No More
AI agents finally work. Here's how to put them to work safely, starting Monday.
Jeff Knauss · CEO & Co-Founder, Arcovo AI
Walk up, let the title sit. Then click into My journey for the quick intro.
My journey
Serial entrepreneur, public board director, angel investor & family foundation
Six clicks, one milestone each. Keep it under a minute; the line fills as you go.
Click 1–3: first-time founder in 2014, Inc. 5000 in 2018, exit in 2021.
Click 4: serial entrepreneur, public board director, angel investor and family foundation.
Click 5: November 2022, ChatGPT comes out and you get AI obsessed.
Click 6: November 2024, Arcovo AI. 300+ AI employee implementations since.
"I want to start with a story about the smartest people in the room getting it completely wrong."
1980
AT&T commissions a forecast: how many Americans will carry a cell phone by the year 2000?
Forecast 0
A $4,000 brick with a 30-minute battery, when there's already a phone on the kitchen wall. A niche product.
Actual 0
Off by more than 100×
AT&T believes it and passes. Its own Bell Labs invented cellular.
A cable-TV entrepreneur, Craig McCaw, buys up licenses and builds the biggest cell network in America.
1994: AT&T pays $11.5 billion to buy McCaw and get back into the market it said would never happen.
$4,000
2 lbs · 30 min battery
The opener. Tell it as a story, slowly. Five clicks. Blockbuster and Netflix follow.
Start: "1980. AT&T, the most sophisticated phone company on earth, hires a top consulting firm to answer one question: how many Americans will carry a cell phone by 2000?"Click 1: "900,000." Their logic was reasonable : a two-pound, $4,000 brick with a 30-minute battery, when there's already a perfectly good phone on the kitchen wall.Click 2: "The actual number: about 109 million. Off by more than 100x."Click 3: "AT&T believed it and walked away. The crazy part: their own Bell Labs invented the technology." (Background, if asked: at the 1984 breakup the cellular rights went to the regional Baby Bells, and AT&T didn't fight for them.)Click 4: "Meanwhile a cable-TV guy named Craig McCaw buys up licenses all over the country and builds the biggest cell network in America."Click 5: "1994: AT&T writes an $11.5 billion check to buy McCaw and get back into the market it said would never happen.""They had the technology. They had the head start. They asked people what they wanted, and nobody could picture a phone in their pocket. And it happened again with something you all remember."
Friday night, 2004
Photo only, no builds. You tell the facts out loud.
"Remember Friday night? You got in the car, drove to Blockbuster, and walked the New Releases wall hoping there was one copy left."
"Then you had to bring it back by Tuesday, or pay the late fee. Blockbuster made something like $800 million a year on late fees. Their business model was you forgetting."
"In 2000, Netflix offered to sell the whole company to Blockbuster for $50 million. Blockbuster passed." (Per Netflix co-founder Marc Randolph.)
"Nine thousand stores at the peak in 2004. Bankrupt by 2010." "Same story as AT&T. Nobody was asking for movies over the internet. So what did Friday night turn into?"
Friday night, today
Photo only, no builds. Let it land; it'll get a laugh.
"Friday night today. Same movie. You didn't get in the car. You didn't even put pants on."
"Every movie ever made, from bed, no late fees. Nobody in 2000 was asking for this. Nobody could picture it. Convenience always wins."
"Your business software is about to have its Blockbuster moment. Here's what I mean."
Where this is going
Headless software.
Today: the phone on the kitchen wall Walk to the system. Click. Filter. Export. Repeat.
0 clicks
Accounts Filter Deals Notes Email search Calendar Export CSV Pivot table Ask Sharon
Tomorrow: the phone in your pocket Ask. The agent goes and gets it.
Where do we stand with Acme, and when do we meet next?
They signed Tuesday and kick off Monday. Their COO already wants a second AI employee in Q1. Next meeting: Thursday at 2:00.
Agents become the interface. Your data is the value.
Click 1 runs the counter to 200. Click 2 shows the agent. Click 3 the line.
"Your CRM is the phone on the kitchen wall. You walk over to it and click 200 times to get one answer. An AI agent is the phone in your pocket. You ask the question and it goes and gets the answer."
Headless software: this is where it's all going. The screens, the buttons, the menus (the "head") stop mattering. Agents become the interface. What's valuable is the system underneath: your data.
Convenience always wins. Nobody asks for this until they've used it. After that, nobody goes back.
Anthropic S-1 filing
This is what demand looks like.
One quarter of 2026 = 2.5× all of 2025
Q2 '26 vs Q2 '25: ~15×
All of 2025: $4.6B → first half of 2026: $16.3B
Starts with only 2025. Let them read it as healthy growth: $4.6B in a year.
Click 1: Q1 2026 arrives. Axis rescales; 2025 shrinks.
Click 2: Q2 2026: $11.6B. 2025 collapses to slivers. Pause. Let it land.
Click 3: the callouts.
"One quarter of 2026 brought in two and a half times all of 2025. That isn't hype. Those are invoices. Businesses paying for something that works."
Who's paying? Businesses.
0 %
of revenue is usage-based: companies running AI inside their workflows , not people chatting.
Anthropic S-1, H1 2026
1,500
up from 1,500 in December
companies spending $100K+ a year . That's 8× in nine months.
Anthropic S-1
10%
88% of companies use AI
Only about 1 in 10 have made it pay off at scale. We are still early.
Deloitte, State of AI in the Enterprise 2026
The companies that figured it out are spending like it's an arms race. Everyone else is still deciding. That gap is your window.
Close the loop on the chart. Three clicks, three stats, then the line.
80%: this is businesses putting AI to work, not consumers playing.
1,500 → 11,900 big spenders in nine months.
But 88% "use" AI and only ~10% have made it pay. Still early. You can still win. "That gap is your window. And it's still open."
PART ONE
Accounts Payable.
One invoice. Two eras.
Accounts payable
The life of one vendor invoice.
Northside Packaging Co. Syracuse, NY
INVOICE
Invoice #4471 PO-2210 Net 30
Item Qty Price Corrugated boxes, 18×18 100 $4.20 Pallet wrap 12 $31.00 Freight 1 $185.00
Total $977.00
1 Vendor emails a PDF to ap@
2 The AP clerk checks it against the PO
3 Doesn't match? Emails the vendor and waits
4 Chases an approver
5 Enters it into QuickBooks
6 Schedules the payment and files it
Every company in this room does this hundreds of times a month.
Click 1 walks the six steps in. Click 2 the line.
Step 3 is the one everyone feels: the invoice doesn't match the PO, so the AP clerk emails the vendor and the invoice sits until they answer.
Keep it relatable: "Somebody in your building is doing this right now."
The footnote cost stat comes from the report sponsor's summary (the full report is paywalled). Don't lean on it.
2015 · Robotic process automation
Enter the bot.
Open emailInbox ap@
Read fieldsOCR + a template for every vendor
Match the POExact match only
Enter in QuickBooksField by field
Queue the paymentReady to pay
Works 24/7Never sleeps
Never makes a typoSame keystrokes every time
Paid for itselfAs long as nothing changed
RPA started the automation age. But it could only follow orders.
The bot walks the five steps on a loop. Explain why RPA was a big deal: 24/7, no typos, real ROI on stable, high-volume work.
Accurate framing: RPA could do all of this, but reading invoices needed OCR plus a template set up for each vendor, and PO matching was exact rules only. Anything outside the template went to an exceptions queue for a person.
"Step one, step two, step three. Exactly the same, every time. Which is great, right up until the world changes."
Then reality showed up.
Monday
The vendor redesigns their invoice
Total moved from the bottom-right to the top-left
BOT STOPPED
Tuesday
PO says 100 boxes. 96 shipped.
100 ≠ 96
KICKED TO A HUMAN
Wednesday
The invoice arrives as a phone photo
IMG_4471.jpg (sideways, with a thumb)
BOT STOPPED
Thursday
A handwritten note: "Net 45 per Dave"
Net 45 per Dave
IGNORED IT
Friday
"Please update our bank details"
New routing #, sent from a look-alike domain
CAN'T SPOT FRAUD
Every exception = a ticket, a developer and a restart.
Five clicks, Monday through Friday. Have fun with these.
Tuesday: exact-match rules can't decide, so it lands in an exceptions queue for a person. Thursday: the bot reads fields, not notes, so it just ignores it and pays on the wrong terms.
Friday is the security hook: a bot can't tell a real bank-change request from a fraudulent one. If it's built to process vendor updates, it processes this one too. Business email compromise is this room's world.
"RPA follows orders. It can't think."
Same invoice. Now with an AI agent.
Monday
New invoice layout
Agent Reads any format. No template needed.
Tuesday
100 ordered, 96 shipped
Agent Approves 96, holds 4, drafts the email to the vendor.
Wednesday
Phone photo
Agent Reads the photo like it's a PDF.
Thursday
"Net 45 per Dave"
Agent Checks the contract, flags the change, routes to a human.
Friday
"Update our bank details"
Agent Flags possible fraud. Requires a callback before anything changes.
When it isn't sure, it asks a person.
Same five days, now each one resolves in green.
"It doesn't follow steps. It understands what you were trying to accomplish."
Friday: the agent is now a security control , not a security risk.
Why everyone is talking about this now.
RPA (2015) AI agent (today) How it works Follows steps Understands the goal When something changes Breaks Adapts When it's stuck Needs a developer Escalates, with a reason
RPA could only automate perfect processes. There aren't many.Agents handle the messy majority.
The thesis of the talk. RPA could only do the perfect, predictable 20%. Agents can take on the messy work that used to need human judgment.
"That's why everyone is suddenly talking about this. The robots stopped breaking."
Same pattern. Everywhere.
Sales Inbound lead response Every lead answered in minutes, not days
Sales Lead research & enrichment Prospects researched before the first call
Sales Proposal generation Deal context in, ready-to-send proposal out
Sales Quotes & estimates Built from your line items, job history and vendor pricing
Clients Client onboarding Contract signed, systems populated, kickoff ready
Clients Client reporting Updates assembled from your systems, on your cadence
Support Ticket triage Every request sorted, routed and started
People Employee onboarding HR, IT and facilities coordinated; certifications tracked
Finance AP & AR Invoices processed, overdue accounts chased, finance approves
Finance Invoicing Billing reconciled, invoices drafted for review
Finance Job costing & profitability What every job actually made
Leadership Data visualization CRM, ERP and spreadsheets turned into live dashboards
90 seconds, rapid fire. Click 1 brings in the tiles. Don't read them all: pick three that fit the room.
These are the solutions on arcovo.ai. Good ones for this audience: inbound lead response, ticket triage, employee onboarding (access provisioning), AP & AR.
"Same pattern as the invoice: messy inputs, judgment calls, a person approving what matters. It shows up in every department."
Why it matters
One deal. Two salespeople.
Day 0
Lead to onboarded · illustrative
Without AI Onboarded · Day 45
Speed Consistency Time to value Time to revenue
The customer gets more, faster. The salesperson gets to focus on the relationship.
The "why" slide. Click 1 starts the race (about 10 seconds; talk over it). Click 2 the payoff.
Without AI: the lead sits until tomorrow, four emails to find a time, fifteen minutes of Googling, a recap that may never get sent, a week building slides, another round of scheduling, contracts drafted by hand, then weeks of onboarding forms.With AI: an agent replies in minutes with a booking link. The moment it's booked, an agent researches the company and the person and fills it all into the CRM, so the salesperson walks in knowing the business and how they can help.Right after the call the transcript goes to the agents: a recap email with next steps goes out automatically, and an agent builds a full proposal as a website, not a static PowerPoint, that deeply understands the client. The next meeting was booked before the first call ended.
Client loves it. Agents generate the MSA and SOW the same day. They sign, and the onboarding agent gathers the data and billing info automatically.
"Same salesperson. Same talent. One spends their time on data entry, slides and paperwork. The other spends it on the relationship. Faster time to value for the customer, faster time to revenue for you. Everybody wins."
Purple "You" rows are the human steps. Everything else is an agent. Day counts are illustrative.
PART TWO
Data is the food
Headline slide. Say it, pause, move on.
"Data is the food for AI. And I hear one excuse more than any other..."
"My data isn't ready."
A messy pantry is fine. Spoiled food is not.
✓ Messy is fine
Files in the wrong folder No naming convention 14 versions of the price list Answers buried in email threads ✗ Wrong is not
Prices that are out of date Records that were never entered Contacts who left in 2019 Two systems that disagree
Agents don't need tidy. They need it to exist , and they need it to be right.
"Sometimes that's true. Usually it's an excuse to bury your head in the sand."
Agents don't need folder names or naming conventions the way humans do. If you know where the data lives, they'll find it. What hurts them is data that's wrong or missing: agents only know what they're fed.
"Things don't have to be perfect for you to start. You just need to start."
Where does the data live?
YOUR ENVIRONMENT
Email
QuickBooks
PO system
Teams
Agent
⇄ Encrypted in transit
■ Encrypted at rest
⚿ Credentials in a vault, not a script
☰ Every action logged
✋ A human approves money
✗ Providers don't train on your data
The data stays home. The agent visits.
Click 1: the flows animate and the line appears. Click 2: the controls.
The data stays in your systems. The agent goes and gets what it needs, analyzes it and puts the result back.
Run through the controls quickly: this room knows these words. Business/API terms from the model providers don't train on your data.
Confirm with Michael before the talk that every one of these is exactly how we say it.
Treat an agent like a new hire.
New AP clerk AI agent Identity A login = Its own account, never a shared one Role Only what the job needs = Least privilege Training SOPs = Specific instructions Supervision A manager = Human approval on what matters Accountability A paper trail = Every action logged Offboarding Turn it off = Turn it off
Headlines: Agents running loose and causing chaos!
The reassurance slide. Train it once: it doesn't forget, doesn't leave.
"You already know how to do this. You do it for every new hire."
Click 7: the headline. "When you see the headlines about agents running loose and causing chaos, like the coding agent that deleted a company's live database during a code freeze in July 2025, it's because nobody did these six things. Broad access and a vague goal."
New York, 1945
Automatic elevators existed for decades.
Nobody would ride one without an operator.
Then the operators went on strike, and the city stalled.
So the industry designed trust in:
An emergency stop button An emergency phone A calm, recorded voice Today, an elevator operator would feel strange. Trust is designed.
▲ 12
Short story, about 60 seconds. On the last click the doors open.
"Agents are the same. You don't get trust by hoping. You design it in."
Human in the loop
Agents do the work. Humans make the calls.
Let the agents take
The repetitive The mundane Data entry and matching Chasing and follow-ups First drafts Keep the humans on
The final decision Critical thinking Relationships Judgment on the exceptions Accountability
Agent prepares → Human decides → Agent executes
Lean into the uniquely human work. That's where your people win.
The most important idea in the security section. Least privilege was the start; this is the principle behind it.
Click 1: let the agents take the automated, repetitive, mundane work.Click 2: humans still make the final decisions. They still do the critical thinking. They still build the relationships.Click 3: the loop: the agent prepares, a human decides, the agent executes.Click 4: the line."This isn't about replacing your people. It's about letting them stop doing robot work so they can do the human stuff. That's the part no agent can do, and it's where your business actually wins."
PART THREE
Three waysto work.
Most companies are picking one without realizing it.
"So you've got your data ready and your guardrails in place. Now, how do you actually bring AI into the business? Most companies are doing it one of three ways, and most of them don't realize they've picked one."
1 · Point solutions 2 · Environmental AI 3 · Orchestration you own
Point solutions: one tool per problem.
× Solves the same problem for everyone, the same way
× Doesn't fit how you actually do the work
× 40 vendors with access to your data
Fast to start. Hard to scale.
Three clicks: the tools, the problems, the verdict.
"The first way is point solutions. One tool that solves one problem. A tool for invoicing. A tool for proposals. A tool for scheduling."
Click 1: "Do that across a business and you end up with 40 different vendors inside your data. And to find the best 40, you probably talked to 160."
Click 2: "The bigger problem: a point solution solves the same problem for everyone, the same way. It doesn't fit exactly how you do that workflow. You bend your process to the tool."
Click 3: "It's fast to start. It's very hard to scale."
1 · Point solutions 2 · Environmental AI 3 · Orchestration you own
Environmental AI: agents inside your software.
Microsoft Copilot Salesforce Agentforce HubSpot ✓ Works well if your data lives there
Inside your CRM
Lead follow-up agent
Quote builder agent
Renewal reminder agent
🔒 Their model your only option
Switch CRMs. Lose it all.
Problem 1
Your workflows live in their software. Change your CRM, ERP or project system and every workflow you built goes with it. Problem 2
You can't change the model. Models keep moving in price and quality. You're stuck with the one they picked, at the price they set.
Three clicks.
"The second way is what I call environmental AI. Copilot agents in Microsoft. Agentforce in Salesforce. The agents in HubSpot. It can work well, if your data lives in the environment those agents are built in."
Click 1: "So you build your workflows right inside your CRM."
Click 2: "Problem one. If you ever want to move off that CRM, or change your ERP or your project management system, and you built all your workflows inside of it, they go when your software goes."
Click 3: "Problem two is model optimization. Models go up and down in price, and tokens are getting more expensive. You'll want to choose which model powers each agent. With environmental agents, you don't get that choice."
1 · Point solutions 2 · Environmental AI 3 · Orchestration you own
Orchestration you own: agents working together.
Any model Claude GPT Gemini Open source ⇄ swap any time
Your orchestration platform
Agent Reads
→ Agent Checks
→ Agent Acts
= AI employee Your exact workflow
Any system CRM ERP Email Projects Files ⇄ change it, keep it all
✓ Built for your exact workflows , not plug and play
✓ Any model , so you can optimize your token costs
✓ Change your software, lose nothing
This is how we build at Arcovo.
Three clicks.
"The third way: agents working together on an orchestration platform that you own. It's agnostic to the models and to the technology your business runs on."
Click 1: walk the picture top to bottom. "Any model on top: you pick the best one for each job and swap it when prices change. Your agents chain together into an AI employee that runs your exact workflow. And underneath, any system. Change your CRM and you don't lose a thing."
Click 2: "It's built for your exact workflows, not plug and play. You can optimize your tokens. You don't lose anything when you change the underlying software. And it scales with you."
Click 3: "That's how we build at Arcovo." Then go straight into Part Four.
PART FOUR
Arcovo runs on agents.
Everything I just told you to do, we did to ourselves first.
"I'm not going to ask you to do anything we haven't done to ourselves first. Here's how we run Arcovo."
The connected brain.
ArcovoAI agents
Where do we stand with Northside Packaging, what's blocking their build, and when do we meet next?
Status: signed on Oct 21 and starting implementation. Their CFO called it "the easiest yes we've made this year" (kickoff call) .
Blocker: the build is waiting on their QuickBooks admin access, 3 days so far (Jira) .
Next meeting: Thursday, 2:00 PM. Their CFO accepted (calendar) .
Read.ai · 2 calls Email · 4 threads Jira · 1 ticket Calendar CRM
Every system we run on is connected: CRM, email, calendar, Read.ai transcripts of every call, Jira, Slack, Drive, client code and Nova.
Click 1: ask the question; the brain lights up. Click 2: the answer, with sources.
"One question. It reads the calls, the emails, the tickets and the calendar, and answers in under a minute."
(Northside Packaging is fictional, the same vendor from the AP invoice.) CRM is labeled generically since we're moving to our own.
Arcovo OS: how we run the company. Live from HQ (sort of)
⚲ Ask anything ⌘K
Meetings
Critical Numbers
IMP Status Reports
Reporting
Revenue & AR
Forecast Accuracy
Client Work
Consultations
Implementations
Live Clients
Accounts
Jeff Knauss
Critical Numbers Meeting Agenda IMP Status Critical Numbers Issues
Critical number 1
✱ Median Project Cycle Time Ahead of Target
Q4 new starts · weeks from kickoff
Vandelay Industries
3.2 wks Los Pollos Hermanos
2.9 wks
Critical number 2
✱ AI Employees Live in Q4 Comfortably On Track
Weekly Leadership & Ops Meeting Agenda IMP Status Reports Critical Numbers
Data refreshed this morning Synced from the CRM each morning.
✱ IMP Status Review · 4,812 of 4,812 active IMPs reported
Client ASM Stage Status Sentiment Go-live Days AI Emps Live MRR Dunder Mifflin Paper Co.Paper Sales & Beet Farm Ops Pam Live Green Ecstatic Oct 3 41 Ahead 12/12 $41,700 Acme CorpAnvil & Rocket Skate Logistics Wile E. Live Green Ecstatic Oct 7 38 Ahead 9/9 $38,250 Stark IndustriesSuit Inventory & AP Pepper Live Green Ecstatic Oct 9 36 Ahead 21/21 $96,000 Vandelay IndustriesImporting / Exporting George Live Green Ecstatic Oct 12 33 Ahead 4/4 $12,400 InitechTPS Report Automation Milton Live Green Ecstatic Oct 14 30 Ahead 7/7 $22,050 Los Pollos HermanosFranchise Operations Gus Live Green Ecstatic Oct 15 29 Ahead 11/11 $33,800 Bluth CompanyBanana Stand Finance Michael Live Green Ecstatic Oct 18 27 Ahead 3/3 $9,900 Cyberdyne SystemsDefinitely Not Skynet S. Connor Live Green Suspiciously Happy Oct 21 24 Ahead 800/800 $1,000,000
Revenue & AR Overview Forecast Trends Receivables
This Month · 3 Days Left
On pace to beat goal with 3 days left.
Monthly Recurring Revenue
Churned MRR (this quarter)
$0 Top-client share of MRR
Dunder Mifflin 0.0005%
Live Clients Clients Risks 0 Growth Snapshots
✱ Red Accounts · 0 No live client is rated red.
✱ Check-ins Overdue · 0 Every client has heard from us this week.
Consultations AWCs Actions Lists
0
Total open AWCs (it's over 9,000)
Deal BDM Signed Days in Next step AWC: Gringotts Bank JK Oct 26 2 Deep dive with the goblins AWC: Sterling Cooper JK Oct 25 3 Blueprint presentation AWC: Hooli JK Oct 24 4 Kickoff (they want to "make the world a better place")
⚲
Three clients mentioned Q1 expansion budget on calls this month:
• Acme Corp (Oct 14): wants two more AI employees "for the roadrunner project."
• Stark Industries (Oct 19): asked about AP for all 14 subsidiaries.
• Dunder Mifflin (Oct 22): Michael wants a "World's Best Boss" agent. We advised against it.
Drafted follow-up emails for all three. Ready for your review.
Read.ai · 3 calls CRM · 3 deals Email · 5 threads Drafts · 3
Illustrative demo · fictional data
The fun one. Each click, the cursor clicks to the next screen. Keep it moving, about 90 seconds total. The numbers are obviously absurd on purpose: let the room catch it.
Critical Numbers: "Every Monday our leadership meeting runs off this. Nobody builds a slide. It builds itself from our systems." (Median project 3 weeks vs. a 4-week target; 31,337 AI employees live.)Click → IMP Status: "Every project, every week, reported automatically. Sentiment comes from the actual call transcripts." (Cyberdyne: "Definitely Not Skynet," "Suspiciously Happy.")Click → Revenue & AR: "Billing reconciled from our billing systems, so we know where the month lands before it ends." ($57M projected against a $57M goal.)Click → Live Clients: "Health of every client, flagged before it becomes a problem."Click → Consultations: "Our whole pipeline." (Over 9,000.)Click → Ask anything: the real point. Type a question in plain English, get an answer across every call, email and deal, plus drafts ready to send.
"I'll be honest: some of these numbers are slightly aspirational."
What the brain does for us. Every day.
1
Discovery follow-ups Drafted from the call transcript, in my voice, within the hour.
After every call
2
Kickoff emails Drafted the moment a client pays, built from what they told us.
On payment
3
Cash forecast 13 weeks out, rebuilt from the books and emailed to leadership.
Mon & Fri
4
Deal intelligence Who they are, what they've said, what to ask, before every discovery call.
Before every call
5
Support tickets From the client's email to shipped code. In minutes.
Next slide →
Support tickets
Days or weeks → minutes.
01 · CLIENT Emails support "Can the weekly report include Region 4?"
02 · AGENT Reads the request Understands what's being asked
03 · AGENT Pulls the project history Jira tickets, call transcripts
04 · AGENT Reads the code Finds where the change goes
05 · AGENT Makes the change Opens it for review
06 · HUMAN ENGINEER Reviews & approves Nothing ships without this
07 · AGENT Ships it Change goes live
08 · AGENT Emails the client "Done. Region 4 is in this week's report."
Before Days or weeks
Now Minutes. With a human in the loop.
Click 1 runs the whole flow with the clock ticking. Click 2 before/after.
Walk it: the client emails in, the agent reads the request, digs into the project, reads the code, makes the change and pushes it for review. A human engineer accepts it, it ships, and the agent emails the client that it's done.
Point at step 6: that's the guardrails slide in action. The agent can propose; a human approves.
Client story · scDataCom · The problem
Salespeople buried in admin.
A security systems integrator selling to federal and commercial customers.
A salesperson's day
Admin & data entrymore than half
Selling
Hunting bids Searching SAM.gov for federal opportunities by hand
Logging deals Typing every opportunity and email into the CRM
Building quotes Pulling specs out of bid documents line by line
Writing proposals About 8 hours for every federal proposal
One click. About 25 seconds. Set up the pain, then go straight to the next slide.
"scDataCom integrates security systems for federal and commercial customers. When we met them, their salespeople were spending more than half their day on admin. Hunting for bids, typing into the CRM, building quotes, and about eight hours on every federal proposal. Everything except selling."
Sources: "more than half" of account managers' time on admin (scDataCom discovery and blueprint); ~8 hours per federal proposal (Aug 14, 2026 check-in, Read.ai).
Client story · scDataCom · The solution
Five AI employees joined the sales team.
Samantha Finds federal bids
Dexter Pulls data from documents
Quinn Builds the quote
Preston Drafts the proposal
Olivia Keeps every deal updated
Federal opportunities found and logged by AI
0
≈ 800 hours of manual searching. Twenty work-weeks.
3.5 hrs
back in every salesperson's day
$6,000+
a month in staff time saved
+20%
more qualified opportunities
25+
companies they've referred to us
“
I was a bit of an AI naysayer, and then Arcovo just blew my socks off.
Kathleen Ford, CEO, scDataCom
Two clicks: the numbers, then Kathleen's quote. About 45 seconds.
"So we built them a team. Samantha finds the federal bids. Dexter pulls the data out of the documents. Quinn builds the quote. Preston drafts the proposal. Olivia keeps every deal updated."
Click 1: "Over 3,200 federal opportunities found and logged. That's about 800 hours of searching nobody had to do. Three and a half hours back in every salesperson's day. More than six thousand dollars a month in staff time. Twenty percent more qualified opportunities."
Click 2: "And here's what the CEO said." Read the quote. "She's referred us more than 25 companies since." (Kathleen also introduced us to Ollivier, the next story.)
Sources: 3,201 opportunities / ~800 hours (Chris, Slack Jun 29, 2026; Arcovo's calculation). 3.5 hours (scDataCom testimonial video, Jul 2026). $6,000+/month = Olivia ~$3,000/month (Alaina Meyer, Aug 14 call; she said the real total is "much higher") + Samantha ~$2,900/month direct labor (Alaina, Oct 23 call). Dexter (~17 hrs/month) and Quinn (15–30 hrs/month) come on top and aren't in the dollar figure. +20% qualified opportunities year over year (Aug 14 call). Quote: arcovo.ai/results. Check: 25+ referrals is your number; the Aug QBR sheet listed 6 named referrals.
Client story · Ollivier · The problem
Their clients were flying blind.
Ollivier provides security guards and runs security systems for businesses.
Guards
Patrol logs
Shift reports
Incident write-ups
×
Systems too old to talk to each other Security systems
Cameras
Access control
Alarms
"Were all my patrols covered last night?"
→
Someone has to go dig through the logs .
One click. About 25 seconds.
"Ollivier is a managed security company. They put guards on site and they run the cameras, access control and alarms. Their clients pay a fortune for both, and have no single way to see either. Ask a simple question, like were my patrols covered last night, and somebody has to go dig. Everyone in the industry told Louis, their CEO, that one screen for all of it couldn't be done. The systems are too old to talk to each other."
Source: Louis Boulgarides testimonial recording, Oct 19, 2026 (Read.ai): "single pane of glass," "never existed in the security industry."
Client story · Ollivier · The solution
One screen for guards and security systems.
Day 1 Vision on a call
Day 30 Live demo at their trade show
Day 90 In clients' hands
✓ Every milestone hit
1 Pulls guard and security-system data into one secure client portal
2 Clients ask questions in plain English and get answers in seconds
3 Maps patrol coverage and flags the gaps
4 Writes every client's monthly report
Anything sensitive goes to an Ollivier staff member.
Client security portal
98% Patrol coverage
142/142 Cameras online
3 Incidents this week
Were all patrols covered last night?
14 of 14 rounds completed. One late check at Gate B, 11:52 PM.
Illustrative
“
This is by far the best experience I've ever had with a technology deployment.
Louis Boulgarides, President & CEO, Ollivier
Two clicks. This one lands with a security crowd.
"Louis described it on a call. Thirty days later we had a working demo at their trade show. In ninety days it was in his clients' hands. Every milestone hit."
Click 1: "Here's what it does. It pulls the guard data and the security-system data into one secure portal. A client can just ask: were all my patrols covered last night? And get the answer in seconds. It maps patrol coverage and flags the gaps. It writes every client's monthly report. And anything sensitive goes to a person. He showed it to five different people on his phone at a football game."
Click 2: read the quote, then: "He also told us, 'If you can imagine it, you can do it.'"
Sources: Louis's testimonial recording, Oct 19, 2026 (Read.ai): 30-day trade-show demo, ~90-day product, "hit every single one of those milestones," "never went dark," "secure, multi-factor authenticated," demoed five times at a game. What it does: Ollivier status reports (chat Q&A on client security data, monthly reports, patrol-coverage gap mapping, escalation to staff). The dashboard is an illustration with made-up numbers, not their product.
Client story · Precision Walls / AES · The problem
Every bid started from scratch.
AES, Precision Walls' exterior division, bids the outside of commercial buildings.
283
pages in one drawing set
By hand, for every bid
1 Read every page to find what's in scope
2 Measure square footage and linear feet
3 Price materials across three suppliers' spreadsheets
4 Estimate labor and build the proposal
20–30 hrs
of estimator time per bid
2–3 days
to get one proposal out the door
One click. About 25 seconds. Contractors in the room will feel this one.
"AES does the exterior skin on commercial buildings. Every bid starts with a stack of drawings. This one was 283 pages. An estimator reads every page to find what's theirs, measures it, prices it across three suppliers, estimates the labor and builds the proposal. Twenty to thirty hours. Two to three days to get one bid out the door."
Sources: 20–30 hours and 2–3 days are AES's own words (Matt Chance, Jun 4, 2026 deep dive, Read.ai). 283-page set from Jake's testing (Slack, Jun 19). Not on the testimonial list: get permission to name them, or say "a commercial construction firm."
Client story · Precision Walls / AES · The solution
An AI estimator that does the first draft.
283 → 283 pages
It reads the whole set and pulls the 7 pages that matter .
1 Finds the scope and flags every in-scope product
2 Measures square footage and linear feet, by area
3 Estimates the labor and prices materials across three suppliers
4 Drafts the proposal PDF
★ In testing, its labor estimate came within about 5% of their own estimator's
✓ The estimator reviews and approves every stage
Estimators stop starting from scratch. They review, approve and bid more jobs with the same team.
Two clicks. The most visual story.
Click 1: watch the counter. "That 283-page set? The AI finds the seven pages that matter and flags exactly what's in their scope. It measures the square footage and linear feet, estimates the labor, prices it across their three suppliers and drafts the proposal. In testing, its labor estimate came within about five percent of their own estimator's. And the estimator approves every stage."
Click 2: "So the estimator stops starting from scratch. More bids, same team."
Be accurate: 283 → 7 pages is from Jake's testing (Slack, Jun 19). The ~5% labor-estimate result is from the Oct 15, 2026 playback, in testing. This build is in UAT, not live, and on some drawings the estimator still does the measuring. Don't quote a time saved until AES measures one. If someone asks "why not perfect?": it's a first draft, and the estimator signs off on every number.
Start Monday.
1 Pick one painful, repetitive process. Getting the right use case is critical to success and return on investment.
2 Write down where its data lives. And who owns access to it.
3 Pilot it read-only, with a human approving. Measure it for 30 days.
arcovo.ai · jeff@arcovo.ai
The tactical takeaway. Three steps anyone can start next week.
Add the CTA here (checklist, AWC booking, attendee offer) once decided.
In 1980, nobody needed a phone in their pocket.Today, nobody needs to click 200 times.
No more waiting.
Jeff Knauss · CEO & Co-Founder, Arcovo AI · jeff@arcovo.ai · arcovo.ai
Callback to the opener. Click for the final line. Thank them, and point to the panels that follow.
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