Google · Vertex AI
Production AI on Google Cloud.
We design, build, and operate Gemini agents, RAG systems, and tuned models on Vertex AI, with proper MLOps, evaluation, and governance so your AI ships and stays reliable.
- Vertex AI & Gemini
- BigQuery-native RAG
- MLOps by default
- Production
- Not just prototypes
- Grounded
- RAG on your own data
- Evaluated
- Objective quality metrics
- Governed
- IAM, VPC-SC, audit
The problem
AI POCs are easy. Reliable production is not.
Prototype-to-production gap
The demo worked in a notebook, but nothing about it is fit to run in front of customers.
No RAG on real data
Your data lives in BigQuery, Drive, and a dozen SaaS tools. Nothing grounds the model in it.
No evaluation loop
You can't tell whether the AI is getting better or worse, because nothing's being measured.
Security unanswered
Legal, IT, and compliance won't sign off because nobody's shown them the guardrails.
The solution
Vertex AI, engineered for production.
Gemini-native builds
Agents and RAG systems built on Vertex AI with tool-use, function-calling, and grounding.
BigQuery-native retrieval
Vector Search and BigQuery ML embeddings, retrieval that scales with your warehouse.
MLOps by default
Model Registry, Pipelines, monitoring, and rollback, every deployment is auditable.
Governance & compliance
VPC Service Controls, CMEK, IAM, and audit logging aligned with SOC 2 and HIPAA.
What's included
What we ship.
PILLAR 01
Gemini agents
Agents that take action, safely.
- Function-calling and tool-use design
- Multi-step reasoning workflows
- Human-in-the-loop where it matters
- Fallback and escalation logic
PILLAR 02
RAG systems
Grounded in your data, not the model's guess.
- Vertex AI Vector Search
- BigQuery ML embeddings for scale
- Hybrid retrieval and reranking
- Citation and freshness controls
PILLAR 03
Model tuning & MLOps
Custom models, operated properly.
- Supervised tuning for Gemini and PaLM
- Vertex Pipelines and Model Registry
- Continuous evaluation with test sets
- Rollback and canary deployments
PILLAR 04
Governance
Ready for legal and IT.
- VPC-SC and CMEK
- IAM and per-endpoint access
- Prompt injection defense
- Audit logs and evaluation reports
Process
Five stages. Ship, then compound.
- 01
Discover
Prioritize use cases by ROI, feasibility, and risk. Pick the first one to ship.
- 02
Design
Architecture, retrieval design, guardrails, and evaluation plan approved before build.
- 03
Build
Vertex AI pipeline, RAG or agent, integrations, and evaluation harness.
- 04
Validate
Objective evals against a golden set. Human review of edge cases before rollout.
- 05
Operate
Monitoring, drift detection, and quarterly model reviews.
READY WHEN YOU ARE
Ship AI on Google Cloud, properly.
Tell us the use case. We'll come back with a Vertex AI architecture and cost estimate within one business day.