Notes Vertex AI pour B2B SaaS: séparer FAQ commerciale et historique support

Dans un B2B SaaS, tout ressemble à du contexte utile: FAQ commerciale, centre d’aide, tickets support, notes de vente, roadmap et réponses Slack. Si noms clients, contrats, incidents et fonctions non publiées entrent dans le même index, la FAQ devient un chemin de fuite.

Cet article utilise Claude Code pour préparer une note d’intégration Vertex AI avant l’implémentation. Le but est de séparer FAQ commerciale et historique support, réduire l’incertitude avant vente et protéger les données client.

Points clés

• Keep sales FAQ, help content, support history, deal notes, and roadmap data in separate buckets.

• Use Vertex AI RAG, Grounding, Agent Search, and safety filters after data boundaries are clear.

• Claude Code drafts classification tables, test questions, prompts, and operations notes.

• Humans decide customer names, contracts, incidents, unpublished features, legal wording, and pricing answers.

• Send implementation-heavy readers to training because every SaaS has different data rules.

Où les projets FAQ IA SaaS échouent

Commencez par quatre catégories: FAQ commerciale publique, FAQ support, revue interne, et ne pas envoyer à l’IA. Vertex AI RAG, Grounding, les data stores Agent Search et les safety filters aident à ancrer les réponses, mais ne décident pas quelles données SaaS conviennent à une réponse publique.

Official references: RAG Engine, Grounding overview, Grounding with Google Search, Agent Search data stores, Safety filters, and Responsible AI.

Flux: séparer les données avant Vertex AI

Commencez par quatre catégories: FAQ commerciale publique, FAQ support, revue interne, et ne pas envoyer à l’IA. Vertex AI RAG, Grounding, les data stores Agent Search et les safety filters aident à ancrer les réponses, mais ne décident pas quelles données SaaS conviennent à une réponse publique.

| Source | AI use | Human review |

| --- | --- | --- |

| Sales FAQ | public features, public pricing, buying questions | legal wording and pricing examples |

| Help center | operations, settings, roles, errors | old UI and deprecated features |

| Support history | anonymized patterns and frequent errors | customer names, contracts, incidents |

| Deal notes | usually no direct AI use | budget, discount, roadmap, buyer name |

Ce que fait Claude Code et ce que valide l’humain

Commencez par quatre catégories: FAQ commerciale publique, FAQ support, revue interne, et ne pas envoyer à l’IA. Vertex AI RAG, Grounding, les data stores Agent Search et les safety filters aident à ancrer les réponses, mais ne décident pas quelles données SaaS conviennent à une réponse publique.

3 cas d’usage

Use case 1: Split sales FAQ and support history

• Input: public FAQ, help articles, support CSV columns, ticket tags, pre-sales questions.

• Output: four-bucket table: sales FAQ, support FAQ, internal review, do-not-send-to-AI.

• Human review: check customer names, contracts, incident history, unpublished features, and special pricing.

Use case 2: Build test questions before Vertex AI

• Input: buyer persona, pricing page, help articles, prohibited answers, escalation rules.

• Output: allowed questions, handoff questions, and questions requiring source links.

• Human review: decide boundaries for pricing, contracts, security, incidents, cancellation, and custom requests.

Use case 3: Create operations notes for wrong answers

• Input: wrong answers, ticket logs, support corrections, FAQ update history.

• Output: issue class, data source to fix, retest question, and approver.

• Human review: legal wording, refund terms, incident explanations, customer impact, and release timing.

Prompt à copier

Create a Vertex AI integration note for a B2B SaaS. Goal: separate sales FAQ from support history and avoid sending customer data into broad AI context. Create four buckets, twenty test questions, handoff rules, source-link rules, and operations notes. Do not paste real customer values. Main CTA: /training/.

Code de vérification

const faqCandidates = [{ source: 'support_history', title: 'Client incident response', bucket: 'sales_faq', fields: ['customerName', 'incidentId', 'contractNote'] }];

const sensitiveHints = [/customer/i, /contract/i, /incident/i, /discount/i, /account/i];

const findings = faqCandidates.flatMap((item) => {

  const sensitiveFields = item.fields.filter((field) => sensitiveHints.some((pattern) => pattern.test(field)));

  return item.bucket === 'sales_faq' && sensitiveFields.length ? [{ title: item.title, issue: sensitiveFields.join(', ') }] : [];

});

console.table(findings);

if (findings.length) process.exitCode = 1;

Pitfall: RAG rend de mauvaises données crédibles

Commencez par quatre catégories: FAQ commerciale publique, FAQ support, revue interne, et ne pas envoyer à l’IA. Vertex AI RAG, Grounding, les data stores Agent Search et les safety filters aident à ancrer les réponses, mais ne décident pas quelles données SaaS conviennent à une réponse publique.

Cause: RAG brings answers closer to the indexed data. If the indexed data is wrong for sales use, the answer becomes confidently wrong or unsafe. Fix: split data before ingestion, use anonymized support patterns, and keep contracts, incidents, and roadmap notes out of public FAQ context.

FAQ

Q. What should come first? A. A data classification table, not a model setting.

Q. Should all support history be removed? A. No. Patterns and tags can help, but customer-specific details need separation.

Q. Why training? A. Vertex AI, Agent Search, RAG, data boundaries, and security review differ by SaaS.

Nous contacter

ClaudeCodeLab training can help create FAQ buckets, test questions, and operations notes before Vertex AI or Agent Search implementation.

Ce que j’ai vérifié

This article checks frontmatter, official links, three use cases, input-output-human review labels, code fences, and the /training/ CTA. The JavaScript sample detects customer, contract, and incident fields mixed into sales FAQ candidates.