Integration tips: Wrap behind your own gateway with small‑first routing and per‑surface budgets.
- Speech (STT/TTS) and audio understanding
- Use cases: meeting notes, call analytics, IVR, real‑time agent assist, multilingual voice UX.
- What to require:
- Low‑latency streaming, speaker diarization, word‑level timestamps, punctuation, domain‑adaptation, neural TTS with voice settings and caching.
- Vision and document intelligence
- Use cases: OCR, invoices/receipts, IDs, diagrams, UI screenshots, product images, charts/tables.
- What to require:
- Multi‑page OCR, table/key‑value extraction, layout coordinates, redaction, confidence scores, doc classifiers, image quality tools.
- Translation and localization
- Use cases: product UI, support replies, knowledge bases, transcripts, global SEO.
- What to require:
- Glossaries/style, formality control, HTML/Doc translation, batch jobs, PII masking, domain adaptation.
- Search and retrieval (RAG stack)
- Use cases: grounded chat, help, policy answers, product/docs Q&A.
- What to require:
- Hybrid search (keyword + vector), reranking, filters by tenant/role, chunk provenance, timestamps, embeddings API, freshness rebuilds.
- Agents and tool‑calling middleware
- Use cases: multi‑step workflows that read, decide, and write back to systems with approvals.
- What to require:
- Deterministic schema validation, idempotency keys, retries, planning with verification, state management, policy hooks.
- Safety, redaction, and content filters
- Use cases: prompt hardening, PII masking, toxicity/compliance gates, jailbreak detection.
- What to require:
- Inline/stream filters, redaction by entity type, audit logs of blocks/refusals, customizable policy sets.
- Evaluation and monitoring
- Use cases: regression gates, golden sets, drift detection, quality/cost tracking.
- What to require:
- Test runners for RAG/groundedness, extraction accuracy, JSON validity, latency p95/p99, token usage, refusal rates.
- Orchestration and serverless inferencing
- Use cases: queueing, fan‑out/fan‑in, retries, batch pipelines, multi‑model routing.
- What to require:
- Durable functions/queues, webhooks, cron, per‑flow budgets/alerts, secrets management, region routing.
- Domain APIs your SaaS likely needs alongside AI
- Payments/billing: subscriptions, metering on “successful actions.”
- Comms: email, SMS, chat for automations and NBA delivery.
- Storage/media: file ingest, image/video transforms.
- Search/analytics: logs, metrics, tracing for observability.
Reference integration patterns
- Evidence‑first RAG
- Ingest content → hybrid search → rerank → LLM with citations/timestamps → refusal when insufficient → cache snippets and explanations.
- Schema‑constrained actions
- LLM produces JSON conforming to your schema → validate → call domain APIs (CRM, ticketing, billing) with idempotency and audit logs.
- Small‑first routing
- Classify/route with compact models → escalate only for complex synthesis → cache embeddings/results, compress prompts.
- Progressive autonomy
- Suggest → one‑click → unattended for low‑risk paths; approvals + rollbacks for high‑impact changes.
Observability and SLOs you should implement
- Performance: p95/p99 by surface (100–300 ms hints; 2–5 s drafts).
- Quality: groundedness/citation coverage, JSON validity rate, refusal/insufficient‑evidence rate.
- Adoption: acceptance rate, edit distance for drafts, action success rate.
- Economics: token/compute per 1k decisions, cache hit ratio, router escalation rate, cost per successful action.
Security and governance must‑haves
- Data handling: opt‑out of training on customer data, PII redaction, tenancy isolation, region routing/VPC options.
- Change control: model/prompt registry, version pinning, champion–challenger, rollback gates.
- Audit: decision logs from input → evidence → route → action → outcome; export for compliance.
30–60–90 day implementation plan
- Days 1–30: Foundations
- Pick one workflow (support deflection, PRD/status drafting, invoice coding).
- Stand up: retrieval/search, one LLM with JSON mode, schema validator, and your first action connector with idempotency.
- Ship MVP: cited answers + one safe action. Instrument p95/p99, groundedness, JSON validity, acceptance, and cost/action.
- Days 31–60: Reliability and routing
- Add small‑first classifier, reranker, caching, prompt compression, and budgets/alerts.
- Introduce speech or vision if relevant (meeting notes, document intake).
- Start golden evals (retrieval accuracy, groundedness, extraction F1), and a value recap dashboard.
- Days 61–90: Scale and governance
- Add agentic multi‑step flows with approvals and rollbacks.
- Expose admin controls: autonomy thresholds, retention/residency, model/prompt registry.
- Add monitoring for router mix, refusal rate, interval coverage (if forecasting), and publish a case study with outcome lift and cost trends.
Practical tips to avoid common pitfalls
- Chat without execution: Always wire at least one safe write‑back with audit logs; judge on resolved outcomes.
- Hallucinations: Enforce citations and timestamps; block uncited outputs; schedule re‑indexing.
- JSON brittleness: Use strict schemas + validators; reject/repair loops; cap output tokens.
- Cost/latency creep: Cache embeddings/snippets, small‑first routing, per‑surface budgets, pre‑warm around peaks.
- Over‑automation: Keep approvals for pricing, credits, access; simulate/shadow before unattended.
Example “starter stack” by use case
- Support copilot (RAG + actions)
- Search/embeddings + LLM with JSON/tool‑calling + helpdesk/billing connectors + redaction/safety + eval/monitoring.
- Sales/RevOps assist (meetings → actions)
- Speech STT + LLM summaries + CRM tool‑calling + forecast intervals + analytics hooks; approvals for discounts/commit changes.
- Finance ops (AP intake and coding)
- Vision/OCR + extraction templates + LLM for narratives + ERP connector + evaluator for field accuracy and exception routing.
- Product/engineering (PRD/status + risk)
- RAG over issues/PRs/docs + LLM drafts with citations + planner for assignments + VCS/CI tool‑calling + latency budgets.
If helpful, share the specific workflow and constraints (latency, residency, budget), and I can map an exact set of API picks and a minimal reference architecture for your stack.
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