Products building multimodal AI experiences
Analyse and generate across text, images, audio, video, files, live interactions, and structured application state.
Gemini API, multimodal applications, function calling, structured output, grounding, live experiences, Vertex AI, and evaluation
Google Gemini integration services
Rokad builds Gemini-powered applications across multimodal understanding, function calling, structured outputs, grounding, agents, Vertex AI, and production evaluation.
Platform fit / 01
Designed for teams with a specific platform requirement.
Gemini supports text, image, audio, video, files, structured output, function calling, and grounded application patterns. Rokad designs the user experience, context, data, tools, model access, Google Cloud integration, evaluation, safety, latency, cost, and production operations.
Google Cloud organisations adopting enterprise AI
Use Vertex AI identity, networking, data, monitoring, evaluation, governance, and application infrastructure.
Teams requiring grounded and tool-enabled applications
Combine search or private data with function calls, structured outputs, application actions, and verification.
Implementation risks / 02
The platform problems Rokad is prepared to solve.
Multimodal input lacks a consistent task model
Files, frames, images, audio, text, metadata, timing, quality, and output requirements are not normalised.
Grounding is treated as guaranteed truth
Sources, retrieval quality, citations, freshness, conflicting evidence, unsupported claims, and user verification are not controlled.
Google AI and cloud responsibilities are fragmented
API access, projects, identities, regions, data, quotas, logging, networking, billing, and operations lack one architecture.
Platform capabilities / 03
What Rokad can implement and operate.
01
Gemini API and Vertex AI architecture, model evaluation, task design, and integration
02
Text, image, audio, video, file, document, and multimodal application workflows
03
Function calling, structured outputs, application actions, schemas, tools, and approvals
04
Grounding with search or private data, retrieval, citations, verification, and knowledge systems
05
Agent and live-interaction experiences, streaming, session state, and user interfaces
06
Google Cloud identity, projects, networking, data, storage, logging, monitoring, and deployment
07
Evaluation, tracing, safety, latency, cost, quotas, versions, fallbacks, and managed operation
Implementation system / 04
The architecture behind a dependable platform delivery.
Multimodal input pipeline
Capture, upload, transform, segment, metadata, quality, privacy, storage, context, and task-specific representation.
Gemini application layer
Models, prompts, structured output, function calls, streaming, sessions, grounding, and application contracts.
Google Cloud foundation
Projects, identity, regions, networking, storage, data, APIs, quotas, monitoring, billing, and deployment.
Evaluation and governance
Datasets, multimodal quality, groundedness, tool use, safety, latency, cost, audit, and release controls.
Use cases / 05
Where this platform creates practical leverage.
Multimodal document and media analysis
Understand text, images, tables, audio, video, and metadata, then produce structured findings and review workflows.
Grounded customer or employee assistant
Combine Google or private search with source-aware responses, application context, tools, and escalation.
Visual inspection and field workflows
Analyse images or video, collect context, classify issues, produce structured records, and route human validation.
Gemini agent on Google Cloud
Use authorised functions, enterprise data, cloud services, identity, monitoring, evaluation, and application controls.
Architecture / 06
Platform-specific engineering decisions and boundaries.
Multimodal evidence is normalised
Define resolution, duration, sampling, file formats, metadata, privacy, retention, and output schemas for each input type.
Grounded claims remain inspectable
Preserve source references, retrieval context, freshness, confidence, unsupported cases, and user access to evidence.
Google Cloud identity surrounds actions
Use scoped service identities, project boundaries, secrets, policy, audit, network controls, and approvals for tools and data.
Quality and governance / 07
Production controls are part of the implementation.
Evaluated behaviour
Representative datasets, task criteria, failure modes, model comparisons, and release thresholds are defined before production expansion.
Governed model access
Identity, data boundaries, tool permissions, moderation, approvals, audit, retention, and provider controls match the use case.
Provider-aware operation
Models, prompts, tools, latency, cost, quotas, versions, fallbacks, telemetry, and migration risk are monitored explicitly.
Delivery / 08
A controlled path from assessment to operation.
Assess
Clarify the business outcome, current systems, platform constraints, data, integrations, risks, ownership, and measurable acceptance criteria.
Design
Define the platform architecture, workflow or storefront model, extensions, integrations, security, environments, and migration sequence.
Implement and validate
Build in controlled increments with testing, stakeholder review, observability, documentation, and platform-specific quality controls.
Launch and operate
Deploy safely, transfer ownership, monitor production behaviour, support users, and improve the implementation using operational evidence.
Typical platform deliverables
Gemini use-case, modality, grounding, data, cloud, security, and risk assessment
Multimodal, application, tool, grounding, Vertex AI, evaluation, and operating architecture
Production Gemini API or Vertex AI applications, agents, and multimodal workflows
Functions, structured outputs, search, retrieval, data, Google Cloud, and application integrations
Evaluation datasets, groundedness tests, tracing, monitoring, safety, cost, and release controls
Developer, product, cloud, operator, governance, and handover documentation
Engagement models / 09
Use the delivery structure that matches the platform work.
Assessment and roadmap
A bounded review of the current platform, requirements, gaps, risks, architecture, and an executable next-stage plan.
Fixed-scope implementation
A defined integration, migration, storefront, application, workflow, or platform outcome with explicit acceptance criteria.
Embedded platform specialists
Specialists working alongside internal product, engineering, operations, marketing, data, or enterprise teams.
Managed platform evolution
Ongoing maintenance, releases, integrations, support, optimisation, governance, and roadmap execution after launch.
Related platforms and services / 10
Compare adjacent platforms or continue into the wider system.
01
Microsoft Foundry
Azure enterprise AI platform for models, agents, tools, evaluations, tracing, and operations.
02
OpenAI
Responses API, agents, tools, file search, multimodal workflows, and evaluations.
03
Anthropic Claude
Long-context analysis, tool use, MCP integration, and governed agents.
01
AI development
AI applications, agents, retrieval, evaluation, model integration, and intelligent workflows.
02
Cloud and DevOps
Cloud architecture, delivery automation, observability, security, reliability, and platform operation.
03
Data engineering
Pipelines, platforms, warehouses, analytics engineering, BI, and governed data operations.
FAQ
Google Gemini integration services
Platform scope, ownership, licences, data, integrations, security, migration, and long-term operation are clarified before delivery.
01Can Gemini process images, audio, video, and documents?
Can Gemini process images, audio, video, and documents?
Yes, depending on the selected model and API. We design file preparation, segmentation, metadata, context, structured outputs, quality checks, and user review around the task.
02Should we use the Gemini API or Vertex AI?
Should we use the Gemini API or Vertex AI?
The choice depends on identity, cloud governance, networking, regions, enterprise controls, data, operations, integrations, and product requirements.
03Can Gemini call functions and APIs?
Can Gemini call functions and APIs?
Yes. We define tool schemas, validation, permissions, identity, approvals, idempotency, audit, timeouts, and recovery around application actions.
04Can Rokad evaluate Gemini against other models?
Can Rokad evaluate Gemini against other models?
Yes. We compare representative tasks across multimodal quality, grounding, tool use, latency, cost, safety, context, and operational fit.
Google Gemini · AI integration services
Use Gemini as part of a measurable multimodal application architecture.
Rokad can design the experience, integrate functions and grounding, establish Google Cloud controls, and build production evaluations.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.