- contact@verticalserve.com
We are a Google Cloud partner building Gemini-powered agents, governed enterprise search and the data platforms underneath them — delivered as Google-standard services in your own project, from a library of production accelerators rather than a blank page.
Most programs stall on the same three things: content that isn’t governed, permissions that don’t follow the data into retrieval, and a data estate nobody trusts. We start there.
Gemini-powered multi-agent harnesses for support, operations and decision workflows, with human escalation paths and tool policies kept outside the model.
Governed knowledge bases with permission-aware retrieval, metadata strategy, freshness management and retrieval-quality evaluation.
Multimodal extraction from complex documents into BigQuery and Cloud Storage pipelines, with validation and human review for regulated work.
Ingestion, curation and consumption layers on Cloud Storage, BigQuery, Dataflow and Dataproc, serving analytics, data science and agents.
Pub/Sub and Dataflow pipelines, plus Kafka where it is already the backbone, for event-driven features and agent context.
Vertex AI training, tuning and deployment, domain-specific models on GKE GPU clusters, and evaluation and tracing as standard.
Zero-trust data platforms, cataloging and lineage, access monitoring and DLP, so many teams can build safely on one platform.
Automated Hadoop-to-GCP moves and Airflow on GKE, with workload inventory, pattern mapping, phased cutover and validation.
Finding the use cases where Gemini changes the P&L, then building the intelligence layer: 360° views, analyst copilots and decision agents on enterprise data.
Governed data first, models and agents on top, people deciding at the edge.
Working products, not slideware. On a Google Cloud engagement they compress the first weeks of an agentic program, and everything they produce lands on GCP-native services.
| Product | What it does | Google Cloud mapping |
|---|---|---|
| InsightAsk | Governed enterprise search and knowledge base: permission-aware retrieval, freshness and evaluation built in. | Gemini Enterprise search & grounding; Vertex AI Search patterns |
| InsightWorker | Multi-agent harness that automates engineering and operational workflows. | Gemini function calling & tool use; Cloud Run / GKE |
| InsightStudio | Business teams assemble and ship AI apps in the browser. | Gemini models; GCP-native deployment |
| InsightXtract | Agentic document extraction with validation and human review. | Gemini multimodal; Cloud Storage pipelines |
| InsightPulse | 360 entity views assembled from internal and external sources. | Gemini reasoning over BigQuery and unstructured sources |
| InsightDLM | Domain-specific small and large models, from curation to serving. | Vertex AI tuning & Model Garden; GKE GPU serving |
| InsightLense | LLM tracing: cost, latency, quality and drift per agent and prompt. | Cloud Logging / Monitoring; token & cost governance |
| InsightTestBench | AI-native QA: agent-generated and agent-executed regression suites. | Any GCP delivery program's test cadence |
| InsightRAG & InsightCatalog | RAG with first-class metadata, plus an enterprise AI data catalog. | BigQuery / Dataplex alignment |
| InsightViz | BI where the dashboard explains why a number moved. | BigQuery extracts; deployment notes |
| InsightClickIQ | AI-native click analytics with an AI analyst. | Cloud Run, Cloud SQL or AlloyDB, Cloud Storage, Vertex AI; deployment notes |
| InsightResolve | Agentic L1 support and incident resolution. | Cloud Monitoring, Cloud Logging, Error Reporting; deployment notes |
Case studies and the kinds of programs we have delivered on Google Cloud. We don’t name clients on this site.
Weather, vegetation, terrain and grid telemetry fused into a near-real-time ignition-risk platform for a major utility.
Read the case study →A multi-petabyte on-prem Cloudera estate moved to a serverless GCP data lake and ETL stack, using automation rather than heroics.
Read the case study →Enterprise data lakes and warehouses on GCP with end-to-end ML on Vertex AI, Dataproc and Dataflow, built for platform teams to reuse.
Zero-trust security data platforms with governance and cataloging, and unstructured data platforms that became the base for enterprise RAG.
An agent harness that triages, retrieves and resolves L1 support on a governed knowledge base, with human escalation preserved.
Stand up a working pilot on customer data in days, or use an accelerator as the reference implementation for your own Gemini foundation.
How PODs work →Posts from VerticalServe and our product teams on building and running on Google Cloud.