Gemini's edge is multimodal input — text, image, video, and audio in the same call — and a native fit with Google Cloud when that's already where your infrastructure lives. We integrate the Gemini API for products that need those two things specifically, not as a default pick over other models.
Gemini is straightforward to call and easy to reach for out of habit. A dedicated team picks it when it's actually the right model and builds the integration to hold up past the demo.
Prompts and pipelines built to handle image, video, and audio input alongside text — not text-only integrations with modality bolted on later.
Gemini called through Vertex AI when your data, auth, and infrastructure already live on Google Cloud, keeping the integration inside your existing security boundary.
Integration layer built so swapping or mixing Gemini, GPT, or Claude later is a config change, not a rewrite.
Model tier selection, caching, and token budgeting so a useful feature doesn't turn into a runaway API bill.
Documented prompts, eval sets, and clean integration code so your in-house team can own and extend it after launch.
Scoped to the AI features your product actually needs — not a generic chatbot bolted on for its own sake.
Image, video, and document understanding built into your product using Gemini's native multimodal input.
Gemini called through Vertex AI for teams already running on Google Cloud, keeping data and auth inside existing infrastructure.
In-product chat, support assistants, and copilots built on the Gemini API.
Vector search over your documents or product data so responses are grounded and current, not just general knowledge.
Adding AI features into your existing product — search, summarization, drafting — without a rebuild.
Mainly two cases: your product needs native multimodal input — image, video, or audio alongside text in one call — or your infrastructure already runs on Google Cloud and Vertex AI keeps data inside that boundary. Outside those cases, we benchmark against your actual use case rather than assuming Gemini is the default.
Yes, that's one of Gemini's clearest strengths. We've built document understanding, image analysis, and video-input features on top of the API for products where text-only models weren't enough.
Yes. We call Gemini through Vertex AI when it makes sense, reusing your existing IAM, networking, and data pipelines instead of standing up a separate integration outside your Google Cloud environment.
It depends on scope — a single multimodal feature costs far less than a retrieval-backed assistant integrated across your product. We give a fixed estimate after a short scoping call rather than a generic range.
Tell us what you're trying to add — multimodal features, chat, search, an assistant — and we'll map an approach and a fixed estimate.