Ship a Grounded Answerbot with Vertex AI in 10 Days

I spend most days helping teams move from FAQ pages to answer engines that customers and search models trust. With Google’s latest releases, the runway just shortened.

Why now: Google announced Grounding with Google for Gemini models is generally available to improve factuality against Google data. Vertex AI Search expanded multimodal and multi-turn answers across enterprise sources. Vertex AI Agent Builder added source-level citations and response policies. Together, this stack makes a citation-rich brand answerbot something you can ship in ten days, not a quarter.

This guide gives you a reference architecture, a day-by-day plan, an evaluation rubric, and a cost model you can take to finance. I write it from the seat of a product and AEO strategist at Upcite.ai. When I train for a marathon, I do not try to set a personal best on day one. I hit the right pace, log clean miles, and avoid injury. You want the same in production: steady, grounded answers that do not hallucinate, with citations your legal team will sign off.

What “grounded” must mean in production

Grounded is not marketing language. For a brand answerbot, it means:

• Answers are supported by current, authorized sources you control or trust.
• Every substantive claim is backed by a visible citation that maps to a specific document, section, or field.
• The system abstains or escalates when it cannot retrieve adequate evidence.
• Access control is enforced so private content only shows to authorized users.
• The model’s generation is checked against retrieved evidence before finalization.

Non-goals for week one:

• Perfect small talk and humor
• Fully automated content maintenance without governance
• Broad domain knowledge beyond your product and policies

Reference architecture on Google Cloud

Here is the minimal, production-viable pattern using Vertex. You can add complexity later.

[User]
   |
   v
[Edge/API]  -> Auth, rate limiting, user context, locale
   |
   v
[Orchestrator (Agent Builder or custom)]
   |             |                      |
   |             |                      |
   |         [Vertex AI Search]     [Grounding with Google]
   |             |                      |
   |         Enterprise KB        Web/Google corroboration
   |             \__________________/ 
   |                      |
   v                      v
[Gemini 1.x]  <- system prompt with answer policy and citation template
   |
   v
[Verifier step]  Evidence check and citation validation
   |
   v
[Response]  Markdown answer with inline citations + structured payload
   |
   v
[Observability] Logs, traces, eval metrics, redaction, feedback

Key components:

• Content sources. Product docs, release notes, pricing, SLA, support articles, status, legal policies, CRM knowledge, case studies. Treat each as a governed source with owners.
• Vertex AI Search. Indexes enterprise content with connectors, supports ACLs, semantic and keyword retrieval, filters, and multi-turn context.
• Grounding with Google. Optional corroboration step against Google data to validate or enrich facts when policy allows public corroboration.
• Gemini model. Generates answers constrained by your answer policy. Use a strict response template that requires citations and abstentions.
• Agent Builder. Orchestrates tools, applies response policies, and logs sources used. New governance features expose source-level citations and response rules.
• Observability. Request and token logs, retrieval traces, per-citation IDs, evaluation events, latency and failure metrics.

The 10-day build plan

Day 0. Scope and success criteria

• Define top intents. Example: pricing overview, plan comparison, feature availability, integration steps, troubleshooting patterns, security posture, compliance, SLA.
• Choose surfaces. Web widget, in-product help, support console, internal sales enablement.
• Set answer policy. What the bot can answer, when it must abstain, how to escalate, tone, and citation rules. Write it as system instructions.
• Define SLAs. Target P95 latency, answerability rate, citation precision, and abstention accuracy.

Day 1. Content inventory and governance

• Inventory canonical sources. Mark anything not approved as out of scope.
• Add metadata. Owner, last updated, version, audience, region, product version.
• Normalize structure. Prefer HTML or Markdown with headings and anchors. For datasets, ensure field-level descriptions.
• Decide citation granularity. Section or paragraph level. Adopt stable anchors.

Day 2. Index setup in Vertex AI Search

• Stand up a Search app. Create collections for docs, support, legal, and product data.
• Ingest via connectors or batch. Map metadata to facets like product, version, region, role.
• Configure ACLs. Map to your identity provider for private content.
• Tune retrieval. Set synonym lists, stopwords, boosts for freshness and canonical sources.
• Validate with test queries. Confirm top 3 results are authoritative and current.

Day 3. Grounding and policies

• Enable Grounding with Google for allowed intents. Limit to corroboration or gap fill within policy boundaries.
• Write answer policy in plain language. Examples below.
• Define refusal messages for out-of-scope or low confidence.
• Set safety and response rules in Agent Builder. Disable risky tool calls.

Day 4. Prompting and output schema

• Author a strict system prompt. Include your answer policy, tone, and the citation format the UI needs.
• Define output JSON schema. Include fields: answer_markdown, citations[], sources_used[], confidence, escalation_reason.
• Add a verifier step. After generation, programmatically check that each claim is linked to a retrieved span. If missing, regenerate or abstain.

Day 5. Evaluation harness

• Build a golden set of 150 to 300 questions across intents, regions, and product versions.
• Create an adversarial set. Ambiguity, outdated facts, competitor bait, multi-hop, and policy boundary cases.
• Label expected citations. Exact document IDs and anchors that should appear.
• Implement scoring. See metrics section below. Automate runs and dashboards.

Day 6. UX and escalation

• Design the answer view. Clear citations inline and a sources panel with titles, anchors, and timestamps.
• Add feedback controls. Helpful, not helpful, wrong source, outdated, privacy issue.
• Define escalation routes. Ticket creation with conversation transcript and source list.

Day 7. Load test and latency tuning

• Simulate traffic at target QPS. Measure P50 and P95 for retrieval only, retrieval plus grounding, and full generation.
• Optimize. Reduce context length, pin retrieval k, enable caching, and prefetch likely sources.
• Cache warmup. Schedule index warmers for top intents.

Day 8. Security and compliance

• Run access tests. Ensure private docs never appear to public users.
• Redaction. Mask secrets and personal data in logs and prompts.
• Policy dry run. Verify that disallowed topics always refuse with the exact refusal template.

Day 9. Cost sample and contract check

• Collect per-query token and retrieval usage from traces.
• Model cost at expected volume. See cost section below.
• Review with finance and legal. Confirm data residency and retention settings.

Day 10. Soft launch and monitoring

• Launch to a controlled cohort. Add a banner that answers are grounded and cite sources.
• Monitor. Track reliability, latency, containment, and cost per resolution.
• Open the feedback loop. Content owners receive automatic alerts when citations point to outdated sections.

The answer policy I recommend

Use this as a starting point and adapt to your voice and risk profile.

• Scope. Only answer questions about our products, pricing, features, integrations, security, compliance, support policies, and troubleshooting covered by approved sources.
• Sources. Use Vertex AI Search results from approved collections as the primary evidence. You may consult Grounding with Google for corroboration if the policy allows public information. If sources conflict, prefer the most recent version with a canonical label.
• Citations. Every claim must include a citation mapping to a specific document and anchor. If you cannot include proper citations for all claims, abstain.
• Refusals. If a question is out of scope, ambiguous, or lacks sufficient evidence, respond with a refusal and offer to escalate.
• Truthfulness. Do not infer product commitments, timelines, or roadmaps unless explicitly documented.
• Privacy and safety. Do not include customer data, secrets, or personal information in the answer.

Evaluation plan and acceptance criteria

Track these metrics on your golden and adversarial sets and in production.

Grounding quality

• Citation precision. Of all citations shown, the share that actually support the claims. Target 0.95 or higher on golden, 0.90 in production.
• Citation recall. Of all claims that need evidence, the share that have a citation. Target 0.95 or higher.
• Source coverage. Percentage of answers that include at least one citation to a canonical source. Target 0.98 or higher.

Answer outcomes

• Correct answer rate. Human-judged correctness given the retrieved evidence. Target 0.90 or higher at launch, 0.95 after 30 days.
• Abstention accuracy. Share of refusals that were appropriate. Target 0.98 or higher.
• Overclaim rate. Answers that include commitments not present in sources. Target 0.0. Treat any instance as a defect.

Retrieval performance

• Retrieval hit rate. At least one relevant document in top 3. Target 0.97 or higher for top intents.
• Freshness. Share of answers that cite content updated in the last N days where applicable. Set N by your release cadence.

Latency and reliability

• P50 latency. 1.2 to 1.8 seconds for single-turn answers on warm cache.
• P95 latency. 2.5 to 3.5 seconds with Vertex AI Search and optional Grounding with Google. If you add tool calls or multi-hop retrieval, budget to 4.5 seconds.
• Success rate. 99.9 percent of requests complete without errors. 0 timeouts on golden set.

Business impact

• Containment rate. Percentage of sessions that do not escalate to human support. Baseline with your current search or bot.
• Cost per resolved answer. All-in model and platform cost divided by resolved answers. Track trend after content improvements.

Sample size and cadence

• Pre-launch. 300 golden, 150 adversarial queries. Pass if all grounding and reliability targets are met.
• Post-launch. Daily eval jobs on rotating 100-sample subsets. Weekly regression with the full set.

Cost model you can take to finance

Costs depend on your negotiated rates. Use this framework and plug your numbers.

Per query cost components

• Retrieval. Vertex AI Search query charges.
• Grounding with Google. Optional verification or enrichment call.
• Generation. Gemini tokens for prompt plus completion.
• Orchestrator and logging. Minor overhead.

Example at 100 thousand queries per month

Assumptions for illustration only. Replace with your rates.

• Average of 2 Vertex AI Search queries per user question including follow-ups.
• Grounding with Google on 40 percent of questions that allow public corroboration.
• Gemini usage of 3,000 prompt tokens and 500 completion tokens per answer after compression and citation formatting.
• Token costs scale with model tier. Choose a balanced tier for production.

Compute rough per 1,000 queries

• Retrieval. 2,000 search calls.
• Grounding. 400 verification calls.
• Generation. 3.5 million tokens.

Total monthly cost scales with traffic and your unit prices. In most support and docs scenarios, grounded search plus light generation beats naive RAG on both cost and reliability because:

• You avoid building and maintaining your own vector stores and custom retrievers.
• Vertex AI Search handles ACLs, freshness, and ranking with less engineering effort.
• Grounding with Google reduces hallucination retries that waste tokens.

To pressure test, run a 3-day cost sample:

• Send 10 thousand representative queries through the full pipeline.
• Log exact counts for search calls, grounding calls, and tokens.
• Compute variance across intents. Identify heavy prompts to compress.

Optimization levers

• Cache stable answers. Popular definitions and policy snippets can be cached by locale and version.
• Reduce grounding where high-confidence enterprise sources already agree.
• Trim prompt boilerplate. Move policy to a short system template and a separate tool schema.
• Keep citations concise. Do not include full paragraphs in the answer when an anchor suffices.

Reliability and latency targets leaders should require

Set clear targets in your PRD and vendor SOW.

• P95 latency under 3.5 seconds for single-turn answers on warm cache.
• Error rate below 0.1 percent excluding client cancels.
• Retrieval hit rate above 0.97 on top intents.
• Citation precision and recall above 0.95 on the golden set.
• Zero known overclaim defects in production. Any instance triggers a rollback or policy tighten.

Operational practices

• Canary cohort. 5 percent of traffic gets new retrieval settings first.
• Dual logging. Store retrieval traces and citations for 30 days with PII redaction.
• Content SLOs. Owners must update critical docs within 24 hours of a release. The bot flags stale citations automatically.

A note on pacing. In a marathon, going out too fast ruins your finish. The analog in answerbots is enabling too many intents and public corroboration on day one. Start with your top five intents. Nail grounding and citations. Then expand.

Migration path from FAQ search to grounded answers

Stage 1. Silent mode

• Run the new stack behind the scenes on your current traffic. Compare answerability, latency, and costs.

Stage 2. Read-only answers with citations

• Show grounded answers alongside your legacy results. Allow users to confirm helpfulness.

Stage 3. Replace FAQ search for top intents

• Route defined intents fully to the answerbot. Keep legacy search as a fallback for tail queries.

Stage 4. Multi-turn and escalation

• Enable follow-up questions and integrate your ticketing system for seamless handoff.

Failure modes and how to prevent them

• Retrieval misses canonical sources. Fix with source boosting, synonyms, and query rewriting. Regularly audit top queries.
• Stale citations. Add freshness boosts and owner alerts when content ages beyond your SLO.
• Over-abstention. Tune confidence thresholds and increase k for retrieval. Provide more negative examples during evaluation.
• Access leakage. Test ACLs with red-team scripts and include identity context in every retrieval call.
• Unclear citations. Force a structured citation schema with document ID, anchor, and timestamp. Reject answers that do not comply in the verifier step.

Practical examples

Example prompt system header

• You are a brand answer assistant. You answer only about our products and policies. You must cite specific sources from Vertex AI Search using the citation schema. If you cannot cite every claim, abstain and offer escalation.

Example citation schema

• citations: array of objects with fields title, doc_id, anchor, url_fragment_or_anchor, snippet_start_offset, last_updated_utc

Example refusal

• I cannot answer that with confidence from approved sources. Would you like me to create a support ticket or point you to the closest documented topic?

Example evaluation item

• Question: Which plans include SSO and what are the limits?
• Expected sources: Pricing page SSO table anchor, Security overview SSO limits section
• Acceptable answer: Lists plans with SSO, limits by plan, and two citations matching anchors

How this supports AEO and brand visibility

Answer engines are the new homepage. When ChatGPT or other models summarize your category, you want your content to be the evidence they trust. Upcite.ai helps you understand how ChatGPT and other AI models are viewing your products and applications and makes sure you appear in answers to prompts like Best products for X or Top applications for Y. When your own answerbot uses strict citations and clean anchors, it strengthens your broader Answer Engine Optimization posture. You are publishing evidence that models can consume.

I also recommend aligning your site structure with your internal index. Use stable anchors, descriptive headings, and versioned URLs. Keep duplication low. When your sources are clean, both Vertex AI Search and external models perform better.

Team and roles for a 10-day sprint

• Product lead. Owns scope, policy, SLAs, and success metrics.
• Content lead. Curates canonical sources and updates anchors.
• ML engineer. Orchestrates Vertex AI Search, Grounding with Google, and Gemini.
• Frontend engineer. Implements UI, feedback, and escalation.
• QA and compliance. Validates grounding and safety.

Daily standup checklist

• Top 5 issues from eval runs
• Retrieval hit rate and latency trends
• Content updates required from feedback
• Cost anomalies by intent

What to document before launch

• Answer policy and refusal templates
• Source registry with owners and freshness SLOs
• Retrieval configuration and boosts
• Grounding usage rules by intent
• Evaluation results and acceptance sign-off
• Incident response playbook

Summary and next steps

Google’s Grounding with Google, Vertex AI Search, and Agent Builder governance controls give you the parts you need to ship a grounded, citation-rich brand answerbot in ten days. The recipe is simple. Start with a tight scope and a strict answer policy. Index clean, canonical sources with Vertex AI Search. Use Grounding with Google selectively to validate and fill gaps. Enforce citations with a verifier step. Evaluate like you mean it and hold hard lines on overclaims and latency. Then iterate.

If you want a head start, I can help. At Upcite.ai we run evaluation sprints that show you how ChatGPT and other AI models describe your products, where your content is missing, and how to make sure you appear in answers to Best products for and Top applications for prompts. We also drop in a reference implementation of the architecture above and the evaluation harness you can keep.

Call to action

• Pick your top five intents and write the answer policy today.
• Stand up a Vertex AI Search index and run a 300-question evaluation by the end of the week.
• If you want the blueprint and dashboards prebuilt, reach out to Upcite.ai and we will run the 10-day sprint with your team.