Vertex AI & Gemini Development Services
Enterprise-grade generative AI, developed where your data already lives. We design, launch, and operate Gemini-powered applications, agents, and RAG systems on Vertex AI — within your Google Cloud tenant and within your own security boundary.
When Vertex AI is the right answer
It is a promising demo which was created on someone's laptop using a public API key — there is no IAM, no audit trail, and no path to production that your security team is able to approve.
Even though your data is stored in BigQuery and your applications are deployed on GKE, AI experimentation occurs elsewhere, so every request pays an integration cost.
Different teams are calling various models without shared grounding, without any means of controlling costs, and without a common quality standard.
What we build on Vertex AI
Gemini application development
By using custom LLM applications on Gemini models via Vertex AI, you are able to access generation, analysis, and multimodal capabilities together with enterprise controls from the start.
Agents on Vertex AI Agent Builder
Production agents that make use of your tools and data, created with Agent Builder and custom orchestration — with guardrails and human approvals built into the design.
Grounded RAG & Vertex AI Search
You are able to get answers together with the sources by making use of Vertex AI Search and vector retrieval with the knowledge systems that are based on your documents and data, all remaining within your own network.
Model tuning & evaluation
When your domain requires it, we tune models on Vertex AI and carry out rigorous evaluations both before and after, so that any improvements are proven rather than just assumed.
MLOps on Vertex AI
Pipelines, the model registry, the endpoints, and the monitoring systems make up the operational core which ensures that the models are deployed, supervised, and retrained.
Safety, cost & governance
Safety filters, quota and cost controls, and IAM-aligned access controls are put in place so that growth will not unexpectedly affect either your bill or your CISO.
Where this earns its keep.
Every engagement runs spec-first, agent-accelerated, human-owned.
A written specification that has been approved by the client prior to any coding taking place — covering the architecture, the edge cases, and the acceptance criteria.
The engineers arrange for the AI agents to carry out the specification in parallel, taking weeks rather than quarters.
The evaluations, tests, and security checks are carried out together with the build, and humans examine every decision that is important.
The system has been deployed to your GCP or AWS, complete with monitoring, production evaluations, and retraining loops.
From the portfolio.
A knowledge-grounded assistant that speeds up support resolution
An internal support assistant which enables employees to resolve technical problems by asking questions in plain language and receiving answers based on the organization's own documentation, rather than having to go through error logs and refer to dispersed sources.
Tools we reach for.
Chosen for each project based on evaluations and fit — never out of habit.
Questions, answered.
Why Gemini and Vertex AI over calling a model API directly?
Is our data used to train Google's models?
Can you migrate our existing OpenAI or other LLM app to Gemini?
How do you control Vertex AI costs?
Do you only work with Gemini models?
Planning something on Google Cloud? Let's pressure-test it — free.
A 30-minute call with a certified engineer, not a salesperson. You'll get an honest feasibility read, a rough architecture, and a clear next step.
Book a free AI consultation