Incomplete, ungoverned data
A model can answer correctly and still be unusable. We fix retrieval and data quality before model work starts.
AI & Generative AI · Capability
Agentic workflows, decision intelligence, and assistants that run in production on your real business systems.
AI programs do not fail because teams lack demos. They fail because the demo never connects cleanly to data, security, workflows, evaluation, cost control, and ownership. SquareShift helps engineering, data, and business teams turn useful AI ideas into systems people can trust and operate.
Google Cloud Premier Partner – ServiceVertex AI deliveryElastic Generative AI Partner
02 The pivot
Most AI pilots stall on data, trust, and ownership — not on the model.
Most AI pilots do not fail because the model is weak. They fail where the system around the model is incomplete.
01
02
Answers no one relies on
An assistant can sound confident and still be wrong. We build evaluation and grounding into the assistant, not just the generation step.
03
No one owns the exceptions
A time-saving workflow stalls the moment an exception appears. We define workflow boundaries, review points, and an audit trail before it goes live.
04
Cost and risk you can't see
Developers adopt AI tools faster than leaders can see their cost or risk. We add AI engineering observability so adoption is visible instead of assumed.
The same four gaps keep showing up — whether the project is a search tool, an assistant, a workflow, or a developer tool.
03 How we make it production-ready
We help you pick the first AI use case that can reach production — not just impress in a demo.
Each stage below is a decision, not just a delivery task. We name what you decide and what you get at the end of it.
01
Use-case selection and readiness review
Decide which one workflow, data problem, or assistant idea is worth building first. Produces a scoped use case with a named owner and a readiness checklist.
02
Data and retrieval architecture
Decide what data sources, permissions, and retrieval design the use case actually needs. Produces the data and retrieval plan the pilot is built on.
03
Working pilot with evaluation criteria
Decide how you will know the pilot is good enough before real users depend on it. Produces a working pilot and the evaluation criteria it has to pass.
04
Production rollout with governance
Decide who owns the system, how it is monitored, and how it scales. Produces a production system with a named owner, monitoring, and a support model.
One tradeoff we make explicit early, not after the fact: which model family to use. That choice is driven by cost, privacy, performance, latency, and compliance requirements, not by which model is newest. On the production work behind this page, that has usually meant building on Google Cloud's Vertex AI and Gemini models — see the case studies below.
04 What we build
SquareShift builds five kinds of production AI.
Each pattern below solves a different production problem, and most map to a ready-made accelerator you can start from.
Decision intelligence and conversational analytics
Natural-language questions answered against a governed BI model, so more day-to-day decisions run on trusted numbers without bypassing metric definitions or access controls.
ExploreAgentic workflows & business process transformation
Agents that safely take on manual effort across a business process, with review points, an audit trail, and a fallback path — reshaping how the work runs, not just automating one task.
ExploreAI-assisted software engineering
Using AI across the delivery lifecycle — code, review, tests, documentation — without losing architectural judgment.
ExploreAI-powered search & document intelligence
Semantic and hybrid search over enterprise content and documents that shows the evidence behind an answer, not just the answer.
ExploreAI assistants and copilots
Narrow, role-specific assistants — support triage, knowledge lookup, sales enablement, security analysis — connected to the right systems and tested against real user questions. Delivered as part of the work above.
05 Accelerators
Ready-made accelerators make these plug-and-play, not a long build cycle.
Each one is a proven starting point with its own delivery path and case-study proof, so the work begins from a running base, not a blank page.
Decision intelligence & conversational analytics
Natural-language analytics over Looker's governed semantic layer with Google Gemini, so questions never bypass metric definitions or access controls.
ExploreAgentic workflows & business process automation
Agents that safely take on manual effort across a business process, with review points, an audit trail, and a fallback path.
ExploreAI for software engineering
AI-assisted development across the delivery lifecycle — code, review, testing, documentation — without losing architectural judgment.
ExploreAI-powered search
Semantic and hybrid retrieval on your product catalog, tuned for relevance.
ExploreSearching internal documents instead? See Document Intelligence
AI Engineering Observatory
Observability for AI-assisted development itself: prompts, cost, and risk, running on the Elastic deployment you already have, with no new infrastructure.
Explore06 Partner-verified + delivered
A Google Cloud Premier Partner, with five delivered AI case studies behind these solutions.
Google Cloud Premier Partner – Service. Vertex AI — Gemini Pro, PaLM 2, and Vertex AI Agent Builder — is the platform behind three of the case studies below.
Client work
Solutions we've crafted for our clients
Agentic AI
Billions of events turned into repeatable insight. A VC-backed analytics platform needed auditable user-friction findings; a hybrid deterministic and LLM pipeline on BigQuery, Vertex AI, and Gemini delivered 10x faster analysis with 100% repeatable findings.
Read the case study AI Powered SearchA generative-AI copilot for global chip security. A Fortune 500 semiconductor leader needed analysts to stop cross-checking multiple tools and advisories by hand; a RAG-based chatbot on Vertex AI now triages and remediates vulnerabilities.
Read the case study Agentic AIAn HR assistant that answers from trusted policy. A global consulting firm needed a small HR team to answer repeated policy and benefits questions from one trusted document base; a Vertex AI Agent Builder chatbot in Slack now answers with source links.
Read the case study AI Powered SearchMatching new defects to proven fixes, across two languages. A commercial-truck maker needed quality engineers to stop manually searching bilingual issue logs; a BERT-based NLP model now matches new defects to the top historical fixes.
Read the case study AI Powered SearchEarly risk signals for a global insurer. Hundreds of analysts once reviewed journals and feeds by hand; an OCR, NLP, and machine-learning pipeline now surfaces emerging signals on a real-time dashboard.
Read the case study08 Book a session
One AI discovery session turns a real workflow, data problem, or assistant idea into a scoped, buildable plan.
Book an AI discovery session. Bring one real workflow, one data problem, or one assistant idea. We will help decide whether it is ready to build, what needs to be true first, and what a credible first release should include.