Your SLA has an error-rate clause.Your QC has a sample.
Taiso checks every transaction against the purchase order, contract and policy it should match — escalates only what it can't confirm to your team, and records the evidence behind every exception. Same team. Every transaction covered.
The clause you signed, and the coverage you actually have
The penalty lands on the transactions your sample skipped.
Your contract prices an error rate. Your QC measures a sample. Those are two different numbers and only one of them is enforceable.
Per-transaction margin is under automation pressure from every direction, and every manual check you add eats what's left.
Your client wants AI in the process and will not accept AI risk in the output. Today you can only offer them one or the other.
When an exception is disputed, reconstructing why it was coded that way costs more than the exception did.
You can't sample your way to an error rate you're contractually liable for. Coverage is an architecture decision, and the clause already assumes you have it.
What the SLA actually measures
Your error rate is measured on the population, not your sample.
10%
of transactions your QC samples
100%
matched against PO, contract and policy
0
transactions posted unchecked
The clause you signed is measured across every transaction. Until now, so was your exposure but not your evidence.
See it run — one closed period
Watch every transaction get checked. Not a sample.
A two-and-a-half-minute walkthrough of one hundred processed transactions — narrated by the delivery lead whose SLA is on the line.
STEP 1 OF 6Your queue today
NarratorOne hundred transactions post to your client's ledger today.
Closed period · 100 transactions10 sampled · 90 unchecked
Taiso verification gate — every transaction checked against the purchase order, contract and policy, before it posts
sampled by your teamposted uncheckedchecked against sourcethe unconfirmed few
90transactions posted that nobody checked
10transactions your QC actually sampled
The ninety you didn't check are the ninety your SLA still covers.
0of 100 checked against PO, contract and policy
confirmed0
the machine couldn't confirm0
No processor re-keys an invoice.
Transaction 58 of 100Vendor invoice — contracted unit rate
The invoice posted at$38,400 for one licence block
The contract says“Unit pricing is fixed at $24,000 per licence block for the initial term.”
VerdictDISPUTED · held for a human
Posted clean, outside the sample. This is the transaction class that shows up in your client's audit, with your name on the SLA.
Exception queue0
Your team works these ten — not all one hundred. The same hours you spend on the sample today, spent on the exceptions that actually cost money.
Exception evidence0 / 100
confirmed0
decided by your team0
posted unchecked0
The backup your client's auditor accepts.
Sampled QC10 errors posted — surfaced at the client's audit, if ever. Your team re-keyed the rest by hand.
Verified processing0 transactions posted unchecked · 10 decided by your team · 100 with evidence attached. Same review hours.
Illustration of the mechanism on a hundred processed transactions. Volumes and counts are illustrative, not measured results — the exception rate most of all. How many items actually need a person is the number the parallel run measures on your own work, and it is the number that decides whether the hours hold.
Every transaction, not a sample
One hundred percent of the period. At the cost of your sample.
Taiso reads every transaction, checks it against the purchase order, contract and policy it should match, and routes only what it can't confirm to your team. Their hours stop going into re-keying and start going into exceptions.
Todayyou sample the period
sampled by your teamposted unchecked — your penalty exposure
Everything you didn't check, your SLA still covers.
With Taisoevery transaction, checked
checked against PO and contract, postedthe few nobody could confirm → your team
with evidence attached — the backup your client's auditor accepts
Same review hours. Every transaction systematically checked. The machine reads all of it; your team decides the few it couldn't confirm — and the error rate becomes a number you can actually quote.
Start with Taiso Verify on a single workflow — no migration, and nothing new for your team to learn. Scale to the Taiso Agent Control Platform — your whole delivery operation, verified.
Before renewal
What procurement asks before it signs again.
CLIENT DATA
Their ledger stays in your boundary
Deploy inside your own environment, or hosted under a DPA with a no-training clause and retention you set — the terms your client's own vendor review will ask you to evidence.
YOUR CERTIFICATIONS
Your SOC 1 stays the control
SOC 1 Type II is what your clients gate on for controls over their financial reporting, and that stays yours. We are a processor in your chain, not a hidden one — you get the subprocessor disclosure and the security pack your client's vendor review will ask for.
YOUR TEAM
Seats move up the stack, they don't disappear
The team stops re-keying transactions that were already right and works the exceptions that carry an SLA penalty.
THE UNIT ECONOMICS
Priced per action, against the clause
The question is not what a check costs in isolation — it is what one missed transaction costs you under the error-rate clause you signed. Verification is priced per action so it scales with the work, and the parallel run tells you the rate on your own population before you commit to anything.
On certification: SOC 2 Type I is on the clock, and Type II is what most financial buyers gate on — we are not going to pretend otherwise. Closed work is still confidential data, so the pilot runs under NDA and a DPA, in your environment or ours, and goes through the same security review your clients require of any new processor. We will fill in the questionnaire.
Where the gate runs
Five checks on every transaction.
01
Intent
Does the posting match the transaction that actually occurred?
credit memo coded as a new invoice → mismatch
02
Evidence
Is the term in the contract, or did the model infer it?
contracted unit rate located in the pricing schedule → supported
03
Policy
Does the posting follow your client's approval matrix?
invoice above threshold posted without a second approval → blocked
04
Risk
What does this cost under the SLA if it's wrong?
payment above the contracted rate → your team decides, not the model
05
Audit
Is the exception recorded, sourced and replayable?
Five checks, one verdict — ALLOW — before the transaction posts.
What the SLA gets back
The evidence compounds. The next SLA review starts ahead.
EXCEPTION EVIDENCE
Backup your client's auditor accepts
Every exception with the contract clause behind it, the verdict, and who approved it — attached to the transaction, not reconstructed at quarter end.
AN ERROR RATE YOU CAN QUOTE
Measured on the population
When the SLA review asks for your error rate, the number covers every transaction in the period — not the slice you had time to sample.
YOUR EXCEPTIONS
The judgment stays yours
Every call your team adjudicates sharpens what gets escalated next time. The queue gets shorter while the record gets longer.
By day 3 you see what it would have caught on a period you already closed. By day 14 you have the exception evidence and the list of what your sample missed — yours to keep either way.
Look familiar?
What the SLA review keeps surfacing.
A PENALTY WAS INVOKED
On transactions nobody sampled
The error rate in the contract was measured on the population. Yours was measured on a sample. The gap cost you.
THE CLIENT WANTS AI
And will not accept AI risk
They want your per-transaction price to fall and your error rate to hold. Automation alone gives them one of those.
MARGIN IS THE CONSTRAINT
Every check costs a seat
Accuracy and cost trade against each other because both are paid for in the same hours.
If two of these are true, the pilot below was built for you: a period you have already closed, and nothing in production changes.
The offer
One closed period. Your own error rate.
Give us a period you have already delivered, under NDA. We run it in parallel — no live posting is touched, no client system changes — and we hand back what the pipeline caught, what your sample missed, and what we got wrong. Bounded defect rates with the sample size beside them, never a zero-miss promise.