Model choice should follow your architecture, not the other way around. For organizations already on Google Cloud or living in Google Workspace, Gemini on Vertex AI is often the path of least resistance and most value: the data is already there, the identity and governance are already there, and the integration surface is shorter. We help you decide whether that is true for you, and then build it well.
We deploy Gemini through Vertex AI, build Gemini-powered applications, and integrate them with the Google estate that makes them useful, BigQuery for the data, Workspace for where people work. Around that we do the production engineering, grounding, evaluation, and governance, that makes Gemini reliable inside a larger AI architecture instead of a clever one-off.
The usual failures: treating the model as the project and skipping the data and grounding work underneath it, integrations to BigQuery or Workspace that are shallow enough to demo but not to depend on, and no evaluation, so no one can say whether it is actually working. We build the data foundation and the guardrails first, so Gemini runs on real context and you can trust what it returns.
We are platform-honest. We recommend Gemini when your Google footprint makes it the right fit, and we have the AI engineering depth to deploy it as a production system, not a proof of concept. The goal is the same regardless of model: AI that does real work inside the tools your team already uses.
The engagements we run most often on Google Gemini, from first implementation through optimization.
Gemini deployments on Vertex AI with VPC Service Controls, data residency, and audit logging for enterprise workloads.
Custom applications built on Gemini, leveraging long context, multimodal input, and Google's safety features.
Tight integration with BigQuery for retrieval, analytics, and grounding Gemini in your enterprise data.
Gemini features inside Google Workspace, for organizations standardizing knowledge work on Google.
Architectures that combine Gemini with Claude or GPT, using the right model for the right workflow.
Engagements are measured by movement on the numbers that matter. These are the directions of travel we commit to.
Predictable phases. Clear deliverables. No surprises.
One to two working sessions to map your current state, business goals, and gaps. We come out with a written scope and recommendation.
Documented architecture, realistic timeline, and transparent commercial proposal. No surprises and no hidden scope.
Configuration, development, integrations, data migration, and QA, with weekly demos and on-the-fly adjustments.
Training, change management, hypercare, and ongoing optimization. We do not disappear at go-live.
Consultant-level analysis from the consultants delivering the work.
How Salesforce data and actions reach ChatGPT, Gemini Enterprise and Claude today, what is in beta, how permissions carry over, and how to decide which assistant each team should use with your CRM.
Read the full post →Salesforce announced that Gemini models can power Agentforce’s Atlas Reasoning Engine, naming Gemini 3.5 Flash in an April 2026 release. Agents keep their Salesforce topics, actions and Einstein Trust Layer controls while Gemini handles the reasoning. Model choice becomes a setting your Salesforce admins manage. Confirm which Gemini versions your org can select today, because the list changes.
Gemini Enterprise, which absorbed Google Agentspace, includes a Salesforce connector for searching CRM data beside Drive, Gmail and other sources. Google documents federated search in public preview and data ingestion in private preview, and says Salesforce permissions are honored. Federated queries, possibly including conversation context, are sent to Salesforce. Check the current preview status before planning production use.
Salesforce and Google announced Agentforce Sales inside Gemini Enterprise as an open beta in April 2026. Sellers can ask about accounts and pipeline from Google’s assistant while Salesforce agents carry out the CRM work. Beta features change without much notice, so treat this as a pilot candidate and confirm terms, supported actions and data flows before any wider rollout.
Google renamed Vertex AI as Gemini Enterprise Agent Platform in April 2026. It hosts Gemini and other models, an agent development kit and a managed runtime. For Salesforce teams, it is where custom applications call Gemini with function declarations. Those functions query or update CRM records through your own code and a scoped integration identity.
Salesforce’s bring-your-own-LLM feature lists Google’s Vertex AI among supported providers, letting Prompt Builder and Agentforce use a Gemini deployment in your own Google Cloud project. Inference bills to your Google account while requests still pass through Salesforce’s Models API. Verify how Salesforce’s setup screens currently name the Google provider following Google’s platform rename.
Many Google-centric companies analyze Salesforce data in BigQuery. Data Cloud, also called Data 360, supports zero copy sharing with BigQuery, and Salesforce has announced Agentforce reading from Google’s lakehouse without migration. Grounding Gemini on warehouse data works well for analytical questions, but operational answers about today’s cases or deals should still come from live Salesforce records.
Separate the three paths: Gemini inside Agentforce, Gemini Enterprise searching Salesforce for employees and custom agents on Gemini Enterprise Agent Platform. Each has different owners, identities and contracts. Assign one owner per path, list the objects involved and record which features are preview, beta or generally available before promising anything to users.
Have security review Google’s Workspace generative AI privacy commitments and the Google Cloud data governance terms, including training restrictions, caching and zero data retention options. Confirm how Salesforce data moving through the connector is handled, since federated queries leave Google for Salesforce. Document the approved settings so every later team starts from the same baseline.
Gemini Enterprise must match each Google user to the correct Salesforce user for permissions to hold. Test that mapping with people from different roles, territories and profiles, and confirm restricted records stay hidden. For custom agents, create scoped integration users rather than reusing an administrator, and document every OAuth client you create along the way.
Start with questions only, drawn from real seller and service work, and grade answers for correctness, freshness and appropriate refusal. Compare results from the connector against the same questions answered in Salesforce directly. Add write actions afterward, one at a time, with explicit user confirmation and Salesforce validation rules protecting data quality.
Google and Salesforce are both shipping changes to these integrations quickly, including renames and preview promotions. Assign someone to read release notes monthly, rerun your graded questions after connector or model updates and retire workarounds once supported features arrive. Revisit data handling decisions whenever a feature moves from preview to general availability.
Gemini features in Google Workspace come with Workspace business and enterprise plans, while Gemini Enterprise is licensed separately in several editions. Google has changed this packaging often, so confirm current terms with Google directly. Gemini Enterprise Agent Platform bills model usage on consumption through your Google Cloud account. Gemini models selected inside Agentforce are contracted through Salesforce, whereas a bring-your-own-LLM setup charges inference to your Google Cloud project. Google states it does not use Cloud customer data to train models without permission, and that Workspace content is not used for training outside your domain without permission. Review current terms and any preview conditions before connecting Salesforce data.
Federated search and ingestion for Salesforce in Gemini Enterprise have carried preview labels, which can mean limited support, changing behavior and restricted regions. Building a sales process around a preview feature risks disruption when it changes. Pilot with a bounded group, keep a fallback workflow and plan the production rollout around generally available capabilities.
If a Google account maps to the wrong Salesforce user, or to a broadly privileged one, Gemini can surface records the person should never see. Email differences, shared inboxes and contractors cause most mismatches. Audit the mapping before launch, test with restricted records and recheck after any change to sharing rules or user provisioning.
BigQuery copies of Salesforce data are excellent for trends but may lag hours behind the live org. Asking Gemini about today’s case status from that copy produces stale, confident answers. Route operational questions to live Salesforce data through the connector or APIs, and reserve warehouse grounding for analysis where a slight delay is acceptable.
Teams sometimes create a Google agent and a Salesforce agent that both handle the same sales requests, with different logic and permissions. Users get conflicting answers and admins maintain everything twice. Decide which platform owns each task, let the other call it where an integration exists and publish that ownership so new projects follow it.
Several routes exist. Gemini Enterprise can search Salesforce through Google’s connector, and Salesforce has announced Agentforce Sales inside Gemini Enterprise as a beta. Salesforce also offers its own Gmail integration for logging email and viewing records. Which fits depends on your licenses and appetite for preview features. Start by listing the exact questions sellers ask, then test each route against them.
Choose by testing, not branding. Run your own graded agent conversations against each model Salesforce offers, comparing accuracy, latency and how well each follows topic instructions and action rules. Gemini can be a strong option, particularly where teams already use it elsewhere, but the right choice varies by task. Model settings can be revisited later, so repeat the comparison as new versions arrive.
Google’s Cloud terms state it will not use customer data to train or fine-tune models without permission. Its Workspace commitments say content is not used for training outside your domain without permission. Gemini responses may be cached briefly unless you disable caching as part of a zero data retention setup. Confirm current terms with Google and your legal team before connecting CRM data.
Google announced in April 2026 that Vertex AI is now Gemini Enterprise Agent Platform, bringing model hosting, agent tooling and runtime under one name. Google publishes name-change documentation mapping older product names to new ones. For Salesforce projects, check integration guides, bring-your-own-LLM setup screens and contracts, since some may still use the Vertex AI name for a while. Confirm the exact provider and product names with each vendor before signing anything.
Not for every scenario. Gemini Enterprise connectors, API integrations and Agentforce model selection all work with core Salesforce data. Data Cloud becomes valuable when agents must combine CRM records with data from BigQuery, product usage or other systems, using zero copy access rather than duplicate pipelines. Start with a specific use case, then decide whether unified data is actually required.
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Enterprise API platform, for connecting Salesforce, ERP, data warehouses, and the rest of your stack.
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Cloud data platform, for unified analytics, AI grounding, and data sharing across the enterprise.
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