A system that connects documentation, email, tickets, CRM, BI, and internal repositories to understand the full context of an account, project, or issue. It does more than just answer questions: it prepares briefs, reports, proposals, plans, post-mortems, and next steps—and can launch approved tasks in actual systems.
A sales director asks, "Prepare me for tomorrow's meeting with Client X." The system reviews emails, documents, open issues, product usage, recent reports, and internal notes. Within seconds, it returns a briefing covering context, risks, opportunities, and next steps. If approved, it creates tasks in the CRM or Jira and saves the summary to Drive.
These are not assistants that merely "help." They are agents that receive a case, break it down into steps, validate rules and data, query multiple systems, request approval when necessary, execute the change, and maintain a complete audit trail. This is no longer a chatbot—it is a genuine automated business process with built-in control and auditing.
A claim or supplier onboarding request is received. The agent reviews the case, consults internal policies, checks for missing information, cross-references data between the ERP and CRM, drafts a recommended decision, requests human approval, updates the system, sends emails, opens tasks, and closes the case with a full audit trail.
Voice, chat, and video assistants for web, apps, WhatsApp, or call centers—capable of understanding natural language, switching languages, using tools, querying systems, and escalating to a human agent when necessary. It is not just "a chatbot"—it is a digital reception desk that operates 24/7.
A customer calls or writes to inquire about an order or an issue. The assistant understands the request, responds via voice or text, consults internal systems, and resolves most cases without human intervention. If it detects anger or a sensitive situation, it transfers the call to a human agent, providing a summary of the full context.
Invoices, contracts, claims, forms, policies, or files. The solution extracts, classifies, validates, detects anomalies, proposes a decision, routes items to the right reviewer, updates systems, and archives everything with full traceability. The client doesn't see "OCR"—they see a mountain of manual work disappear.
An invoice arrives in a mailbox. The system extracts key fields, compares them against business rules and master data, detects any anomalies, determines whether to proceed or flag the item for review, loads the information into the ERP, archives the document, and creates an audit trail.
An agent that answers questions about real business data in natural language, explains anomalies, drafts narratives, prepares reports, and outlines next steps. The shift is highly visual: moving from requesting reports from the data team to speaking directly with your data.
The CFO asks, "Why has the margin in Andalusia dropped this week?" The agent queries BigQuery , cross-references sales, discounts, inventory, incidents, and costs, and returns a response featuring a narrative, tables, and key drivers. It then generates the weekly report and prepares charts ready for the committee.
An agent layer that accelerates the data team's work: it proposes pipelines, generates SQL or Dataform code, suggests tests, documents datasets, helps diagnose failures, and answers questions about the data. It is like having an AI-assisted data team.
A product manager requests a weekly dataset showing returns by channel and country. One agent proposes the pipeline, generates the code, suggests validations, documents the dataset, and prepares a draft PR for review. Another agent answers questions about that data on Slack .
It goes beyond mere reporting: systems that predict demand, inventory, staffing, pricing, or risk, and transform those predictions into recommendations or direct actions. Forecasting, bidding, and optimization with a measurable impact on the bottom line.
A retail chain wants to know how many staff members are needed per store on Saturday, which products to restock, and where sales are being left on the table. The solution predicts demand, detects anomalies, recommends actions, and can trigger proposals for purchasing, stock relocation, or staff scheduling.
Solutions that interpret images or video to detect low stock, defects, non-compliance, or maintenance needs, and automatically trigger the appropriate action. This is far more tangible than discussing "computer vision" in the abstract.
A staff member walks along a shelf with a mobile phone and takes a photo. The system recognizes products, detects gaps, pricing errors, or planogram issues, and instantly provides a recommended action. On the shop floor or in the warehouse, it detects defects or reading errors and automatically opens a task.
Agents that investigate alerts, gather evidence, correlate signals, review configurations, and provide an explained conclusion for human review—with full control and traceability. Highly powerful for regulated sectors, as it combines AI with auditing and compliance.
Instead of analysts chasing alerts one by one, an agent gathers logs, permissions, recent changes, and evidence, investigates the case, and provides a structured explanation covering risk, context, and recommended action. It can also check for deviations from internal policies or regulatory requirements.
One of the most striking examples involves systems where, today, a person logs in, navigates, copies and pastes data, validates information, and manually clicks various buttons. An agent can operate that interface visually, execute the sequence, and maintain a complete record of the actions taken—including validations and traceability.
A company uses a legacy application without APIs to record transactions. Currently, a person spends hours each day entering data, reviewing fields, copying information from other systems, and filling out forms. An agent performs this same process in an assisted or semi-autonomous manner, with validations at every step.
At a minimum, you need an active Google Workspace or Google Cloud . Most of these use cases employ Gemini Enterprise as the user layer and Vertex AI as the engine. We conduct an initial 2-hour workshop to identify the 3–5 use cases with the greatest impact and ROI for your company.
A functional PoC (proof of concept) can be ready in 2–4 weeks. Full production implementation depends on complexity: a customer service assistant can be operational in 6–8 weeks, while a complete document processing system takes 2–3 months.
Yes. Everything runs within your Google Cloud project. Your data does not leave your environment, is not used to train Google models, and you control access permissions. Gemini Enterprise offers VPC-SC, CMEK, and Access Transparency for regulated sectors.
Not for most of these cases. Gemini Enterprise allows you to create agents without coding. For more advanced use cases (predictive models, computer vision), we develop and deploy the models on Vertex AI . Your team uses them; we build and maintain them.
Gemini Enterprise is the user layer: agents accessible to all employees, a conversational interface, and Workspace integration. Vertex AI is the AI engine: RAG, memory, an advanced agent runtime, custom ML, fine-tuning, and observability. They complement each other— Gemini for day-to-day tasks, and Vertex for use cases requiring deep customization.
We conduct a free 2-hour workshop to identify the use cases with the greatest impact and ROI for your business. No obligation.
