Retail · Demand forecasting
National grocer — SKU-level weekly forecasts
A grocery chain with more than four hundred stores relied on spreadsheet allocations refreshed manually each Sunday. Store managers overridden the numbers anyway, eroding trust in central planning.
Data Nexus AI rebuilt ingestion from POS and weather feeds into Snowflake, engineered store-SKU features, and deployed gradient-boosted models with hierarchical reconciliation. MLOps pipelines retrain on a fixed schedule with drift alerts sent to Slack.
Result: Forecast error (WAPE) improved by roughly nineteen percent on pilot categories; planners adopted the tool because exceptions were explainable at the SKU level.
Services: Data platform engineering, applied ML, MLOps
Healthcare · Operational analytics
Regional clinic network — patient flow modelling
A multi-site clinic group struggled with unpredictable wait times and staff overtime. Historical appointment data lived in three systems with inconsistent provider identifiers.
We unified records under strict access controls aligned with PHIPA expectations, built a daily feature store of arrival patterns, and trained queue-duration models consumed by a scheduling dashboard. No diagnostic claims—purely operational forecasting.
Result: Average patient wait in pilot sites dropped by approximately twelve minutes during peak hours; nursing leads reported fewer last-minute shift extensions.
Services: Analytics modernisation, applied ML, governance documentation
SaaS · Product instrumentation
B2B software vendor — event pipeline & churn signals
A Toronto SaaS company shipped features quickly but lacked trustworthy product analytics. Events were duplicated, timestamps skewed, and the data science team could not reproduce churn metrics month to month.
We implemented a canonical event schema in BigQuery, backfilled two years of history with documented assumptions, and delivered a reproducible churn model with SHAP summaries for customer success managers. CI tests block schema-breaking releases.
Result: Time-to-insight for new feature launches went from weeks to days; leadership used a single agreed-upon churn definition in board materials.
Services: Data platform engineering, applied ML
Financial services · Reporting automation
Mid-size asset manager — regulatory report generation
Quarterly regulatory filings required analysts to copy figures from five sources into Word templates—a process prone to transcription errors and version confusion.
Data Nexus AI automated extraction from the warehouse, added validation rules against prior quarters, and generated draft narratives with human review checkpoints. LLMs were used only for language polishing of fixed templates, not for calculating numbers.
Result: Preparation time per filing cycle reduced by an estimated forty percent; audit reviewers received a complete lineage log for each published figure.
Services: Data platform engineering, AI governance, limited LLM integration
Professional services · Internal RAG
Consulting firm — permission-aware knowledge search
Consultants searched shared drives manually for past proposals. Sensitive client folders could not be indexed by off-the-shelf tools without respecting folder-level permissions.
We deployed a RAG stack on Azure with embeddings refreshed nightly, ACL mirroring from SharePoint, and citation links in every answer. Prompt guardrails refused legal or investment advice categories defined with the firm’s risk team.
Result: Median time to locate relevant precedent documents fell from twenty-plus minutes to under three in user testing; security review signed off because retrieval respected existing access tokens.
Services: LLM integration & RAG, PIPEDA readiness review
