Field notes on AI that ships

Practical lessons from AI deployments in complex, regulated environments.

A Founder's Guide to GenAI Proofs-of-Concept on Google Cloud

AgentsPOCGCP

The practical playbook for running your first GenAI POC on GCP. Credits, scoping, success criteria, and what to harden before pilot. Written for founders and engineering leaders who need to ship something measurable in 4 to 6 weeks.

Zeptara

Zeptara

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How to Choose a GenAI Consultancy on Google Cloud

AgentsConsultingLLMGCP

A buyer's guide. The signals that actually predict good delivery, the ones that don't, and the questions to ask in a scoping conversation before you sign anything.

Zeptara

Zeptara

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Multi-Model GenAI Architectures on GCP: When Gemini Isn't the Right Tool

ModelsLLMGCP

An honest post about model selection. Gemini is excellent, and it's the default for almost everything we build. It's not always the right answer, and pretending otherwise loses deals.

Zeptara

Zeptara

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GenAI for E-commerce Ops: Catalog Enrichment, Search, and Merchandising on GCP

AgentsE-commerceLLMGCP

E-commerce operations is one of the most under-served categories for GenAI consulting. The use cases are concrete, the ROI is measurable, and Google Cloud has a particularly relevant product stack. Here's what we build.

Zeptara

Zeptara

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Zeptara for Google Cloud

AgentsZeptaraGCP

Our position on when Google Cloud is the right platform for GenAI in 2026, what GCP gets uniquely right, and where we'll honestly tell clients to look elsewhere.

Zeptara

Zeptara

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Building Internal Copilots: A Vertex AI + Workspace Integration Pattern

AgentsCo-PilotLLMGCP

Internal copilots are the GenAI deployment with the fastest path from 'let's try this' to 'the team relies on it.' Google Workspace plus Vertex AI is a particularly clean stack for them. Here's the integration pattern we use.

Zeptara

Zeptara

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Document Intelligence Pipelines on Google Cloud: Document AI + Gemini for Enterprise Back-Offices

AgentsDocumentsLLMGCP

Invoices, contracts, claims forms, KYC packets. Document processing is unglamorous, high-value, and a category where Google Cloud has a stronger product stack than most people realize. Here's how we build it.

Zeptara

Zeptara

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GenAI for Customer Support: A Reference Build on Vertex AI

SupportAgentsLLMGCP

Support automation is one of the most attractive GenAI use cases for enterprise buyers and one of the fastest ways to ship something that fails badly. Here's the reference architecture we build on Google Cloud, and the design choices that separate the systems that hold up from the ones that don't.

Zeptara

Zeptara

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How to Evaluate a GenAI Application Before You Ship It

AgentsEvalsLLM

An opinionated guide to building eval harnesses for GenAI on Google Cloud. The mechanics, the trade-offs, and the patterns that hold up over a 12-month system lifetime.

Zeptara

Zeptara

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Designing GenAI Agents That Don't Hallucinate Themselves Into Production Incidents

AgentsGuardrailsgenAIGCP

Agents are powerful and dangerous in roughly equal measure. This post is the discipline we apply at Zeptara to make them shippable in production.

Zeptara

Zeptara

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Five RAG Patterns We Keep Building on Google Cloud, and When Each One Is Right

AgentsRAGLLMGCP

Retrieval-augmented generation isn't a single technique. It's a family of patterns with very different trade-offs. Here are the five we end up building most often on GCP, and how to tell which one your use case needs.

Zeptara

Zeptara

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Build vs. Buy vs. Fine-Tune: A Decision Framework for Enterprise GenAI on Vertex AI

AgentsFine-TuneGenAIGCP

The most common scoping mistake in enterprise GenAI is picking the implementation approach before understanding the use case. This is our corrective: a decision process that starts from requirements and arrives at the right rung on the complexity ladder.

Zeptara

Zeptara

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The GenAI Consulting Engagement Playbook: How We Scope, De-Risk, and Ship in 90 Days

AgentsLLMGCP

Most GenAI consulting engagements fail before the first model call. They fail in scoping, on the day someone said 'let's build an AI thing' and everyone nodded without defining what done looks like. This is our playbook for not doing that.

Zeptara

Zeptara

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A Reference Architecture for Production GenAI on Google Cloud

AgentsLLMGCP

This is the post we link from every Zeptara proposal. It's the stack we use as a starting point for production GenAI systems on Google Cloud, the seams between the components, and the decisions we've learned to make early.

Zeptara

Zeptara

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From Fragmented Project Data to Focused Standups

Case StudyConstructionRAG

Zeptara used a custom OpenAI and RAG copilot to help Haskell project managers keep subcontractors and subprojects aligned and run higher value standups and review meetings.

Zeptara

Zeptara

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From Sidecar Copilot to Daily Habit in Logistics

GenAICase StudyLogistics

Zeptara used OpenAI based copilots to help Crowley turn a sidecar analytics project into a core tool that optimizes network performance and analyst productivity.

Zeptara

Zeptara

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Halving service Diagnostic time with Generative AI

GenAICase Study

Zeptara used Anthropic Claude on AWS Bedrock to cut technician diagnostic time in half and reduce service escalations for Yamaha.

Zeptara

Zeptara

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