If you’re considering AI Office, you’re probably wondering what it actually looks like. Not the marketing — the actual work.
This is the honest version. Drawn from real engagements, with details changed to protect client confidentiality.
Day 1: The kickoff
9:00 AM. Your AI Office strategist (let’s call her Sarah) and a build engineer (let’s call him Mike) arrive at your office. You’ve cleared 90 minutes for kickoff.
Sarah opens with introductions and the agenda. No slides. She wants to hear your business in your own words.
For 30 minutes, you walk through the company — what you do, who you serve, where the friction is, what’s keeping you up at night. Sarah asks operator-level questions: who’s the bottleneck on quoting? How much of your team’s time is spent on document handling? What’s the customer complaint you hear three times a month?
Mike listens. He’s mostly thinking about which systems will need to talk to each other.
For the last 30 minutes, you cover housekeeping: who’s the executive sponsor (you), who’s the champion (your COO), who’ll be on the working team. You give Sarah read-only access to your CRM and accounting system. Mike gets a walk-through of your file shares.
You leave the meeting feeling like Frogslayer understands your business better than you’d expect from a 90-minute conversation.
That afternoon: Sarah and Mike spend 4 hours reviewing what they learned. By the end of the day, they have a draft inventory of 12 candidate AI use cases and an initial sense of which ones are real.
Days 2–9: The roadmap
For the next week, Sarah works async. She talks to your COO twice. She reviews your systems. She drafts the roadmap.
What she delivers in week 2 is a 12-page document with:
- A 90-day priority (one specific build)
- A 6-month build queue (5 builds)
- A 12-month vision (the bigger picture)
- An ROI projection for each priority item
- A risk and dependency analysis
The 90-day priority she’s recommending is quote acceleration. She’s heard you say multiple times that quoting takes 4–6 days and you’re losing deals because of it. She’s modeled it: AI-assisted quoting could cut turnaround to under 24 hours, and an 8-point win-rate improvement would be worth roughly $1.2M/year to your company.
You have a 60-minute review meeting with Sarah on day 9. You push back on two things. She updates the roadmap. You both agree to start on quote acceleration in week 3.
Days 10–14: The first working session
Wednesday morning, week 3. Sarah and Mike are back in your office.
Today’s session: dive into your quoting workflow with the team that does it. You’ve assigned your sales operations director and one quote analyst.
For 90 minutes, your team walks through how a quote actually happens. Mike takes notes. He’s mapping where the data lives, where humans add value, where the process breaks.
By lunch, Mike has a sketch of the AI workflow:
- Inbound RFQ arrives via email
- AI parses the scope and flags missing info
- AI pulls historical pricing from your past quotes on similar jobs
- AI drafts the quote in your standard format
- Quote goes to a human review queue with margin and risk flags
- Human approves or revises, sends to customer
Mike says he can have a working prototype in 2 weeks. Sarah confirms with you that this is the right scope to start.
You spend an hour after they leave thinking about what other parts of the business this approach applies to.
Days 15–28: The build
This is mostly Mike’s two weeks, with Sarah checking in weekly.
Mike works mostly from Frogslayer’s office in College Station. He uses your test data (anonymized historical RFQs and quotes you provided). He builds the workflow using Claude as the core AI engine, with custom logic for your specific quote format and pricing rules.
He pings your sales ops director on Slack twice with questions. Each exchange takes 15 minutes.
By day 25, he has a working prototype. He shares a Loom video walking through it.
On day 28, Mike and Sarah are back in your office for a 90-minute working demo. Your sales ops director and quote analyst run the workflow on three real RFQs. Two work cleanly. One catches a real edge case Mike hadn’t anticipated.
You’re impressed. So is your team.
Days 29–60: The refinement
Mike fixes the edge case in week 5. He adds a few features your team requested in the demo.
By week 7, the workflow is in production. Your team is using it for real quotes. Mike sets up monitoring so any errors get flagged to him directly.
By week 8, your team has run 47 quotes through the system. Average turnaround: 22 hours — down from 4–6 days. Win rate is too early to measure, but anecdotally trending up.
Sarah documents the wins in your monthly briefing.
The ongoing cadence (months 3 onward)
Now the engagement settles into rhythm. Here’s a typical month.
Week 1
- Monthly working session (60–90 min, in person)
- Roadmap review, prioritization for the month
- Mike picks up next build (predictive maintenance for your equipment)
Week 2
- Mike works async on the build
- Sarah on a 30-min call with your CFO about ROI documentation
- Slack messages with your COO about a customer service question
Week 3
- Mid-build check-in (30 min, video)
- Sarah pulls together a draft 6-month update for your board
Week 4
- Build delivered or pushed to next month
- Sarah’s monthly written report sent
- You forward it to your board
Total time you personally spend on AI: 4–6 hours/month. Total AI Office cost: $5,000/month (you’ve moved up to Operator).
What you’re actually paying for
At $5,000/month, you’re not paying for hours. You’re paying for:
1. Senior judgment. Sarah has done this for 30+ companies. She knows what to build and what to skip.
2. Engineering capacity. Mike’s not on your payroll. He doesn’t need a desk. But he ships.
3. Ongoing momentum. AI doesn’t fail because the technology doesn’t work. It fails because there’s nobody driving it. Sarah is your driver.
4. Pattern matching across clients. Sarah knows what worked for the manufacturer in Tyler. She tells you when she thinks your situation will react similarly. She tells you when it won’t.
5. The Frogslayer team behind her. When you ask about something outside Sarah’s expertise, she has 30 colleagues she can pull from.
If you tried to replicate this with a single internal hire, you’d need to spend $300K+ to get someone half as effective. And that one person would have to do strategy, engineering, and ongoing operations alone.
Where this goes
By month 6, you’ve shipped 3 small builds inside the retainer. In this illustrative engagement, cumulative ROI documented was $400K against the $30K you’d paid so far. The retainer is built to pay for itself — targeting at least 3× payback — and we stand behind the KPIs we set; a strong year can run well beyond that.
Sarah suggests a Sprint to tackle a bigger build: unifying reporting across your three operating divisions. She estimates 6 weeks, $60K, fixed fee, single KPI: cut consolidated-close time from 9 business days to 3. The Sprint carries the 12-month KPI guarantee — if she misses the close-time KPI in a year, the team keeps working at our cost. You agree.
By month 12, you’ve shipped 6 retainer builds plus the Sprint. Cumulative AI investment (retainer + Sprint): ~$120K. In this illustrative engagement, cumulative ROI documented reached $1.4M. Your results will vary — the retainer targets at least 3× payback and we keep working until the KPIs we agreed on move; the Value Sprint additionally puts our fee at risk against its KPI.
In month 13, Sarah proposes a multi-quarter program for year 2: a handful of Value Sprints sequenced in dependency order, bundled around a single business outcome (say, getting cost-to-serve down 15%), plus the hands-on change management to make the new workflows stick. $350K total. Structured as plan / build / manage. You agree, because the year-1 results were undeniable.
You haven’t hired an internal AI lead. You will, eventually. For now, AI Office is doing more than an internal team would.
What’s different about this vs. a project
A project would have:
- Cost you $150–250K up front
- Taken 6 months to scope and contract
- Ended after 12 weeks with a deliverable
- Required you to figure out who maintains it
- Left you wondering what to do next
AI Office:
- Started 2 weeks after signing
- Cost ~$30K in year 1 for the retainer at Sherpa (plus the Sprint, by choice)
- Shipped working solutions every 60–90 days
- Ongoing — you don’t worry about who maintains the work
- Always thinking about what’s next
That’s the whole point of the model.
See if your business looks similar
If you want a 30-minute conversation about whether AI Office makes sense for you, start a conversation. We’ll tell you honestly whether the model fits — or whether something else would serve you better.