Data Panda lands your business data in one warehouse and lets Gemini read it. Gemini 3 Pro answers questions over a million tokens of your own documents at a time, parses PDFs, slides and screenshots natively, and drives agentic workflows that act back on the systems where the work happens.
Gemini is the LLM family built by Google DeepMind, the AI research arm formed when DeepMind and Google Brain merged in April 2023. The first generation (Gemini 1.0) was announced in December 2023 as Google's answer to GPT-4, with multimodality built in from the start rather than bolted on as a separate vision model. Gemini 1.5 Pro then introduced the long-context era in February 2024 with a 1M-token window, and the line has shipped roughly twice a year since: Gemini 2.0 Flash at the end of 2024, Gemini 2.5 Pro in March 2025, and the Gemini 3 series with Gemini 3 Pro, Gemini 3 Flash and Gemini 3 Flash-Lite as the current generation.
Two things set Gemini apart from the rest of the LLM market. The context window is genuinely long: Gemini 3 Pro keeps the 1M-token capacity, which is enough to fit a full codebase, hundreds of pages of contracts, or hours of video transcript in a single prompt. And the multimodality is native: text, images, audio, video, code and PDFs go through the same model rather than through a vision tower glued onto a text model. Access splits along audience: Google AI Studio (aistudio.google.com) is the consumer and developer surface for prototyping, the Gemini API is the direct programmatic interface, and Vertex AI on Google Cloud is the enterprise route with grounding on Google Search or Vertex AI Search, function calling, context caching, VPC controls and per-region data residency.
Gemini usage and retrieval performance per workflow, side by side with the warehouse content the model is reading.
Pipe Gemini 3 Pro and Gemini 2.5 Flash decisions straight into the systems where the work gets done.
Gemini reads what is in the warehouse, with grounding controls to keep answers tied to a citable source row.
Internal tools that sit on warehouse data and call Gemini only for the language and vision work.
A list of concrete reports, automations and AI features we have built on Google Gemini data. Pick the one that matches your situation.
Is Gemini truly answering from our warehouse, or from its own training data?
Grounding-hit rate per query template, plotted against the warehouse table the answer should have come from. Surfaces the queries where Gemini ignores the retrieved context and produces a confident answer from its own weights instead, which is the moment a RAG pipeline starts citing a price list that does not exist or a clause that was never in the contract.
Which workflows are spending the most on Gemini, and is the spend producing answers people use?
Token spend per Gemini model and endpoint joined to the internal workflow that triggered the call and to the user feedback on the answer. Shows the document-parsing workflow burning the bulk of the Gemini 3 Pro budget while half the outputs get manually corrected, next to the briefing-summary workflow that costs less and gets accepted as-is on most calls.
Are we really getting the cache discount on long context?
Context-cache reuse per template, on a daily curve. Catches the system prompt that quietly drifted past the cache-eligible window after a release, or the template whose preamble shifts by a few tokens between calls so the cache misses every time, both of which double the input cost on a 1M-token Gemini call without changing the answer.
Token spend per Gemini model, per workflow and per business unit, joined to the workflow's measured outcome. The AI line on the budget moves from a single Google Cloud invoice to a number that sits next to the documents parsed and briefings shipped.
Per-customer briefing assistant on top of CRM, support and contract history, so the rep walks into the meeting with a one-page summary instead of three browser tabs. Gemini reads the call recordings and the contract PDF in the same prompt, and the rep starts from a draft instead of a blank page.
Gemini usage, grounding-hit rate and answer-quality feedback per workflow on one screen, refreshed daily. The RAG pipeline is followed as a curve, not rediscovered the morning a stakeholder forwards a screenshot of a wrong answer.
You keep the reporting tool you already have. We connect it to the warehouse where your Google Gemini data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Gemini 3 Pro is the current flagship for chat, agents and long-context RAG, with a 1M-token context window and native multimodality across text, images, audio, video and code. Gemini 3 Flash and Gemini 3 Flash-Lite cover the cheaper and faster tiers when the full Pro model is overkill. Gemini 2.5 Pro and Gemini 2.5 Flash remain generally available for workloads that have already been validated against them. Embedding models from the Gemini API can be used to build the vector index that feeds retrieval before the chat call.
The Gemini API at ai.google.dev is the direct programmatic interface, paired with Google AI Studio for prototyping in the browser. Billing runs on a Google account and the workload sits outside Google Cloud's IAM and networking. Vertex AI on Google Cloud exposes the same Gemini models inside a GCP project, with grounding on Google Search or Vertex AI Search, function calling, context caching, IAM permissions, VPC Service Controls and per-region data residency. Enterprise workloads with regulated data, audit requirements or existing GCP footprint typically land on Vertex AI; consumer apps and prototypes usually start on the direct API.
Two things at once. First, an entire codebase, a full bundle of contracts, or hours of video transcript fits in a single prompt, so the model does not have to be primed with a chunked-and-summarised version of the same content. Second, the multimodality is native: a scanned PDF, a screenshot of a dashboard and a slide deck go through the same model as the warehouse rows in the same prompt, without a separate OCR or vision step. The two combined are what make multimodal document workflows and codebase-scale Q&A practical, but only if the warehouse can surface the right million tokens at retrieval time.
We review your Google Gemini setup and the systems around it. Together we pick the first thing worth building.