Commercial Cleaning
You're winning the contracts.You're not keeping the margin.
Your bids are based on square footage and scope. Your costs are driven by which crew shows up and how long they actually take. A 200,000-square-foot office campus should take four hours with a six-person crew. One team finishes in three and a half. Another takes five and pulls supplies from the next job's allocation. The contract price is the same either way. We embed with your team, map every site from bid through nightly execution, and deploy AI specialists that find what your job costing software never breaks down.
The Problem
Where the money
is going.
Cost
Labor Allocation vs. Actual Site Needs
You bid a medical office at 14 labor hours per clean. Your crew logs 18 because the scope expanded after move-in but the contract didn't. Another site is overbid by three hours nightly because nobody recalculated after the tenant downsized. Labor is 60% of your cost structure. The gap between estimated hours and actual hours across 30 sites is where your margin disappears, but nobody reconciles bid assumptions against real crew time until the quarterly P&L shows the damage.
Cost
Supply Waste Across Sites
Your warehouse ships cleaning chemicals, liners, and paper products to each site based on standing orders set when the contract started. The 12th-floor tenant left six months ago but Site 9 still gets supplies for a full building. Meanwhile, Site 3 runs out of floor finish every other week and your supervisor makes a $200 emergency run to the distributor. Nobody adjusts par levels because nobody tracks consumption against actual cleaned square footage.
Process
Quality Inconsistency Between Crews
Your day porter at the downtown tower gets a 98% inspection score. The night crew at the same building averages 71%. Different people, different standards, same contract. Client complaints spike on weekends when the B-team covers. Your operations manager drives between sites doing spot checks, but she can only visit four of your 30 sites per night. The other 26 run on trust until a client calls.
Risk
Contract Margin Erosion
You signed a three-year deal at $0.18 per square foot. Labor costs went up 11% in year two. Chemical costs jumped 8%. The CPI escalator in the contract covers 3%. You're now cleaning the same building at a loss and the client expects the same service level. Nobody modeled the true cost trajectory when the contract was signed because the estimating spreadsheet doesn't pull from actual job cost data on similar buildings.
How We Work
Three steps. Hands on.
We embed with your team, map your operation, find what no one could see, and deploy specialists that fix it. You get a dedicated team, not a login.
Map
We start with structured discovery across every site and shift. Our team interviews operations managers, site supervisors, crew leads, estimators, and account managers. We connect to your job costing platform, scheduling system, supply ordering tools, and inspection software. The result is your Blueprint: a complete, live map of how your cleaning operation actually runs, from bid day through nightly execution. Not the proposal assumptions. The real labor hours, the supply consumption, the inspection gaps that vary from site to site.
Uncover
We analyze everything we mapped. Our platform finds the sites where labor hours exceed bid assumptions by 20%, the supply orders that haven't been adjusted since the contract started, the crew assignments that explain the quality variance between shifts. We validate every finding with your team before acting on it. Not a one-time audit. Always running, always finding more.
Execute
Every finding comes with a concrete plan and a deploy button. We build AI specialists that handle the fix end to end. Flag labor overruns by site before they hit the monthly P&L, adjust supply par levels based on actual consumption, route quality inspections to the sites with the highest variance, surface contracts approaching negative margin before renewal. You approve, they run. We stay with you to make sure they deliver.
Example Findings
What Yield typically finds.
Based on a typical mid-market company with $20M–$50M in annual revenue.
Cost
Labor Hours Exceeding Bid Assumptions Across Sites
$218K/yr
Cost
Supply Waste from Unadjusted Par Levels
$126K/yr
Risk
Contracts Operating Below Margin Threshold
$77K/yr
Process
Ops Manager Time on Manual Site Inspections
11 hrs/wk
Knowledge
Crew-Level Cleaning Sequences Not Documented
7 per site
In Practice
See it work.
From day one.
Week 1
Discovery
We talk to every crew.
AI-led conversations with every operations manager, site supervisor, crew lead, estimator, and account manager across your portfolio. Not surveys. Real conversations that capture the site-specific adjustments, the supply workarounds, the scheduling decisions no job costing system records.
Month 1
Blueprint + First Savings
Your Blueprint is live. Agents are saving money.
A complete, verified map of how each site runs, from bid assumptions through nightly execution. The first cross-site opportunities are identified. AI specialists are already flagging labor overruns, adjusting supply orders, and surfacing contracts headed toward negative margin.
Ongoing
Continuous Returns
Savings compound. Every quarter.
Yield keeps finding inefficiencies, deploying specialists, and compounding savings. Labor allocation tightens as more shift data flows through. Supply waste drops as par levels adjust to reality. Contract renewals get sharper because estimating pulls from actual job cost data. The platform pays for itself and keeps going.
FAQ
Common questions.
Our crews work overnight shifts and most of our site supervisors are not technically savvy. How does Yield capture what's happening on the ground?
Yield doesn't require crews to use tablets or log data in real time. It pulls from the systems already around them: time clock data, inspection reports, supply requisitions, and client complaint logs. During the mapping phase, we have structured conversations with crew leads and supervisors to understand the site-specific adjustments they make that no system captures. When a crew lead knows that Building 7's third floor takes 40 minutes longer because of the carpet type, that knowledge gets encoded into a specialist that adjusts labor estimates for similar flooring across your portfolio.
We already use Swept for inspections and CleanTelligent for quality tracking. What does Yield add that our current software doesn't?
Swept and CleanTelligent track what happened after the fact. They show you inspection scores and deficiency rates by site. Yield connects that quality data to the labor hours, supply consumption, and crew assignments that caused the variance. When Site 14 drops from 94% to 78% quality, your current tools show the decline. Yield shows you that it happened when Crew B started covering that site, that Crew B also underperforms at two other locations, and that the labor hours on those nights are actually higher despite lower quality. The fix isn't more inspections. It's a crew assignment change.
Our contracts have different scope, square footage, and frequency. Can Yield actually compare labor efficiency across sites that are fundamentally different?
Yield normalizes by scope type, square footage, frequency, and building characteristics before comparing sites. A 50,000-square-foot medical office cleaned five nights a week doesn't compare directly to a 200,000-square-foot corporate campus cleaned three nights a week. But when you have eight medical offices of similar size and scope, and one consistently takes 30% more labor hours than the others, that pattern is meaningful. Yield finds those apples-to-apples comparisons within your portfolio and surfaces the specific sites where labor, supply, or quality diverges from your own benchmarks.
We're worried that showing contract-level profitability data to our account managers will create conflict with clients during renewal negotiations.
Account managers see what you decide they see. Yield's role permissions let you control visibility at every level. Operations managers see labor and supply data. Account managers see renewal risk indicators without raw margin numbers. Executives see the full picture. The point isn't to arm account managers with margin data to fight clients. It's to flag contracts approaching negative margin early enough that your estimating team can model the renewal correctly, with actual cost data instead of the original bid assumptions that are now two years stale.
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See what Yield finds in
your cleaning operations.
30 days. Real results. Or walk away.