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Home / Solutions / AI Operating Standards

AI Operating Standards

Your teams already use AI. Make it one system.

In four weeks, every team works from the same standards, the same context and the same guardrails.

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One standardAI operating standardv3, every team
  • Approved tools only
  • Client data stays in
  • Reviewed before it leaves
  • Prompts shared, not lost
ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexity
SalesOn standard!Personal accountBrought inNot used!Personal accountBrought in
OperationsOn standardNot usedNot usedOn standard
Marketing!Personal accountBrought inOn standard!Personal accountBrought inNot used
SupportOn standardNot usedOn standard!Personal accountBrought in
Works withand the rest of your stack

The problem

Sound familiar?

Every team picked its own tools, prompts and vibe-coded workflows. It works today. In 12 to 18 months it becomes tech debt nobody owns. We inventory what's in use, set the standards, build the shared context layer, and train your people to build on it.

  • Different teams use different models with no shared rules for data
  • Useful workflows live in one person's account and break when they leave
  • Nobody can say what AI is touching, or what it costs
  • Leadership wants AI adoption to speed up without losing control

What we build

The workflows we put into production.

01

AI usage inventory

Every tool, workflow and data connection in use across the company, mapped with its owner and risk.

02

Architecture standard

Model-agnostic patterns for where context lives, how data is accessed and what gets approved.

03

Shared context layer

Your company knowledge written down once, so every team and every model works from the same source.

04

Team enablement

Hands-on training so your people can build on the standard without waiting for engineering.

What you get

The AI Handbook

A living document your company owns: the approved tools and models, data rules, build patterns, review steps and owners. Paired with the shared context layer and a trained group of builders inside each team.

  1. 1AI usage inventory and risk map
  2. 2Architecture and data standard
  3. 3Shared context layer, in your accounts
  4. 4AI Handbook and approval workflow
  5. 5Builder training for each team

How it runs

Diagnose, prove, then run.

01Diagnose2 weeks

We interview the people doing the work, check your systems and data, and value every workflow we find.

02Prove4 to 8 weeks

One workflow, fixed scope, built in your own cloud and tested on your real records before it goes live.

03RunOngoing

An embedded team works down the blueprint while we keep what's live healthy and measured.

The full method

Other practices

Finance OperationsReconciliation, margin and payment checks across your accounting, payroll, billing and supplier systems.See the practice Revenue OperationsAccount research, scoring, routing and follow-up, in production inside Salesforce or HubSpot.See the practice Knowledge OperationsDocuments, proposals and company know-how turned into systems the whole team can use.See the practice

Have a process we should look at?

Two weeks to a ranked roadmap, valued in dollars from your own numbers. You decide what happens next.

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