AI Readiness
Your board wants an AI strategy. Your ops team wants something that actually works. Those are not the same conversation — and most firms only know how to have one of them. We build the architecture that makes both possible.
Most AI projects fail before they start. Not because the technology doesn't work — it does. They fail because the data isn't clean, the identity layer isn't governed, and nobody defined what "production" actually means for their company.
You can't bolt AI onto a disorganized Workspace deployment and call it a strategy. You need an architecture that gives AI agents clean inputs, governed access, and a place to put their outputs. That's what we build.
"The companies winning on AI didn't buy better AI tools. They built cleaner data infrastructure and gave those tools something worth working with."
AI agents need to access data. That access has to be governed. Google Workspace identity — SSO, groups, admin roles, API scopes — is the foundation. If your identity layer is messy, your AI will be too.
Shared Drives structured by function. Naming conventions enforced. BigQuery schemas defined and documented. If your data lives in personal drives with no structure, your AI has nothing to reason over.
Gemini in Gmail, Docs, Sheets, Meet, and Drive — enabled for the right users, with the right DLP controls in place so AI features don't create data exposure. Most orgs enable it globally and call it done. We set it up properly.
Custom AI agents that handle real workflows. Built on Vertex AI, trained on your data, connected to your Google Workspace environment. Not pilots — production agents with actual owners and SLAs.
AI acceptable use policy. DLP rules for AI-generated content. Audit logging for agent actions. The controls that let you say "yes" to AI without creating unacceptable risk.
These are real workflows we've built on Vertex AI and Agent Builder — not theoretical use cases.
Operations
New hire triggers account provisioning, Drive folder creation, app assignment, and onboarding checklist — all from a single HR system event.
Finance
Scanned invoices extracted, categorized against your chart of accounts, and routed for approval — without human touch until the approval step.
Sales
Inbound leads enriched with company data, scored against your ICP, and routed to the right rep — before anyone reads the email.
HR
Employees ask questions. The agent answers from your actual policy docs in Google Drive — not a hallucinated summary of generic HR content.
Legal / Compliance
Incoming contracts flagged for non-standard clauses, liability caps, and IP ownership issues — before your lawyer reads page one.
Executive
Weekly data pulled from BigQuery, formatted into board-ready summaries — AI writes the first draft, your team reviews, your board gets it on time.
If any of these aren't in place, we fix them before touching AI implementation. Skipping this step is why most AI projects fail.
Required
Admin console hardened, DLP active, Shared Drives organized, Gemini licensed.
Required
Groups and roles reflect actual org structure. API access audited. No rogue admin accounts.
Required
BigQuery project set up. Core datasets defined. Naming conventions in place across Drive.
Required
Written. Communicated. Not just a draft in someone's personal Drive.
Don't have all of these? We scope them into the engagement. Most clients get from zero to AI-ready in 60–90 days.
We'll audit your current Workspace, data, and identity configuration — and tell you exactly what needs to change before AI implementation makes sense.