Turn your data into foresight. We build custom machine-learning models for forecasting, scoring, churn, and recommendations — embedded where your teams already work, not in a separate dashboard.
Predictive analytics consulting turns the data you already collect into forecasts and scores your teams can act on — which deals will close, which customers will churn, what demand looks like next quarter. Apexify builds custom machine-learning models and embeds their outputs directly in your CRM and reporting tools, so predictions show up where decisions are made.
Typical deliverables:
Every model ships with the plumbing that keeps it honest: documented assumptions, accuracy reporting your team can actually read, and alerts when input data drifts from what the model was trained on. A prediction your team cannot interrogate is a prediction they will eventually ignore.
Data audit first. Machine learning consulting starts with an honest look at your data: history depth, label quality, and leakage risks. We tell you up front whether your data can support the model you want — and what to fix if it cannot. When the foundations need work, our data engineering team builds them.
Baseline and model. We establish a simple baseline, then build candidate models and measure lift against it. If a model cannot beat the baseline meaningfully, we say so rather than ship it.
Validate in the field. Backtesting against held-out history, then a live trial where predictions run alongside current practice. You see accuracy on your business before anything changes in your workflows. The trial also surfaces the workflow questions — who acts on a flag, within what window — that determine real-world value more than another point of accuracy.
Deploy and monitor. Models go to production with retraining schedules and drift monitoring, and predictions are embedded into records, reports, and automations — including Tableau CRM where clients want native Salesforce analytics. Your team gets documentation and a retraining runbook, so the model does not depend on us to stay healthy.
Most predictive models fail at adoption, not accuracy — a churn score nobody sees changes nothing. Because Apexify is an official Salesforce Partner, we put predictions inside the systems your teams already use: on the opportunity record, in the service console, in the forecast — with the automation to act on them. Modeling skill and CRM delivery skill live in the same team, so nothing is lost between the notebook and the workflow.
This service fits companies with at least a couple of years of transaction or pipeline history and a decision that repeats: prioritizing leads, targeting retention, planning demand. B2B pipelines, subscription businesses, and companies with seasonal demand are common fits. We also keep the stack proportionate: many prediction problems are solved with well-engineered features and straightforward models, not deep learning — smaller to build, cheaper to run, easier to trust. If your data is too thin for reliable models, we tell you in the audit and recommend the cheaper path — usually better reporting first, models later.
Apexify is headquartered in Calgary and provides predictive analytics consulting to clients across Canada and the US, backed by 20+ years of combined team experience and a 98% client satisfaction rate.
As a rule of thumb, a few thousand historical examples of the outcome you want to predict — closed deals, churned accounts, fulfilled orders — spanning at least one full business cycle. The data audit answers this precisely for your case. Thin data does not end the conversation; it changes the recommendation, often to simpler scoring first.
Cost depends on data readiness, model complexity, and where predictions must be delivered. A scoring model over clean CRM data is a smaller engagement than multi-region demand forecasting across fragmented systems. We scope a fixed-price data audit first, which yields an estimate for the full build before you commit.
Typical first models reach a validated live trial in 6-12 weeks. The data audit takes the first 2-3 weeks; the biggest variable after that is data preparation. Embedding predictions into your CRM and workflows adds time depending on how many surfaces need them, so we sequence the highest-impact surface first.
Native features like Einstein scoring are strong defaults, and we implement them when they fit. Custom models win when your outcome, data, or business logic does not match the packaged assumptions — external data sources, unusual sales cycles, industry-specific churn drivers. As a Salesforce Partner, we are positioned to recommend either honestly.
Yes. We build models on whatever your stack is — data warehouse, ERP, product analytics — and deliver predictions into the tools your teams actually use. Salesforce depth is an advantage when you are on the platform, not a requirement for working with us.
Salesforce, CRM, and AI working as one system — explore the rest of the stack.