A system that connects documentation, email, tickets, CRM, BI, and internal repositories to provide a complete picture of an account, project, or issue. It doesn’t just answer questions: it prepares briefs, reports, proposals, plans, postmortems, and next steps—and can trigger approved tasks in live systems.
A sales director asks, "Prepare tomorrow's meeting with Client X." The system reviews emails, documents, open tickets, product usage, recent reports, and internal notes. Within seconds, it generates a briefing that includes context, risks, opportunities, and next steps. If approved, it creates tasks in CRM or Jira and saves the summary to Drive.
They aren’t assistants who simply “help.” They are agents who take on a case, break it down into steps, validate rules and data, query various systems, request approval when necessary, implement the change, and leave a complete audit trail. This isn’t just a chatbot—it’s a real, automated business workflow with built-in controls and auditing.
A claim or supplier onboarding request is submitted. The agent reviews the case, consults internal policies, checks for missing information, cross-references data in the ERP and CRM systems, drafts a recommended decision, requests human approval, updates the system, sends emails, creates tasks, and closes the case with a complete audit.
Voice, chat, and video assistants for websites, 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’s not just “a chatbot”—it’s a digital reception desk that operates 24/7.
A customer calls or sends a message to inquire about an order or an issue. The assistant understands the request, responds via voice or text, checks internal systems, and resolves most cases without human intervention. If it detects frustration or a sensitive issue, it transfers the call to a human agent with all the relevant context already summarized.
Invoices, contracts, claims, forms, policies, or files. The solution extracts, classifies, validates, detects anomalies, suggests a decision, routes the document to the appropriate reviewer, updates systems, and archives everything with full traceability. The customer doesn’t see “OCR”—they see a mountain of manual work disappear.
An invoice arrives in an inbox. The system extracts the key fields, compares them against business rules and master data, detects any anomalies, determines whether to proceed or if a review is needed, uploads the information to the ERP, archives the document, and completes the audit.
An agent that answers questions about real-world business data in natural language, explains anomalies, crafts narratives, prepares reports, and outlines next steps. The shift is striking: moving from requesting reports from the data team to speaking directly with your data.
The CFO asks, "Why has the margin dropped in Andalusia this week?" The analyst checks BigQuery, cross-references sales, discounts, inventory, issues, and costs, and provides a response that includes a narrative, tables, and key drivers. He then generates the weekly report and prepares charts ready for the committee meeting.
A layer of agents that speeds up the work of the data team: it proposes pipelines, generates SQL or DataForm queries, suggests tests, documents datasets, helps diagnose issues, and answers questions about the data. It’s like having an AI-assisted data team.
A product manager requests, "I need a weekly dataset showing returns by channel and country." A team member proposes the pipeline, writes the code, suggests validations, documents the dataset, and prepares a draft pull request for review. Another team member answers questions about that data on Slack.
It’s not just reporting: these are systems that predict demand, inventory, staffing, pricing, and risk, and turn 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 it needs per store on Saturdays, which products to restock, and where it is missing out on sales. The solution forecasts demand, detects anomalies, recommends actions, and can trigger suggestions for purchasing, stock relocation, or staff scheduling.
Solutions that analyze images or video to detect low inventory, defects, non-compliance, or maintenance needs, and automatically trigger the appropriate action. This is much more concrete than talking about "computer vision" in abstract terms.
A store manager scans a shelf with their phone and takes a photo. The system identifies products, detects gaps, pricing errors, or planogram issues, and immediately provides a recommended course of action. On the sales floor or in the warehouse: it detects defects or incorrect readings and automatically creates a task.
Agents investigate alerts, collect evidence, correlate signals, review configurations, and provide a detailed conclusion for human review—with full control and traceability. This is particularly powerful for regulated industries because it combines AI with auditing and compliance.
Instead of analysts chasing alerts one by one, an agent collects logs, permissions, recent changes, and evidence, investigates the case, and provides a structured explanation that includes risk assessment, context, and recommended action. The agent can also review deviations from internal policies or regulatory requirements.
One of the most striking examples. Systems where, today, a person logs in, navigates, copies data, pastes it, validates it, and manually clicks several buttons. An agent can operate that interface visually, execute the sequence, and leave a complete record of the actions taken—complete with validations and traceability.
A company uses an outdated application without APIs to record transactions. Currently, one person spends hours each day entering data, checking fields, copying information from other systems, and filling out forms. An agent performs the same process with assistance or semi-autonomously, with validation checks at every step.
At a minimum, you need to have Google Workspace or Google Cloud set up. Most of these use cases employ Gemini Enterprise as the user interface 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 proof of concept (PoC) can be ready in 2–4 weeks. Full production deployment depends on the complexity: a customer service assistant can be up and running in 6–8 weeks. A full-scale document processing system takes 2–3 months.
Yes. Everything runs within your Google Cloud project. Your data never leaves your environment, it isn't used to train Google's models, and you control access permissions. Gemini Enterprise offers VPC-SC, CMEK, and Access Transparency for regulated industries.
Not in most cases. Gemini Enterprise lets you create agents without writing code. 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 integration with Workspace. Vertex AI is the AI engine: RAG, memory, advanced agent runtime, custom ML, fine-tuning, and observability. They complement each other—Gemini for day-to-day tasks, Vertex for cases requiring deep customization.
We offer a free 2-hour workshop to identify the use cases with the greatest impact and ROI for your business. No obligation.
