What is your firm bleeding annually because Claude isn't deployed?
Two sliders, fourteen workflows, ninety seconds. Different tasks have different AI offload depths. A status report is mostly Claude. A client meeting is mostly you. Tick what your firm does. The math compounds.
Two sliders, then pick your work.
Every consulting task can be aided by AI to some degree. A status report is mostly Claude. A client meeting is mostly you. The math compounds across what your firm actually does, not generic averages.
Billable team members only. Exclude support staff.
Blended across seniority. Mid-tier strategy or specialty firms typically land $225-$325.
Which work would you hand to Claude?
Tick what applies. Each workflow has its own AI-offload depth. Research synthesis is different from client meetings. The math compounds across what you select.
Show me the work.
One line per workflow you ticked. Every assumption sourced inline. Hover the teal links to see what the number rests on.
This is the floor, not the ceiling.
The math above models the workflows you ticked, at the depth Claude can credibly handle each one. But three architectural advantages of multi-agent Claude don't fit any single workflow. And three more effects emerge over time but resist clean dollar-modeling. All six come up on the call.
Parallelized work
Most consulting work runs linearly because each consultant is one person. With multiple Claude agents you parallelize the inputs. Research agent reads forty discovery PDFs while proposal agent drafts the response while Slack agent triages while meeting-prep agent walks tomorrow's calendar. The bottleneck stops being "how many hours each person has" and starts being "how many things can run at once."
Overnight execution
Your team logs off at 7pm. Agents continue. By 6am the proposal you queued is drafted, this week's discovery transcripts are synthesized, the CRM is updated with notes from Friday's meetings, the deck for Tuesday is in first-draft form. Wake up to work that's seventy percent done.
Computer use across your stack
Claude doesn't just sit in a chat window. It operates the tools you already use: updating Notion pages, posting to Slack, running cells in your model, moving cards in your CRM, drafting in Drive. Removes the "copy from chat, paste into tool" tax that kills most ChatGPT-seat deployments.
Knowledge compounding past year three
The 5-year number above already compounds at 15% / year. Real long-term effect is bigger. Every engagement deepens the firm's institutional brain. Skill libraries get sharper. Past-engagement retrieval gets faster. Year five with Claude is structurally different than year five without.
Partner time re-allocated to BD
When partners stop spending evenings on slide tweaks and proposal drafts, that time goes somewhere. Usually to client development, recruiting, or strategic work that grows the firm. Top-line effect, not in any line item above.
Key-person risk reduction
When your senior partner takes vacation, or leaves, the methodology, frameworks, and client history stay in the firm. Hard to price until the day you need it.
If the calculator above shows a number you'd say yes to, the actual number is meaningfully bigger. Walked through live on the call.
Where the assumptions come from.
Three external anchors plus my own deployments. The 70% number you'll see in other AI ROI calculators isn't here for a reason.
What actually gets built.
Five components. Six-week scope. I do the work, you keep the keys.
Your firm's brain, structured.
Playbooks, decks, past engagements, proposal templates, methodology docs. Indexed. Claude reads everything in your firm's voice.
Inside the tools you use.
Slack, Gmail/Outlook, Drive, Notion or Confluence, your CRM. Claude operates in your team's existing surface area. No new app to log into.
One-click workflows.
Proposals. Slide assembly. Meeting prep. Status reports. RFP responses. Built around the steps your firm actually takes, not generic templates.
Working in parallel.
Research. Proposal. Onboarding. Slack triage. Several agents running across the firm. Some during the day, some overnight.
Compounds across engagements.
Every project deepens the firm's institutional knowledge instead of resetting. New consultants get a senior partner on demand.
You own it.
Documented, handed over, yours. I'll teach your team the one workflow that runs the whole thing. No vendor lock-in.
One operator. Hands on every deployment.
I'm TJ Ryan. I run Cherry Place AI. I built a full Claude system for myself first: knowledge base, multi-agent stack, 50-100 skills running. Recovered 20 hours a week, tripled my output capacity. That's the architecture I deploy for consulting firms.
I'm a solo operator doing the implementation work myself. I take 2-3 consulting clients a month. After that I'm full.
I'm honest about what I don't have yet: a published case study with named consulting clients. The first reference deployment is in flight. Until I publish a partner's number, you're betting on the architecture and on my prior work, not on a logo wall. That's the trade.
If you want to see what gets built before you commit to anything, the call is the call. We'll walk it on screen with your tools, your stack. You'll know in 30 minutes whether this is a fit.
TJ Ryan · tjryan@cherryplaceai.com
The questions every partner asks first.
Five answers that usually come up in the first ten minutes of the call. Here in advance so the call can use those minutes for your actual situation.
Why not just buy ChatGPT seats for everyone and call it AI strategy?+
Because seats are tools, not deployments. A consultant with a ChatGPT seat opens a chat window, types a prompt, copies the answer back into Slack or Notion. The friction is enormous and the firm's knowledge stays in scattered chat logs that no one reads twice.
A Claude deployment puts agents inside your tools: drafting in Drive, posting to Slack, updating your CRM, reading your firm's playbooks as a knowledge base. The architecture difference is what makes parallelization, overnight execution, and computer use possible. None of those work with a seat.
My team won't adopt it. We barely got them to use the new CRM.+
Adoption is the failure mode of every "AI rollout" project. The way around it is to not make adoption the user's job. The deployment lives where your team already works (Slack, Gmail, Drive) and the agents push outputs to them, not the other way around.
The first thing your team learns is one workflow: how to ask. Everything else runs in the background. We measure adoption by usage in week three, and adjust the deployment to where the friction actually is.
What about client confidentiality and our IP?+
Claude (Anthropic) does not train on your data when used through the API or the work-tier products. Your firm's knowledge base, client materials, and engagement notes stay in your own storage (Google Workspace, Microsoft 365, Notion, your CRM) and Claude reads from them on request via integrations you control.
If you have specific compliance requirements (SOC 2, HIPAA, regulated industries), we scope those into the deployment design before any client data touches the system.
What if the math is right but we still don't see the value?+
The Conservative scenario in the calculator above is what I'd defend if I had to scale it back twice. If the Conservative number doesn't pencil out for your firm, the call will likely end with me telling you that. I'd rather not deploy than deploy something that doesn't justify the spend.
If we do deploy, the first month is heavy on instrumentation. We measure what's actually getting recovered against the model. If the gap is more than 30% in either direction, we adjust scope.
How long does this actually take to ship?+
Six to eight weeks for the full deployment. Week one is discovery: your tools, your workflows, your knowledge base. Weeks two through five build the integrations, the skill library, and the first three agents. Week six is your team learning the one workflow that runs everything. After that, you own it.
You're solo. What happens if you get hit by a bus?+
Fair question. Three things mitigate it. First, the deployment is yours from day one. Code, knowledge base, agent definitions, integration credentials all live in your stack, not mine. If I disappear tomorrow, your team keeps running it.
Second, every deployment ships with a written runbook covering how each agent works, how to update skills, how to add new ones. Designed so a competent ops person on your side can extend it without me.
Third, I'm not building toward an exit. I'm building toward being the deep implementer for consulting firms across the next two years. Continuity is a strategic priority, not a hope.
Can I see a deployment running before I commit?+
Yes. The walkthrough is exactly that. We screen-share my own Claude system, which is the same architecture I'd build for your firm. You see real agents running on my real work: research synthesis from yesterday, a proposal draft in flight, the Slack agent triaging what's hit my channels overnight.
It's not a polished demo. It's the actual system I run my company on. Comparable to the one you'd ship.
Walk through the deployment for your firm.
Thirty minutes on screen. Your tools, your stack. No deck, no demo loop, no recording-pretending-to-be-a-product. We'll scope what would actually ship and what it would change.
Want this breakdown emailed?
One pager. Your firm's number, the math, the deployment scope. Forwardable to a partner. I won't add you to a sequence. Single email, that's it.