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Example · SaaS

Sub-second reconciliation at 10M+ transactions

Northwind's accounting platform had to reconcile ledgers in under a second for accounts with tens of millions of transactions. Their existing batch approach took minutes and the standard database techniques couldn't close the gap.

Northwind Ledger (fictional). This fictional example is for education only. It is not a customer claim or tax advice. It shows how to structure and reason about a SR&ED narrative. Do not copy it into a claim.

Technological uncertainty (T661 line 242)

Weak

We needed to make our reconciliation feature faster because customers complained it was slow.

Strong

It was uncertain whether sub-second reconciliation was achievable for accounts exceeding 10 million transactions. Established indexing and incremental-diff techniques degraded non-linearly past ~2 million rows, and it was unknown whether any data structure could hold the invariants while meeting the latency bound.

The weak version describes a business goal and effort. The strong version names the specific technological unknown and why the known methods couldn't be assumed to work.

Technological advancement (T661 line 246)

Weak

We successfully built a faster reconciliation engine that our customers are happy with.

Strong

We advanced our understanding of incremental reconciliation under high cardinality: a partitioned delta-tree with lazy invariant checking held sub-second latency to 12M transactions in testing. Failed experiments also showed that a pure in-memory approach could not hold consistency guarantees at that scale, which shaped the design.

The weak version reports a commercial outcome. The strong version states the knowledge gained and includes a documented failure.

Evidence matrix

What could support a claim like this

The matrix pairs each described element with the kind of source that could support it. The actual records still need review.

SourceWhat it shows
GitHub commit historyThe sequence of approaches tried, including the reverted in-memory prototype
Load-test resultsLatency curves showing non-linear degradation as transaction counts increase
Design docs / ADRsThe hypotheses and why standard approaches were expected to fail
TicketsThe framing of the problem as a technical unknown, dated during the work

The takeaway

Lead with the unknown rather than the feature. A reviewer is looking for what you could not know in advance and how you investigated it. A passing benchmark is the result, not the claim.

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