Ship Multimodal Products on Google Gemini

Gemini 1.5 Flash is 10–15x cheaper per token than 1.5 Pro and handles the majority of enterprise use cases well: classification, extraction, summarization of well-structured documents, simple Q&A, and code generation for common patterns. Gemini 1.5 Pro earns its cost premium for complex reasoning chains, ambiguous document interpretation, tasks requiring nuanced instruction following, and long-context analysis of unstructured content where Flash degrades. Our standard architecture uses Flash as the default and routes to Pro based on a lightweight complexity classifier, keeping costs predictable while maintaining quality for hard cases. Typical blended cost for a document processing pipeline runs $0.50–$2.00 per 1,000 documents depending on length and routing ratio.

Hallucination control is a design problem, not just a prompting problem. The most effective techniques we use in production: (1) Retrieval-Augmented Generation — only allow the model to cite content retrieved from your verified document corpus, and instruct it explicitly to respond 'I don't have that information' when the context doesn't support an answer. (2) Structured output enforcement — when extracting data, a JSON schema prevents the model from inventing fields or values. (3) Grounding with Google Search (available on Vertex AI) — for factual queries where your corpus doesn't cover recent events, grounding anchors responses to web content. (4) Confidence thresholds — for high-stakes outputs, we run ensemble calls and flag low-agreement responses for human review.

Yes, through function calling. We define your internal APIs as Gemini tools with JSON Schema descriptions, and the model invokes them as part of its reasoning chain. Common integrations we've built: BigQuery query execution (the model writes SQL, we execute and return results), CRM lookups (Salesforce, HubSpot), ERP data retrieval, internal knowledge base search, and calendar/scheduling APIs. The integration layer handles authentication, rate limiting, error handling, and result injection — the model sees clean tool responses and continues its reasoning. This architecture lets you add new capabilities by registering new tools without rewriting prompt logic.

A focused Gemini integration — one use case like document extraction, a customer-facing Q&A assistant, or a code review tool — typically runs 6–10 weeks for a production-quality build including evaluation infrastructure and observability. Cost estimate: 300–500 engineering hours at $70–$100/hour for Codieshub senior AI engineers, totaling $21,000–$50,000 depending on complexity. Projects requiring extensive RAG pipeline development, custom fine-tuning data preparation, or deep Vertex AI compliance configuration land at the higher end. We deliver a fixed-scope Phase 1 to demonstrate value, then move to iterative development for subsequent features.

We handle both, and we help clients make the right choice. Fine-tuning is warranted when: you have 500+ high-quality labeled examples of your specific task, prompt engineering alone doesn't reliably produce the required output format or domain terminology, or you need consistent behavioral alignment with company-specific style and policy. Fine-tuning is not warranted when: your task is well-covered by Gemini's base capabilities, your data is too limited, or the use case evolves frequently (fine-tuned checkpoints require retraining for each significant update). Supervised fine-tuning on Vertex AI using your proprietary dataset with held-out evaluation is our standard approach when fine-tuning is appropriate.