Challenge an idea

To: Priya Raman (for exec offsite pre-read) From: Staff PM Re: The strongest case against committing three squads for three quarters to Fieldline Pay

Bottom line

Payments is a real opportunity, but the $24M is not supported by our own data. Re-estimated from the pack, month-24 run-rate is roughly $1M–$6M, central case about $2.3M. Even the most generous extrapolation of the pilot gives about $9M. The plan would pause the scheduling rewrite, which is tied to $1.9M of churned ARR a year, to fund a payments line that at its central case earns about the same amount after nine squad-quarters. I recommend a smaller, staged bet.

1. The assumption the plan depends on most

The plan assumes a dollar of invoice value is worth 0.7% to us. It is not, because 0.7% is the margin on card payments only.

The term sheet says we net about 0.7% on card value, but ACH nets $1.60 per payment ($2.00 charged, $0.40 cost), and checks and cash earn nothing. The CEO and CFO models apply the card margin to all $4.83B of invoices. What matters is how those invoices get paid.

• Commercial jobs are 58% of invoiced value, and only 5% of that is paid by card. The average commercial invoice is $3,800, so ACH nets $1.60 / $3,800 = 0.04%.
• Residential jobs are 42% of value, and the pilot lifted card share to 71%.

Yield per dollar of volume that actually flows through Fieldline Pay, using pilot card shares and assuming the non-card remainder moves by ACH:

Even if every invoice dollar went through Fieldline Pay, the blended yield would be 0.30%, less than half the modelled 0.7%.

The evidence is consistent with this: - 11 of 14 mostly-commercial customers say their clients require ACH or check. - 6 of 22 won't offer card at all. - Head of Sales expects property managers to refuse 2.9% on a $3,800 invoice.

Pilot revenue is $5,630 per adopting customer, against the $14,700 the CEO's model implies ($2.1M × 0.7%). That is 38% of the claim, from a pilot that was 64% residential, against 42% for the base. This is the best case for the mix.

Two supporting errors compound this: - The $2.1M is a mean. The median customer invoices $640k. The top 115 customers carry 48% of volume, and 71 of them are on multi-year processor contracts, mostly to 2028. Sales expects none to move. - Tradesly is not a comparable. It is about 80% residential, and we are 42%. Its 22% revenue share says little about a base that is mostly commercial by value.

2. Re-estimate of month-24 run-rate

Formula: addressable volume × adoption × share of adopters' invoice value routed through Pay × blended yield.

Addressable volume. I remove the locked-in accounts, assuming the 71 are typical of the top 115: 71/115 × $2.32B ≈ $1.43B. That leaves $3.40B.

• Adoption. The pilot's 61% came from invited customers over four months, so I treat it as a ceiling.
• Routing. The CEO's model implicitly assumes 100%. Customers who adopt still get paid by check and cash on many invoices, and payers choose the method.
• Yield. Base is the whole-base mix. High approaches the pilot's residential-heavy 0.42%. Low assumes the locked-in accounts are commercial-heavy, so the remainder skews slightly more residential and the low case is harsher than it needs to be.

Cross-check against the pilot. The base case implies about $2,300 per adopting customer, against $5,630 in a residential-skewed, hand-picked pilot. That is plausible. The most generous version applies the pilot's $5,630 to 1,610 customers, giving $9.1M. That ignores the lock-ins and the pilot's mix, and it is still 38% of the claim.

Implication for the board. At $2–6M, payments would be roughly 5–12% of revenue, not 35% by 2028. Reaching $24M would need about a 3% yield on the volume in my base case, which is not available on this term sheet.

Caveats: - Payment-method data comes from only 690 self-reporting customers (30%). - The pilot ran four months. - I have not modelled ramp, disputes or chargebacks, or upside from a paid fast-payout product. That last one is the most interesting missing piece.

3. What would prove me wrong, and the cheapest test

To vindicate the plan, we would need to show that commercial volume can yield at least ~0.5% net, with at least 60% of adopters' volume routed through Pay. That would put the total near $15M or more. On a $3,800 invoice, 0.5% is about $19 net per invoice. That requires either much higher commercial card share, or a fee people will pay, such as faster payout.

A six-week constraint: commercial invoices take 52 days on average to be paid, longer than the test window. We cannot wait to observe commercial cash-in, so the test must use leading indicators and data we already hold.

The test (roughly one PM, one analyst and one engineer, using existing pilot plumbing): 1. Week 1: re-cut the existing pilot. Split the 38 customers by commercial share and compute yield per dollar of volume for the commercial-heavy ones. Their older invoices are already paid, so this costs nothing and tells us quickly whether 0.08% is right. 2. Weeks 1–6: a commercial-heavy cohort. Recruit about 20 mostly-commercial, non-locked-in customers, drawn from the 11 who said their clients require ACH or check. Turn on pay-by-link and measure the share of payers who choose card and the share of invoice value routed via link. 3. Weeks 2–6: price a fast-pay offer. Offer these customers accelerated payout, funded by a fee, as a real priced offer with sign-up. Nine of 22 said they would pay to be paid faster, and this is the only route I see to meaningful commercial yield. Measure conversion at the stated price.

Thresholds, set now: commercial card share above 25%, or fast-pay uptake implying at least 0.3% net on routed commercial volume. Below both, the base case stands and the bet should be sized to it.

4. What we should do instead

Don't commit three squads for three quarters, and don't pause the scheduling rewrite. The rewrite addresses the top-cited churn reason in the >50-technician segment, which lost $1.9M ARR last year. Those are also the accounts with the most volume and the ones payments can't reach. Cutting the rewrite risks retention at the top of the base to chase payments revenue the top can't deliver.

A staged alternative: 1. Fund one squad for two quarters, focused on residential-heavy and small customers, where the pilot works (71% card share, ~0.6% yield). Ship pay-by-link, card and ACH broadly, and track net revenue per adopting customer and routed share. 2. Run the six-week commercial test above. Its result decides whether a second and third squad get funded for a commercial cash-flow product (fast payout or early pay). That product fits the real pain: 17 of 22 customers named getting paid on commercial jobs as their biggest cash problem. 3. Set gates. Add squads only when the test or the first quarter's data shows blended yield above 0.4% on routed volume. Revisit the locked-in accounts as contracts approach expiry in 2028. 4. Reset the board narrative from "$24M / 35% of revenue" to a $2–6M range with a clear path to more if commercial monetisation works. For the raise in about 14 months, a measured payments attach rate and net revenue per customer will be more credible to investors than a projection that unravels in diligence.

The CEO's instinct that payments could make Fieldline stickier is sound, and the pilot's 61% adoption is a good sign. But the current plan assumes we can monetise the volume we don't touch at the rate we monetise the volume we do, and our own numbers say otherwise.

To: Priya Raman and the executive team Subject: Fieldline Pay: validate the revenue engine before committing three squads

Recommendation: do not approve three squads for three quarters. Authorise a six-week commercial validation using the existing pilot and protect the scheduling rewrite. Payments may be a useful extension, but the supplied evidence supports an illustrative annual revenue range of $3.3M–$7.2M¹, with substantial uncertainty. It does not support underwriting $24M or making that figure central to our next raise.

The assumption the plan depends on

The unsupported assumption is that customer adoption translates into monetising those customers’ entire invoice value at a 0.7% net yield.

The model multiplies customer count by average invoicing and card economics. Each component hides a material constraint:

• Adoption is not payment capture. Enabling Fieldline Pay does not mean every payer switches. In the reporting sample, 81% of invoice value is currently paid without cards.
• 0.7% is card economics, not blended economics. ACH earns $1.60 per payment. On a $3,800 commercial invoice, that is 0.042%, around one-seventeenth of the assumed yield. Checks and cash earn nothing.
• Customer count is a poor proxy for accessible volume. The largest 5% generate 48% of invoicing. Of those 115 customers, 71 have processor contracts running mostly to 2028. Seventy per cent adoption among smaller customers could leave much of the revenue pool untouched.

Our mix compounds the problem. Commercial work represents 58% of invoice value. Commercial card share barely changed in the pilot, from 5% to 6%, despite pay-by-link on every invoice. Residential card share increased to 71%, but the pilot was 64% residential against 42% across Fieldline.

The customer problem is also different from the proposed solution. Seventeen of 22 interviewees identified slow commercial payment as their biggest cash problem. Offering another payment method does not establish that property managers will abandon net-45 or net-60 terms.

Tradesly shows payments can work for a business with approximately 80% residential customers. Its 22% revenue contribution does not validate our volume, adoption or margin assumptions.

What the supplied numbers support

Use:

Annual revenue = eligible invoice value × adopting share of that value × realised net yield.

The following are scenarios, not statistical bounds. Neither the exact invoicing of the 71 contracted accounts nor representative payment capture is supplied.

Working:

Lower yield:

`19% × 0.7% + 44% × $1.60 × (42% ÷ $410 + 58% ÷ $3,800) = 0.216%`

`$2.51B × 61% × 0.216% = $3.3M`

Upper yield:

`33.3% × 0.7% + $1.60 × (42% × 29% ÷ $410 + 58% × 94% ÷ $3,800) = 0.304%`

`$3.40B × 70% × 0.304% = $7.2M`

These calculations assume eligible customers retain the overall residential/commercial mix and that adoption rates by customer also apply by value. The lower scenario assumes 44% ACH within both job types. Both assume full capture of the payment streams modelled. The upper scenario additionally interprets the pilot’s card shares as shares of value; if they are transaction counts, that extrapolation is unsupported. It also assumes complete conversion of checks and cash to ACH.

Consequently, $3.3M is not a floor. The reporting sample covers only 30% of customers, and actual volume concentration or capture could reduce revenue further.

A separate check points in the same direction: scaling the pilot’s $5,630 annual revenue per adopter to 61–70% of 2,300 customers produces $7.9M–$9.1M, before correcting its residential bias or account availability.

On the upper scenario’s eligible volume and 70% adoption, $23.7M would require approximately 1.0% net revenue on every adopted invoice dollar, above even the quoted card margin. Growth could alter this, but the pack supplies no growth assumptions to bridge the gap.

These are annualised net payments revenues under the partner terms, not profit or contracted software ARR.

What would prove this challenge wrong

We would need evidence that accessible customers generate materially more net revenue per total invoiced dollar than these scenarios imply, at sufficient committed volume to support $24M. That could come from much greater accessible volume, different economics or a demonstrably valuable paid service. Enthusiasm and payment-feature activation would not suffice.

The cheapest six-week test is to extend the existing pilot, with analyst, sales and limited engineering support:

1. Establish the accessible denominator. Match the 71 processor contracts to actual invoice value, expiry dates and any permitted carve-outs. Replace the equal-volume assumption with account-level evidence.
2. Recruit a stratified cohort. Include commercial-heavy businesses and large uncontracted accounts, alongside residential firms. Count refusals in adoption results and weight findings to eligible invoice value.
3. Measure actual money movement. Track total invoicing, activated volume, captured volume, payment methods, realised fees and net revenue. Report residential and commercial results separately. Use existing receivables approaching their due dates to observe commercial payments within six weeks.

Before recruitment, agree that expanding investment requires a volume-weighted model supported by observed payments and credible customer commitments. To justify the original commitment, that model must show a defensible route to $24M by month 24. Six weeks cannot establish mature adoption or commercial collection cycles; an inconclusive result should not trigger automatic approval.

How the bet should change

If validation confirms residential economics, pursue a narrower residential payments offering with staged staffing and revenue gates. Separately test whether commercial customers will make concrete commitments to a faster-payment proposition. Nine expressions of willingness to pay justify discovery, not a financing product.

Keep the scheduling rewrite funded. The affected segment lost $1.9M ARR last year, with scheduling the top-cited churn reason. That is not all recoverable, but pausing the work has a documented cost against a speculative upside.

We should update the board with a scenario range and validation milestones. With the raise approximately 14 months away, protecting credibility and existing revenue matters more than preserving an unsupported headline.