Gemini

Multimodal AI with Gemini across text, image, and data

Google's Gemini models are strongest where inputs are mixed: documents with charts, screenshots, video, and long context windows that swallow entire reports. We build Gemini systems for teams already invested in Google Cloud and for workloads where multimodality is the point, not a gimmick.

Multimodal inputsLong contextGoogle Cloud native
Quick answer

Gemini integration services: multimodal AI across text, image, and data, long-context processing, and native reach into the Google Cloud ecosystem.

Text-only AI misses half your data

Real business information lives in screenshots, scanned PDFs, dashboards, and slide decks, not clean text. Systems that only read text either skip that content or lean on brittle OCR chains. Multimodal models read the artifact as a human would, which removes an entire class of preprocessing failure.

What we build with Gemini

  • Multimodal pipelines that read documents, images, and charts together
  • Long-context analysis over reports and archives without chunking gymnastics
  • Vertex AI deployments wired into your existing Google Cloud projects
  • Structured extraction from visually formatted documents

How we work

  1. Identify where multimodal input removes real preprocessing pain

  2. Prototype against your actual documents, not curated samples

  3. Deploy through Vertex AI when you are already on Google Cloud

  4. Add the same grounding and eval discipline as any model we ship

Typical stack

Gemini APIVertex AIGoogle CloudDocument pipelines

Frequently asked questions

No, the API works anywhere. Being on Google Cloud adds convenience: IAM integration, Vertex AI tooling, and data staying inside one vendor boundary, which some compliance teams prefer.

Mixed-media document processing, long-report analysis, and anything already adjacent to BigQuery or Google Workspace data. For pure-text agent work we often benchmark it against Claude and GPT and let results decide.