The curve is steep then flat
Most of the gain arrives between week four and week twelve. After week twenty the rate moves by one or two points a quarter, which means the twenty-week figure is close to the ceiling rather than a waypoint.
Research · updated August 2026
Every vendor in this category publishes an accuracy figure and almost none publish the spread behind it. A median tells you about the customer in the middle; the interquartile range tells you what happens if you are not that customer, and it is the number worth reading.
Tell us your highest-volume workflow. We will tell you the realistic band before you commit.
Straight-through rate from week 4 to week 20. Shaded band is the interquartile spread — a quarter of customers sit below its lower edge.
What we found
These are the patterns that recur across every customer, and they are more useful for planning than any single headline figure.
Most of the gain arrives between week four and week twelve. After week twenty the rate moves by one or two points a quarter, which means the twenty-week figure is close to the ceiling rather than a waypoint.
Bill coding runs 91% at the median and 78% at the lower quartile. Planning on the median when your data resembles the lower quartile is how automation business cases fail.
PO matching reaches 94% because the comparison is structured. Multi-line allocation sits at 66% at ten times the volume, because the context needed is often not on the document.
Expense coding tops out at 84% and the binding constraint is photograph quality, not the model. Customers who fixed capture gained more than any tuning delivered.
Customers whose top 50 vendors cover 80% of bills automate faster and higher. A long tail of one-off suppliers is the single strongest negative predictor we see.
We score ourselves at 69% on vendor deduplication and publish it because the alternative is implying a solved problem. Two subsidiaries with different names and addresses should not match automatically.
A benchmark that only shows the median is a benchmark designed to be quoted. It sets an expectation half of customers will not meet, and the half that do not meet it conclude the product underperformed rather than that they were told the wrong number.
Publishing the interquartile spread costs us deals where a competitor claims a single higher figure with nothing behind it. It gains us customers whose business case survives contact with their own data, and that is the trade we would rather make.
Four factors explain most of the variance we see, and all four are knowable before you buy. Vendor concentration: how much of your volume comes from suppliers you bill repeatedly. Document quality: whether invoices arrive as structured PDFs or as photographs. Policy clarity: whether coding rules are written down or reconstructed each time. And history depth: how many prior examples exist for the agent to learn the pattern from.
A customer strong on all four typically lands above the upper quartile within twelve weeks. A customer weak on two of them will sit below the median indefinitely, and no amount of model improvement changes that — the information required simply is not present.
Straight-through means the transaction completed without a person touching it and was not subsequently reversed. It does not include transactions a person approved quickly, because an approval click is still a person in the loop and counting it would inflate every figure here by ten to fifteen points.
We also exclude the first three weeks of any deployment. Early rates are dominated by configuration rather than capability, and including them would make the improvement curve look more impressive than it is.
An agent operating at 91% with a 0.3% reversal rate is safe to grant authority to. One operating at 94% with a 3% reversal rate is not, because reversals are errors that got through and were found later — which means the ones that were not found are also out there.
Reversal rate across the set runs between 0.1% and 0.6% by workflow. It is the figure we watch most closely and the one that triggers an authority reduction when it moves.
Twenty-three customers running at least one agent workflow continuously between January 2025 and June 2026, all of whom opted in to aggregate reporting. Figures are computed from production data rather than reported by customers.
Straight-through is defined as: the transaction completed with no human interaction and was not reversed within 90 days. Weeks are counted from the date authority was first granted for that workflow, not from contract start.
The sample is small and skews toward services businesses and software companies, which is what our customer base looks like. It under-represents distribution and manufacturing, and we would expect different results there. We state that rather than extrapolating.
No customer is identifiable in the aggregates, and any workflow with fewer than five contributing customers is excluded rather than published on a thin base.
Questions
A few hundred historical transactions with known outcomes is enough to tell you where you would land.