Raising correct invoices at the end of every month is, for most finance teams, a slow and unforgiving job. Prices change mid-month. Discounting is slab-based. Billing happens across multiple dimensions — client, location, route. One paste error, one stale lookup, and the wrong number lands in front of a client. So the team does the work carefully, by hand, for days.
This is a walkthrough of one such workflow, automated end-to-end in one hour.
The setup, and how it usually gets done
The company sells a commodity to several clients. The same commodity is priced differently for different clients, and even for different client locations — the price depends on which supply center ships to which destination. And those prices change periodically, not on a fixed schedule.
The finance team has three pieces of information:
- Daily transaction data — which supply centre shipped how much, to which destination, on which date
- A price reference — what price applied between each supply centre and destination, with effective dates
- A client mapping — which destination is billed to which client
At the end of every month, they sit down and combine these three sources to figure out: how much do we charge each client? For most teams, this means stacking monthly files together, looking up the right price for each transaction (the latest price effective on or before the transaction date), applying any slab discounts, mapping destinations to clients, and rolling up by client and month.
It’s not difficult work. It’s just a lot of it. And every step is a chance to introduce an error nobody catches until a client disputes the invoice.
User attached the datasources and gave the computation goal to the agent
Overall plan was made, clarifying questions asked where needed
User verifies step level actions done by the agent and also output at each step to ensure correctness
What we wanted to change
We took this exact problem to Hywit — our natural-language automation framework — to see if a finance team could hand it off and trust the result. Three things had to be true for the output to be usable:
Things that need to be kept into consideration are
- Accuracy to the last digit. This is a financial transaction. “Close enough” isn’t acceptable.
- Auditability. The output has to come with a trail that a finance lead, or an external auditor, can read and verify.
- Step-by-step visibility. If a number looks wrong, someone needs to be able to trace it back to the exact row of source data it came from.
Approach
The user attached the data sources — three monthly transaction files, the price cross-tab, and the client-destination map — and gave the agent a single natural-language goal:
Goal: Compute client wise, monthly sales value. You have been monthly supply data that has quantity supplied between a supply centre and destination. Prices are given in price cross tab file with effective date. If you don’t find the price for a date, pick the price for the previous available date. Client_Destination_Map mentions which destination needs to be billed to which client.
That’s it. No code, no formulas, no schema.
From this prompt, the agent explored the data sources, surfaced clarifying questions where the inputs were ambiguous, and proposed a step-by-step plan: load and stack the supply files, reshape the wide price sheet into a long format suitable for lookups, load the client map, apply an as-of price lookup for every transaction, multiply quantity by price, and roll up by client and month.
The user reviewed the plan before any computation happened. Once approved, the agent executed each step and showed its output.
Ensuring correctness, step by step
The key design decision here is simple: the agent doesn’t just produce a final answer. It produces — and shows — the output of every intermediate step. Row counts after each load. The reshaped price table. The stacked transactions. The priced rows. The monthly rollup.
This matters because AI agents can make mistakes, and the only reliable defence is letting the finance person inspect the working at the points where mistakes are most likely. A team member can see, for example, that 27 blank separator rows were dropped from the source files, leaving 810 valid transactions — and confirm that’s the number they expected.
If the output of any step looks off, the user can correct course before the error compounds into the final billing number.
Output at each step
Auditing the output
For a financial computation, “the answer looks right” isn’t enough. The agent was also instructed to produce explicit audit artifacts and write them into a shared file for reference. These included:
- A row-by-row record of which price date was matched to which transaction, with a note explaining why
- The full destination-to-client mapping, including any entries added manually during the run
- A row-level rollup showing how every individual transaction contributed to each client-month total
- A bottom-up cross-check recomputing the totals independently and flagging any mismatch
Anyone — a teammate, a finance head, an external auditor — can open the file and trace any line of the final invoice back to the exact transaction and exact price that produced it.
What This Changes for a Finance Team
With that the entire billing process was automated. All within an hour. Now next month, the team can just upload new data and run the system, and it can produce completely new results.
Have a billing or reconciliation workflow that eats your month-end?
We’d be happy to walk through it with you and show how Hywit would approach it — no pitch, just a working session on your actual problem.
Talk to us at support@hywit.ai