Technology & Software Companies

A technology company has engineers, and they are busy with the product. The data science around the business usually waits: which customers are about to churn, what revenue next quarter will really be, and which support answer is already written somewhere in the docs. Meanwhile the back office grows with every hire, with supplier and cloud invoices in several currencies, month-end and collections. We take the models and the back-office systems off the product team’s list, build them on your data and hand over code your engineers can read.

What waits while the engineers build the product?#

• Churn you see too late. Usage drops, seats shrink and the tone of support tickets changes months before a cancellation. The signal sits in event data, and nobody models it.
• A forecast nobody checks. The revenue and renewal forecast is rebuilt in a spreadsheet every quarter, and nobody compares it with what actually closed.
• Support knowledge in pieces. The answer to a customer’s question exists in an old ticket, a docs page and a chat thread, and the team writes it again.
• A growing back office. One or two people hired for something else handle the supplier invoices, the cloud bills in dollars, the card statements and the month-end pack for the accountant.

Where do models and automation pay in a tech company?#

• Churn and expansion signals from product events: a weekly list of accounts, with the reason each one was flagged, for the customer success team.
• Revenue forecasts checked against actuals at fixed horizons, so the error is known before the board asks.
• Search over docs and past tickets that answers with its source.
• Invoices, reconciliation and the month-end pack handled by a system, with the exceptions on a short list.

What decides the outcome is less the model than how it is tested: a clean split between training and test data with no leakage, error tracked over time, and a metric agreed before anything is built.

Which of our solutions fit?#

• Customer & Risk Intelligence: churn analysis and behavioral classification on real event data.
• Demand & Sales Forecasting: forecasts at several horizons, checked openly against actuals every month.
• Receivables anomaly detection: customers whose payment behavior is drifting, on a weekly list.
• Supplier invoices and month-end: invoices and receipts read and booked, bank and cards reconciled, one folder for the accountant.
• The weekly management brief: the week’s numbers on one page, each with its source.
• Knowledge & Document AI: answers from your docs and tickets that cite where they came from.

How does an engagement start?#

With the question the team keeps postponing, usually churn or the revenue forecast. In the fixed-price scoping stage we read your event data, CRM and billing history, and write down what they can support and with what error. One metric is agreed before anything is built, for example how many of last quarter’s churned accounts the model would have flagged a month ahead. What you get is code and pipelines in your repository, reviewed with your engineers. How we work sets out the stages.