How It Works
We design the integration foundation — selecting the right Gemini model (Flash vs Pro), defining grounding strategy with Google Search or enterprise data, and architecting function calling and tool use patterns for your use case.
We build the integration end-to-end — wiring the Vertex AI API, configuring grounding, building the evaluation pipeline, and applying safety filters before any production traffic touches the model.
We deploy to production, wire up token cost monitoring, and run prompt optimisation cycles to maximise quality-per-dollar as usage scales across your team or product.
What's Included
Rigorous benchmarking of Gemini Flash, Pro, and Ultra variants against your actual workload — latency, accuracy, cost, and context window — so you pick the right model from day one.
Connect Gemini to live Google Search results using the Vertex AI Grounding API, dramatically reducing hallucinations and keeping responses current without manual knowledge-base maintenance.
Ground Gemini responses in your proprietary documents, databases, and knowledge bases using Vertex AI Search datastores — keeping sensitive data within your GCP environment.
Build Gemini integrations that call external APIs, execute database queries, trigger workflows, and use tools — turning Gemini from a text generator into an action-taking agent.
Supervised fine-tuning of Gemini models on your domain-specific data using Vertex AI — improving response quality and domain knowledge without sharing your data with Google.
Systematic prompt compression, caching strategy, and model tier selection to reduce Gemini API spend by 30–60% while maintaining or improving response quality.
Who It's For
Engineering teams building Gemini-powered features into their SaaS products or internal tools — you need a production-grade integration with grounding, safety, and cost controls from the start.
Teams migrating from GPT-4 or Claude to Gemini on Vertex AI — you need expert guidance on model parity, prompt adaptation, and cost optimisation during the transition.
Enterprises that cannot send proprietary data to external search APIs — you need Gemini grounded in your internal documents within GCP's VPC Service Controls perimeter.