AI workspace
We set up the AI tools your team already uses (Claude, Codex, whatever you prefer) to work with your real systems: Slack, QuickBooks, your CRM, your docs. So the answers come from your business, not a generic guess.
The problem
Your team uses ChatGPT for drafts, Claude for summaries, maybe Copilot in a spreadsheet. The AI is impressive in isolation. But it answers from a sandbox. It doesn’t know which retainers are running hot, which deals closed last week, or where last quarter’s engagement notes live.
Every useful question becomes a context-pasting exercise. Copy the spreadsheet in, copy the Slack thread in, copy the invoice PDF in. The AI is doing impressive work on data you spent ten minutes assembling, and you’ll need to do it again tomorrow.
And every person solves it differently. One employee has wired up a couple of connections, another pastes everything by hand, a third gave up. Ten people, ten setups, no shared way of working. The good prompts and shortcuts live in someone’s private chat history, so nothing your team figures out ever compounds.
There’s a better setup. One your whole team plugs into, that already knows your business.
What we set up
Connecting tools is the easy part. The work that earns its keep is the setup everyone shares: the connections to your tools, a map of where your data lives, ready-made shortcuts for the work you repeat, and the rules for how AI should handle your business. Each person connects to the same one, then layers on the access they personally have. One way of working, not ten.
Most teams either connect everything and use nothing, or wire up the impressive-sounding integrations and miss the boring high-ROI ones. We start by mapping which workflows move the needle for your business.
Connect a tool and the AI can reach whatever the person holding it can reach. Without scoping, a junior employee’s AI suddenly has access to executive comp through the CRM. We design role-based access, redaction rules, and audit logs from day one.
The high-leverage piece isn’t ad-hoc chat. It’s repeatable patterns: a morning briefing pulling from CRM, calendar, and Slack. Weekly client status drafts. Project handoff workflows touching the right systems in order. These don’t ship out of the box.
Half your stack probably has no ready-made connector. We build them. Legacy tools, niche industry software, internal systems no vendor will ever support. All become something your AI can reach.
How it works
We map which workflows matter, who needs access to what, and what routines we’ll build first. The AI surface gets chosen (Claude Desktop, Codex, etc.) based on how your team works.
Connectors for your common tools, custom ones for the gaps, data scoping and permissions, and the first batch of routines wired up. We install on your team’s machines or self-host.
Your team learns which questions to ask and how. Runbooks for the common workflows. Ongoing tuning so the setup keeps working as your business and the AI tools both evolve.
Common questions
The shared setup your whole team plugs into: one version of how AI works at your company. Part directory (which tools are connected, where your data lives, what the AI can reach) and part rule book (how it should answer, what it shouldn’t touch). Everyone works from the same playbook, so the AI behaves consistently no matter who’s asking. People still manage their own connections and only reach what they’re allowed to.
Whatever fits your team. Claude Desktop is the common pick, Claude Code for developer-heavy teams, Codex for OpenAI shops. We help you choose based on how your team already works, and the setup isn’t locked to one, so you can switch later.
Setup is on us. Once it’s running, your team uses plain conversation, the same way they’d use any AI assistant. The technical layer stays behind the scenes. Training covers what to ask, not what’s under the hood.
Your data stays in the systems it already lives in. The AI pulls it on demand, it doesn’t copy it somewhere new. We scope access from day one, so a junior employee’s AI can’t reach executive data through a shared connector. Role-based access, redaction rules, and audit logs are part of the build. Model calls run under standard provider terms, with no training on your data.
What an engagement looks like
Your AI tools, connected to your real business data.
$5K–$15K · 2–4 weeks per build
Where this often leads
Setting up your AI often surfaces a bigger gap: work your team does every day that no off-the-shelf tool fits. That’s when we build the custom app for it. Your AI works with it the same way it works with QuickBooks or Slack.
See custom apps