Google Gemini AI employee
A Superkind AI employee that uses Google Gemini to check deployments and alerts, explain error causes, and prepare structured results. Message it in Teams or Outlook, it works with Google Gemini and reports back with the result. Sensitive actions wait for your approval.
- Hosted in the EU
- GDPR data processing agreement
- Ready to start today
- Works in Teams, Slack and more
What is a Google Gemini AI employee?
A Google Gemini AI employee is an AI that connects to your Google Gemini account and completes work there. It analyses files, explains errors, generates structured content, and processes large tasks as batch jobs. Superkind builds this AI employee with your company knowledge and delivers the finished result to wherever your team works.
Unlike workflow builders such as Zapier or Make, there is nothing to configure. No triggers, no zaps, nothing to maintain. You describe the outcome in plain English, the AI employee picks the right Google Gemini actions, chains them with your other systems, and asks for your approval before sensitive actions run.
Google Gemini is Google’s multimodal AI platform for text, images, audio, video, code, embeddings, and structured output.
You ask in Teams
Describe what you need in plain English.
Superkind picks the actions
Selects the right Google Gemini actions and chains them together.
Superkind works in Google Gemini
Analyses files, generates content, and processes batch jobs with your real data.
Superkind reports back
Delivers the finished result to Teams or Outlook.
What can you ask Superkind to do in Google Gemini?
Messages you would actually send. Copy one, swap in your specifics, and Superkind takes it from there.
“Use Google Gemini to check the latest failed deployment run and summarise the alert clearly.”
you, to @Superkind“Ask Google Gemini to explain the most likely cause of this error from the logs and configuration.”
you, to @Superkind“Create a Jira ticket from the Google Gemini analysis with the cause, evidence, and next steps.”
you, to @Superkind“Let me know here as soon as a new incident arrives, and have Google Gemini narrow down the cause first.”
you, to @SuperkindHow does Superkind work with Google Gemini?
- Native integrations and connectors for 1,000+ tools
1Connect your systems
Google Gemini connects securely to Superkind. Add Teams and the systems holding your incidents, logs, and documents. Superkind handles the sign-in. Your team can then get started directly in Teams.
@Superkind use Google Gemini to analyse today’s deployment alert and create a Jira ticket from the cause.
On it. Google Gemini is checking the alert, logs, and configuration, then I will prepare the Jira ticket.
2Tell Superkind what you need
Message your AI employee in Teams like a colleague. Ask Google Gemini for an error analysis, a summary, or structured data. Superkind gets the necessary context from your connected systems. Plain English is enough.
- Gemini analysis complete
- Cause: API configuration invalid
- Jira issue GEM-317 prepared
Gemini analysis reportWaiting for your approvalRestart the deployment now?3Superkind operates, you approve
Superkind has Google Gemini analyse the data and writes the result back in Teams. If a Jira ticket or file should be created, Superkind prepares everything. Sensitive changes wait for your approval. Every step is logged.
What can Superkind do in Google Gemini?
Ask in plain English from Teams or Outlook. Superkind picks the right Google Gemini actions, runs the work, and reports back. No workflows to build.
List models
Lists available Gemini Models with their supported generation methods.
Get model details
Reads the name, version, input limit, and output limit of a Gemini Model.
Count tokens
Counts the input tokens of Content before calling a Gemini Model.
Generate embeddings
Generates embeddings for text, documents, or classification tasks with a Gemini Model.
Generate text
Generates text from a prompt and a system instruction with generateContent.
Summarise documents
Summarises PDF Files or other documents with their relevant context.
Explain error cause
Analyses logs and configurations and explains the most likely cause of an error.
Classify alerts
Classifies alerts by cause, urgency, and affected service into structured fields.
Analyse images
Examines images with multimodal Content and describes detected details.
Generate structured JSON
Returns a response as JSON according to a specified response schema.
Plan function calls
Selects suitable Function Calls and returns their arguments as structured data.
Upload file
Uploads a file as a File resource for repeated Gemini requests.
List files
Lists File resources with name, MIME type, size, and state.
Get file details
Reads the metadata and URI of a specific File resource.
Delete file
Permanently deletes an uploaded File resource from the Gemini API.
Create context cache
Creates CachedContent with a Model, Contents, system instruction, and expiration.
List context caches
Lists existing CachedContent resources with their Model and expiration.
Update cache expiration
Updates the TTL or expiration of a CachedContent resource.
Delete context cache
Deletes a CachedContent resource and its stored context.
Create batch job
Creates a Batch Job from inline requests or a JSONL input File.
List batch jobs
Lists Batch Jobs with state, Model, creation time, and output File.
Get batch result
Reads inline responses or the output File of a completed Batch Job.
Cancel batch job
Cancels a running Batch Job before it finishes.
Review safety filters
Reads Safety Ratings and Block Reasons from the Candidates of a Gemini response.
Review usage metadata
Reads Prompt, Candidate, and Cached Content tokens from Usage Metadata.
Get search evidence
Reads Grounding Metadata and search evidence from a grounded Gemini response.
Superkind AI employees that work with Google Gemini
Every role brings its expertise and uses Google Gemini as one of its tools.
Companies working with Superkind
Frequently asked questions
Everything you need to know about your AI employee for Google Gemini.
Yes. Superkind connects Google Gemini through a managed connector. Once connected, your team can ask the AI employee from Teams or Outlook to run analyses, create summaries, and return structured output. The AI employee only uses the agreed Gemini Models and receives no permissions beyond those genuinely required for its tasks.
An admin provides the approved Gemini API access and confirms the required permissions. Together, we select the appropriate Gemini Models, data sources, and allowed actions. The connection is then available to the AI employee in Teams and Outlook. Superkind handles the technical setup, secure credential storage, and logging for every request.
It can use Google Gemini to generate text and structured output, analyse Files and images, explain errors from logs, create embeddings, count tokens, and manage Files, CachedContent, and Batch Jobs. The list above shows typical actions. We define which Models, data sources, and follow-up actions your team may use for your specific use case.
No. With Zapier or Make, you build triggers, steps, and branches, then maintain them whenever something changes. With the AI employee, you describe the desired outcome in plain English. Superkind picks the right Google Gemini actions itself, connects them with Teams, Jira, or GitHub when needed, and asks when information or a decision is missing.
Only with your approval. Superkind can run analyses, summaries, and read operations independently. Uploading or deleting Files, creating CachedContent, and starting or cancelling Batch Jobs waits for a responsible person to approve it in Teams. Your team defines exactly which Google Gemini actions count as sensitive and therefore require explicit approval.
Superkind is hosted in the EU and signs a GDPR data processing agreement with you. The AI employee receives minimal permissions for the agreed Google Gemini actions. Your content is not used for training. Credentials are stored securely, and every request, approval, and change is recorded in traceable logs.
Paid Google Gemini tiers start at around 20 euros per user per month, depending on the package. They provide models and features but do not automatically complete your workflows. Zapier starts at around 20 euros and Make at around 10 euros per month, both plus setup time. Superkind is priced per use case, a fraction of a full-time hire.