At Honeycomb, the monthly ARR close was a manual, cross-system slog. The Finance team spent a full week recalculating ARR and reconciling figures against contract lines because Salesforce could not encode all the business logic required to produce the real number. The work was repetitive, error-prone, and consumed senior Finance capacity at the most predictable bottleneck in the month.
The request was
“Help Finance close faster with AI.”
Discovery showed
The constraint was not speed of calculation. It was missing, scattered business logic, rules living in spreadsheets, tribal knowledge, and contract PDFs that Salesforce never captured.
Adding AI on top of a broken data model would have automated confusion. The real work was reverse-engineering the rules, rebuilding ARR from first principles using the actual contract evidence, and creating a system where Finance reviewed exceptions instead of rebuilding the truth every month.
- Pulled contract data from Salesforce and the Snowflake datamart alongside the actual PDF contracts to identify where the source systems diverged.
- Interviewed Finance SMEs to surface the tribal rules and edge cases that never made it into documentation.
- Mapped each contract against business-logic categories to separate clean, repeatable cases from genuine exceptions.
- Validated the rebuilt ARR logic against historical closes to measure coverage before asking the team to trust it.
Exhibit
From Manual Reconciliation to Exception Review
- 01
Ingest
Contracts from Salesforce, Snowflake, and source PDFs
- 02
Classify
Apply rebuilt business-logic rules to each contract
- 03
Resolve
89% of contracts resolve automatically with traceable logic
- 04
Flag exceptions
11% of contracts route to Finance for human review
- 05
Learn & refine
Each exception feeds back into the rule library
The loop matters: exceptions are not noise; they are the next set of rules to encode.
Exhibit
Contract Coverage
- 01A rebuilt ARR and iACV calculation engine grounded in contract-level evidence rather than system assumptions.
- 02A classification and rule library that captures the business logic Salesforce could not encode.
- 03An exception-review workflow that turns Finance capacity from reconciliation into judgment.
- 04A feedback loop so each resolved exception improves the next month's coverage.
- 05Cross-functional partnership with Finance, Data Engineering, and Engineering Enablement to keep the platform on shared infrastructure.
- Problem framing
- Reframed the request from 'close faster' to 'encode the truth once'.
- Rule engineering
- Reverse-engineered the contract logic that produced the real ARR number.
- Architecture
- Designed the ingestion, classification, and exception-review flow with data and engineering partners.
- Enablement
- Built Finance trust through validation against historical closes and transparent exception handling.
- Finance moved from a full week of manual reconciliation toward reviewing a small exception set.
- 89% of contracts now resolve automatically with traceable, auditable logic.
- The exception rate is tracked toward the <20% target, with each cycle encoding more rules.
- The work became a template for how Honeycomb automates high-value business workflows safely.
The lesson I keep learning: AI is not the product. The product is the judgment you embed in the system, and the trust you build by showing your work. Finance did not need a chatbot. They needed the contract truth to live somewhere they could audit.