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They use prepared data. They avoid messy exceptions. They show the happy path. That may be useful for understanding a product at a high level, but it does not answer the question finance teams actually care about. Will this work on our process? That question depends on details. Your ERP exports may be inconsistent. Your spreadsheets may have hidden logic. Your team may rely on judgment that is not written down anywhere. Your process may include exceptions that only show up when the data gets messy.

Finance work is not just "read data and produce an output." It often requires source tracing, reconciliation, interpretation, calculation, exception handling, and knowing when not to guess. A system that looks polished in a demo may still fail when it sees your real files. Don't partner with us because of a pretty marketing deck. Do it because you get to see our agents deliver first.

Specter handles the agent setup. Your team mainly provides the process data, gives context where needed, and joins the live review. The data can come from the same places your team already uses manually. That may include messy spreadsheets, ERP exports, PDFs, emails, reports, schedules, or other files involved in the workflow. You do not need to clean the process into a perfect demo format. In fact, that would defeat the point. Specter is trying to test the work as it actually exists.

The agent is designed around the actual process, not a generic template. It reads the relevant inputs, applies the process logic, performs calculations or comparisons where needed, and produces an output that can be reviewed. The agent should not hide its reasoning behind a black box. Its output should be tied back to source evidence, calculations, and the judgment used to arrive at the result.

Finance teams cannot approve work they cannot inspect. If the agent codes an invoice, calculates a commission, prepares an adjustment, or flags a discrepancy, the reviewer should be able to see why. The agent should also surface exceptions and uncertainty instead of guessing. If a file is missing, a rule is ambiguous, a source conflicts with another source, or the agent cannot determine the right answer with confidence, it should ask, not guess.

Once the proof of concept is ready, Specter runs the agent live and reviews the result with your team. It is a working review of the agent's output against your expected result. In more than 25% of our PoCs, the agent found errors in customer finance teams' existing manual work. Some were five-figure mistakes quietly hitting the bottom line. The proof of concept usually teaches both sides something useful. Your team sees how Specter handles the process, how the agent reasons through the inputs, and where review is still needed. Specter learns the shape of your process, the data quality issues, the exceptions, and the rules that matter.

The proof of concept should be judged by accuracy, reviewability, exception handling, lift, and fit. It is designed to give your team a concrete basis for evaluation.

Start with one real workflow, prove value on live data, then move into a supervised rollout with your team still in control.