Bryant M.

Your billing says one thing, inventory says another, the field says a third, and nobody has compared them, so the money leaking through the gap stays invisible. Finding it is what reconciliation pipelines, AI agents, and Python automation are for. Every service below is the same problem seen from a different side: operational data that cannot be trusted, in a system that keeps running anyway. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ TWO SYSTEMS DISAGREE AND THERE IS NO WAY TO PROVE WHICH ONE IS RIGHT Invoices that will not tie to inventory, assets billed that do not exist, assets running that were never billed, and the only check anyone has ever done was by eye on a spreadsheet. What you get: line-by-line reconciliation across every source, each mismatch classified rather than totalled, and a Python pipeline that reruns the check monthly instead of once. An audit of 121 network links against contract and invoice records turned up 28 billed but absent from inventory and 70 in service but missing from the convention, roughly 1.19 billion FCFA($2M) per quarter that had been invisible because nobody had put the three sources side by side. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SOMEONE ON YOUR TEAM REBUILDS THE SAME REPORT EVERY MONTH Four exports, pasted into a sheet, the same broken rows fixed by hand, sent out, and then again in thirty days, and the cost of it never shows up on any line item. What you get: a scheduled ETL pipeline or a LangGraph agent that assembles the thing end to end, flags the rows that will not reconcile instead of quietly averaging them away, and delivers the finished artifact wherever it needs to land. Python, FastAPI, SQL, whatever the sources demand. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ EQUIPMENT FAILS AND THE FIRST WARNING IS A CUSTOMER CALL The sensor data exists, nobody trusts it, and the alerting either screams all day or has never once fired. What you get: anomaly detection and failure prediction tuned so a false alarm costs a phone call and a missed failure does not cost a site, with drift detection on the inputs, because a model reading a lying sensor is more dangerous than no model at all. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ YOU INHERITED SOMETHING THAT HALF WORKS Whoever built it has moved on, the handover was a notebook and a README, and it holds together until connectivity drops, which it always does. What you get: the retraining path, the monitoring, and the documentation that were never written, plus a version your team can change without you in the room. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ THE NUMBER MOVED AND SOMEBODY HAS TO EXPLAIN WHY Forecasts that cannot survive a question in a meeting are not forecasts, they are decoration. What you get: forecasting with explainability layers, so the person approving the budget sees why it moved and not only that it did. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WHY THESE ANSWERS LOOK DIFFERENT The starting point is the sensor and the meter, not the CSV. An electrical engineering background means the question asked first is what the signal physically is, how the hardware distorts it, and what drift looks like before it quietly poisons a training set. Most data work inherits clean inputs and assumes them. Here the inputs get doubted before the model does, which is usually where the real project was hiding. Constrained environments are the default, not the exception: limited connectivity, sparse labels, sensors that lie, infrastructure hours from the nearest engineer. Systems that need a stable cloud connection and a labelled dataset do not survive there, so they do not get built that way. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ A GOOD FIT WHEN Your data is messier than the brief admits, the systems that hold it were never designed to agree, and you would rather know the true scope than hear a confident yes. A POOR FIT WHEN You want a dashboard skin over numbers nobody has validated, or a model shipped by Friday on data that has not been looked at. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Scoping something down and telling you the problem is smaller than you feared beats taking a contract on a guess. Send the exports, the schema, or the notebook that never made it to production, and you will get back an honest read on what is actually wrong with it.