A system that combines documentation, emails, support tickets, CRM, business analytics, and internal repositories to understand the full context of a customer account, project, or issue. It goes beyond answering questions: the system creates baseline tasks, reports, quotes, plans, follow-up analyses, and next steps—and can trigger approved tasks into real systems.
A sales manager asks to prepare for tomorrow's meeting with customer X. The system reviews emails, documents, unresolved issues, product usage data, recent reports, and internal notes. In a matter of seconds, it creates an overview that includes context, risks, opportunities, and next steps. If approved, the system creates tasks in CRM or Jira and saves the summary to a Google Drive folder.
These are not assistants who simply help. These are agents who take a case, break it down into stages, check rules and data, query information from multiple systems, ask for confirmation if necessary, implement the change, and ensure full traceability. This is no longer a chatbot – this is a fully automated business process with control and auditability.
A complaint or request to add a supplier comes in. The agent reviews the case, checks internal policies, sees if information is missing, compares data in the ERP and CRM systems, prepares a decision proposal, requests confirmation from a person, updates the system, sends emails, creates tasks, and closes the case with a full audit trail.
Voice, chat and video assistants for the web, app, WhatsApp or call center that can understand natural language, switch languages, use tools, retrieve information from systems and, if necessary, direct communication to a human. It's not just a chatbot - it's a 24/7 digital receptionist.
A customer calls or texts to ask about an order or issue. The assistant understands the request, responds via voice or text, retrieves information from internal systems, and resolves most cases without human intervention. If the system detects an upset or sensitive situation, it routes the case, along with summarized context, to a customer service representative.
Invoices, contracts, claims, forms, insurance policies or files. The solution extracts data, categorizes and validates documents, identifies anomalies, makes a decision proposal, routes the document to the right reviewer, updates systems and archives everything in a traceable manner. The customer doesn’t see the OCR – they see a huge amount of manual work disappearing.
An invoice arrives in the inbox. The system extracts key fields, compares them with business rules and master data, identifies possible deviations, decides whether the process can continue or requires review, transfers the information to the ERP system, archives the document, and leaves an audit trail.
An agent that answers questions about real business data in natural language, explains anomalies, creates explanatory texts and reports, and prepares next steps. The change is very visible: instead of ordering reports from the data team, you interact directly with your data.
The CFO asks why margins in a sales territory fell this week. The agent queries the BigQuery data warehouse, collates sales, discounts, inventory, issues, and costs, and returns a response that includes explanatory text, tables, and key drivers. It then generates a weekly report and prepares appropriate charts for presentation to management.
A layer of agents that accelerates the work of the data team: suggesting data flows, generating SQL or Dataform code, suggesting tests, documenting datasets, helping to identify errors, and answering questions about the data. It's like an AI-powered data team.
A product manager requests a weekly dataset of returns across channels and countries. One agent proposes a data processing flow, writes code, suggests validations, documents the dataset, and drafts a pull request (PR) for review. Another agent answers questions about this dataset in Slack.
It's not just reporting: these are systems that forecast demand, inventory, labor needs, pricing, or risks and turn the forecast into recommendations or direct action. Forecasting, bid management, and optimization that have a measurable impact on business results.
A retail chain wants to know how many employees are needed in each store on Saturday, which products should be replenished, and where sales opportunities are being missed. The solution forecasts demand, identifies anomalies, recommends actions, and can initiate proposals for ordering goods, repositioning inventory, or staffing.
Solutions that analyze images or video to identify low inventory, defects, non-compliance, or maintenance needs, and automatically trigger the appropriate action. Much more tangible than talking about computer vision in the abstract.
An employee walks along a row of shelves with a mobile phone and takes a photo. The system identifies products, notices empty spaces, price errors or problems with product placement and immediately recommends the necessary action. In production or warehouse, the system identifies defects or incorrect samples and automatically creates a work task.
Agents that investigate alerts, collect evidence, correlate signals, verify settings, and provide a reasoned conclusion for human review – ensuring full control and traceability. A very powerful solution for regulated sectors as it combines AI with auditing and compliance.
Instead of analysts dealing with alerts one by one, the agent collects logs, permissions, recent changes, and evidence, investigates the incident, and creates a structured explanation that includes information about the risk, context, and recommended actions. The agent can also check for deviations from internal policies or regulatory requirements.
One of the most prominent cases. Systems where today a person enters, navigates, copies data, pastes, validates, and manually presses multiple buttons. An agent can visually use this interface, trigger a sequence of actions, and maintain a complete overview of what has been done – with validations and traceability.
The company uses an outdated application that lacks API to record transactions. Today, an employee spends hours a day entering data, checking fields, copying information from other systems, and filling out forms. An agent performs the same process assisted or semi-autonomously, with validation at each stage.
The minimum requirement is an active Google Workspace or Google Cloud environment. Most of these cases use Gemini Enterprise as the user layer and Vertex AI as the engine. We will conduct a two-hour introductory workshop to identify 3-5 use cases that will provide the greatest impact and return on investment (ROI) for your business.
A working proof of concept (PoC) can be completed in 2–4 weeks. Full deployment in production depends on the complexity: a customer service assistant can be up and running in 6–8 weeks, while a complete document processing system can be up and running in 2–3 months.
Yes. Everything runs inside your Google Cloud project. Your data never leaves your environment, it's not used to train Google models, and you control access rights. Gemini Enterprise offers VPC-SC, CMEK, and Access Transparency solutions for regulated sectors.
Not for most of these cases. Gemini Enterprise allows you to create agents without coding. For more complex cases, like predictive models or computer vision, we develop and deploy the models on Vertex AI platform. Your team uses them, we build and maintain them.
Gemini Enterprise is the user layer: agents available to all employees, a chat interface, and integration with Google Workspace environment. Vertex AI is the AI engine: RAG, memory, an advanced agent runtime, custom machine learning, fine-tuning, and traceability. They complement each other – Gemini for everyday work, Vertex AI for cases that require deep customization.
We'll conduct a free two-hour workshop to identify the use cases with the highest impact and return on investment (ROI) for your business. No obligation.
