Cutting Forecast Error and Catching Fraud
FMCG / B2B Lead GenerationCutting forecast error on static spreadsheets and catching fraudulent leads before they reach the funnel, for two enterprise clients with no data science team.
Overview
A global FMCG manufacturer and a separate B2B lead-generation operation both needed production ML without an in-house data science team: one was running demand planning on static spreadsheets, the other losing budget to fraudulent lead submissions. Canvas Labs Partners built a forecasting ensemble for the first client and a fraud-detection model for the second.
40-50% lower forecast error
versus the manufacturer's spreadsheet baseline
80%+ recall on fraud
fraudulent leads flagged before the sales funnel
Challenge
Two enterprise clients needed production machine learning without an in-house data science team to build or run it. A global FMCG manufacturer was running demand planning on static spreadsheets, with no model behind the numbers at all. A separate B2B lead-generation operation was losing budget to fraudulent lead submissions that nothing in the pipeline was built to catch.
Solution
For the global FMCG manufacturer, Canvas Labs Partners built a weighted ensemble of gradient-boosting machines and neural networks, deployed on Azure, to replace demand planning that had been running on static spreadsheets. The ensemble combines both model families rather than relying on either model alone.
For the B2B lead-generation operation, Canvas Labs Partners deployed a managed fraud-detection model on AWS SageMaker, flagging fraudulent leads before they ever entered the sales funnel. The model scores incoming leads at the point of submission, so a bad lead is caught before sales time is spent chasing it.
Both engagements share the same premise: production machine learning can run without a dedicated data science team on either side, deployed as a managed service or a tuned ensemble rather than a bespoke system each client has to maintain. One model cut forecast error on numbers that used to come from a spreadsheet; the other cut spend on leads that were never going to close.
Results & benefits
The FMCG manufacturer's demand forecast error fell 40 to 50 percent against its spreadsheet baseline, once a weighted ensemble of gradient-boosting machines and neural networks replaced manual planning. The B2B lead-generation operation now catches fraudulent leads at more than 80 percent recall, flagged by a managed model on AWS SageMaker before a single one reaches the sales funnel. Neither client needed to hire or build a data science team to get there: one engagement replaced a spreadsheet with a tuned ensemble, the other replaced a gap in the pipeline with a managed service.
Customer
FMCG manufacturer & lead-gen operation
Services
- Agentic AI consulting & development
- Applied ML & forecasting engineering
- Fraud detection & risk modeling
Tech stack
About us
Canvas Labs Partners is a software consulting team that designs and ships production agentic AI systems: multi-agent pipelines, tool-calling architectures, and human-governed automation, together with the trading infrastructure, blockchain, mobile, and product engineering those agents plug into. We publish our work as a case ledger rather than a portfolio, so every claim on this site links to the engagement behind it. Engagements follow the same four-step discipline, Map, Bound, Instrument and Govern, whatever the stack, and every entry in the ledger carries a result we are willing to be measured against.
Delivered individually, in a prior role, by one of Canvas Labs Partners’ founding team members. Not by the firm itself.
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