Already on Google Cloud but short on capacity?
Fixed-scope project or Google Cloud engineers integrated into the team.
See GCP implementation →
When it makes sense to adopt the cloud
The platforms must be migrated or modernized.
Google Cloud offers tools for scaling, delivery pace and security.
Data needs to be centralized in BigQuery
Data source preparation, data warehouse configuration and analytics.
AI agents connected to organizational data
Agentic platform in Google Cloud, with limits, permissions and filters.
Looking for cost control in GCP
Workload optimization and observability lead to cost savings.
Looking for certified skills
We work only with Google Cloud certified engineers / architects.
Looking for security and auditing
IAM, logging, design of operational and security controls.
What we built with Google Cloud
- Financial GenAI: four years of reports, daily analysis in four minutes.
- Gaming analytics: BigQuery for hundreds of thousands of players.
- Quoting: the integration of Gemini with Entersoft has reduced the quoting time by 68%.
- Price intelligence: BigQuery + Gemini for price, stock and margin.
- Commercial automation: HubSpot and Google Cloud integrated into quoting workflows.
Google Cloud connects existing data with applications, analytics and AI capabilities
What we deliver
For organizations that want Google Cloud services delivered by a team of individually certified professionals.
Landing Zone & Lift and Shift
We design and migrate workloads to Google Cloud.
Application modernization
We refactor applications for cloud services and modern deployment.
BigQuery & data platforms
We centralize data and prepare it for analytics and AI.
Agentic Platform & Gemini
Search, generative AI, Search for Commerce, Forecast.
Security, IAM & observability
IAM, logging, monitoring, CMEK and controls for production.
Cost optimization & support
We measure consumption and optimize Google Cloud operations.
Additional services
Data, Analytics & AI
We build BigQuery, pipelines and analytics on top of operational data.
See Data and BI
Automation & AI agents
We connect Gemini and Agent Platform to existing workflows and systems.
See how to automate
Software development
We migrate and refactor applications for Google Cloud.
See how we develop
Industries where we apply it
How we implement cloud
1
Workload and outcome
We evaluate the applications, databases and AI workloads that need to be migrated or built.
2
Architecture and cost
We choose between Cloud Run, Compute Engine, Cloud SQL and BigQuery; we estimate the costs.
3
Landing zone and data
We configure IAM, VPC, security policies and infrastructure as code, where necessary.
4
Build, migration and testing
We implement workloads and integrations, including the Gemini Enterprise Agent Platform (Vertex AI).
5
Go-live and optimization
We monitor performance, costs and security through Cloud Monitoring and Billing.
Two ways to work with us
Take the outcome, or add the capacity
Defined-scope Google Cloud project
We implement or modernize a Google Cloud solution and hand it over for operation.
- audit of workload and cost estimation
- IAM, VPC and landing zone architecture
- Cloud Run, Cloud SQL or BigQuery
- testing, security and monitoring
- documentation and handover
Best when the project outcome is clear.
Embedded Google Cloud capacity
Cloud, data and AI engineers embedded into your roadmap and team.
- cloud development and administration
- BigQuery, SQL and data pipelines
- Cloud Run, GKE and infrastructure
- Gemini, Agent Platform and AI applications
- FinOps and Senior Oversight
Best when roadmap pressure is high.
We integrate technology with existing business systems
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Quick Questions
What should I choose between GKE and Cloud Run?
Use Cloud Run for most stateless services (APIs, webhooks) for speed and optimized costs. Choose GKE (Kubernetes) for complex workloads that require fine-grained control, such as those with GPUs or intensive streaming.
How can I reduce costs in BigQuery?
Through technical methods like partitioning and clustering tables, selecting only the necessary columns, using Materialized Views for frequent queries, and setting budgets and alerts.
How do your solutions integrate with HubSpot?
Integration is done via the official HubSpot API, webhooks, Cloud Functions jobs, or ETL connectors (e.g., Airbyte). We ensure incremental synchronization, ID mapping, and data validation.
How long does a Gemini PoC (Proof of Concept) take?
A typical PoC takes 2 to 4 weeks, including defining the scope, prototyping an agent on Vertex AI, user testing, and creating a production plan.
Relevant Insights & Guides
Insight
30 September 2026
What AI project can you build with a 2-year TCO under 50,000 euros: the Setup + Run formula, the real cost of AI agents over ERP, and scaling from 1,000 to 100,000 runs per month.
Event
25 September 2026 · Updated: 1 October 2026
How to Web 2026 takes place in Bucharest on October 6-8. Three agenda themes: product slop, AI ROI, and software agents in production.
Event
2 September 2026 · Updated: 2 October 2026
The OPTI team attended Google Cloud Day Romania 2026. Notes from sessions on Antigravity, GKE Pod Snapshots, AI agents and recommendation systems.
Book a 30-minute technical assessment
We look at the problem, the systems involved and which KPIs can be reached.
You get a solution quickly.
See OPTI's delivery models and guarantees →
Last updated: 6 November 2026
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