Named clients.
Real work.

Help articles were assembled by hand and drifted away from the product with every release.

A Claude workflow inside the release pipeline that writes and checks each article as code ships.

Loans were tracked by hand, with little record of why a number changed.

Claude reads each credit agreement and drafts the loan-book change, citing its source clause.

Rapid growth needed a secure, HIPAA-compliant platform for AI-powered radiology.

Migration from AWS to Google Cloud, with applications refactored for HIPAA compliance and GPU capacity for model training.

Buyers could not search structured and unstructured supplier data in plain language.

Generative AI search on AlloyDB Vector Store and Vertex AI, with contextual filtering.

ChargePoint had to replicate its AWS environment on Google Cloud and simplify procurement.

Terraform-built GKE, Cloud SQL, secure networking and Cloud Armor across Dev and Stage environments.

Mission-critical satellite workloads had to move from AWS to Google Cloud under ITAR rules.

Wave-based migration with Migrate to Virtual Machines and Database Migration Service, on Assured Workloads.

Drug discovery research slowed by manual data extraction and analysis.

Vertex AI and Document AI models, plus a Gemini assistant scientists can question.

In-game banners, characters and backgrounds took too long and cost too much to produce.

A tuned Stable Diffusion model on Google Cloud, with curated data and a secure interface.

Manual document review slowed every loan through underwriting.

A generative AI underwriting copilot on Vertex AI and Dialogflow, using directed retrieval.

Carbon needed a secure Google Cloud foundation to leave AWS and modernize its delivery pipelines.

A landing zone with hybrid connectivity, high-availability GKE clusters and automated CI/CD.

Machine learning for personalized mental health care had to scale while keeping patient data compliant.

A HITRUST-compliant Google Cloud environment and an ML pipeline on Vertex AI, Kubeflow and KServe.

Infoblox had to prove a secure, scalable platform before moving its core applications.

A production-ready Google Cloud landing zone on GKE, IAM and secure networking.

Customer data arrived from four database types, of uneven quality.

Datastream replication to BigQuery from Oracle, MySQL, SQL Server and PostgreSQL, with validation.

Finding lithium-rich regions meant reading vast volumes of scientific reports by hand.

Automated extraction and predictive models across 50,000 reports with Vertex AI, Document AI and BigQuery.

Customer conversations were spread across HubSpot, Slack and email, with inconsistent answers.

BenBot, a conversational hub on Vertex AI trained on a generative playbook, across every channel.

Field-service teams needed real-time, AI-driven answers on technicians and operations.

A multi-tenant Vertex AI conversation agent built with Dialogflow CX, LangChain and ReAct.

Every shopper saw the same products, whatever their interests.

Personalized recommendations built on Google Retail API and BigQuery, served through Vertex AI.

Teams lacked self-service, real-time insight into trade promotion results.

A secure Looker platform with LookML models, access controls and hands-on training.

Users needed accurate answers drawn from a vast library of recordings and transcripts.

A retrieval-based generative AI assistant on Gemini, grounded in Pray.com’s own content.

A single-cloud setup limited availability and created vendor lock-in.

Translation Service migrated into a modular, containerized Kubernetes environment on Google Cloud.

Spotz needed infrastructure that could scale with customer growth and future AI features.

A full migration of its backend infrastructure from AWS to Google Cloud.

In-store stylists needed to navigate 7+ systems to manage purchases, inventory, returns, security and more.

STITCH, an AI knowledge assistant that answers across knowledge bases and SOPs in seconds, on a RAG workflow.

Selecting and optimizing creative assets for account-based marketing journeys was manual, slow to market and subjective.

A dual-agent system that learns from historical CTR data and image features to suggest assets autonomously.