AI and automation
Google Gemini
Technology in service of the outcome.
Use Gemini in AI applications and business workflows when its evaluated capabilities fit the task, with data access, model choice and operating controls made explicit.
- Workspace docs
- Retrieval
- Gemini
- Citations
- Grounded answer
- Review
Document tiles feeding retrieval, then Gemini, producing a cited answer with a review step.
Business information arrives as documents, images, tables and conversations, while the decision a team needs to make usually sits across several systems. Emerge uses Gemini where its evaluated capabilities fit that combination. We design a workflow that turns permitted inputs into structured, reviewable information and connects the result to the right business process.
The starting point is a task and a reliable source boundary. A multimodal model does not remove the need to establish what an image represents, which record is authoritative or who may act on the interpretation. Our delivery joins data engineering, application controls and task-based evaluation so the model is a useful component of an accountable service.
Choose the right starting point
Product expertise, in detail.
Multimodal workflows
Prepare information from supported document and image inputs with source references, explicit output schemas and reviewer checkpoints.
Data-connected assistants
Bring approved business data into an assistant through controlled retrieval and query interfaces with observable costs and outcomes.
Google Cloud AI integration
Connect the surrounding data foundation, application identity and operational controls when Google Cloud is the selected delivery environment.
The starting point
What needs to change.
Mixed-format inputs often lack consistent metadata. A photograph, PDF and spreadsheet can refer to the same asset with different identifiers, leaving downstream teams to resolve ambiguity manually.
A data-connected assistant can answer a grammatically simple question incorrectly if business measures, time periods or access scopes are unclear. It needs approved definitions and constrained queries rather than unrestricted database access.
Quality varies by task, input condition and model configuration. Evaluation must expose incorrect extraction, unsupported conclusions and inappropriate tool requests before the application is released to a wider audience.
What we deliver
From opportunity to working systems.
From mixed inputs to a review queue
We classify incoming material, preserve its origin and define the fields that matter to the receiving team. Gemini can prepare a structured interpretation; validation checks required fields and reference values before a person accepts ambiguous or consequential results.
Business questions with defined meaning
We agree a vocabulary for entities and metrics, then provide controlled retrieval or query tools. An assistant can clarify the period, business unit or measure before it retrieves data. The answer carries relevant context and avoids presenting a partial dataset as the whole business.
Assistance connected to action
A verified interpretation can prepare a service request, update draft or follow-up task. Writes remain behind an application control that checks the requesting user’s authority, validates fields and records the destination result.
Illustrative solution architecture
How the pieces work together.
Bring context into the workflow, connect the right solutions, and make progress visible.
01 Understand the context
Signals & knowledge
Use the context and information already in place.
- Business priorities
- Trusted knowledge
- Operational data
02 Connect the solutions
From mixed inputs to a review queue
Business questions with defined meaning
Assistance connected to action
03 Put it to work
Teams & operations
Connect people and systems to the next useful action.
Measured outcomes
Track agreed measures, learn, and improve the workflow.
Built around your existing technology
Implementation design
How the pieces work together.
Separate the input and decision layers
Original files, parsed information, model interpretation and accepted records are stored separately. This supports replay, correction and retention decisions without losing the original evidence. The reviewer can see what changed between a raw input and a committed business value.
Function calling with bounded tools
Gemini function calling can request an application-defined operation. Emerge supplies a narrow schema and implements authorisation, argument validation and result handling outside the model. High-risk tools require a deliberate review step; a model request alone never establishes permission.
Decisions to make early
Choose the channel deliberately
Gemini through the Gemini API and Gemini through Google Cloud have different account, governance and commercial contexts. We verify the proposed feature set, regional requirements and administrative controls for the actual environment rather than assuming parity across products.
Use model output for the right job
An image interpretation or generated explanation is not a verified measurement, clinical decision or financial record. The workflow states what the output supports and what additional validation is needed before it changes a business outcome.
Our approach
A clear path into delivery.
Start with the business problem. Make each stage useful, reviewable and owned.
- 01
We begin with representative inputs and the people who process them. Discovery identifies unsupported formats, unclear ownership, sensitive fields and the decisions that require human judgement. It produces an input contract and measurable acceptance criteria.
- 02
A pilot implements the complete path for one use case, including extraction failures, absent information and user corrections. Alternative models or simpler deterministic processing are compared where they may offer a better balance of cost and reliability.
- 03
Production preparation covers the selected deployment channel, service identities, quotas, usage allocation, monitoring and rollback. The handover includes the task evaluation set and a review process for model changes and new input categories.
Go deeper
Build a more informed brief.
Related Agentforce integration guides for Google Gemini.
These integration guides examine specific systems, access boundaries and illustrative use cases.
Practical questions
Before we begin.
Do we need to move all our data to Google Cloud?
No. The design can retrieve a permitted subset through existing APIs and preserve the current systems of record. We assess data movement, latency, access and operating cost before recommending any consolidation.
How do you test multimodal quality?
We use examples from the intended task, covering clear and poor inputs, missing metadata and ambiguous cases. Reviewers score specific fields and conclusions against expected evidence. The release gate includes appropriate uncertainty and escalation as well as successful answers.
Explore the detail
Related work and resources.
See the approach in context. Client engagement stories are anonymised; related examples may come from other sectors or platforms.
Connected expertise
Your next move
Bring us the business problem.
Pick a time below. We will help you define a useful starting point, the expertise you need and a practical path to delivery.
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