Each card below describes the shape of an engagement we're built to take on. Where a card reflects specific past work, identifying details have been generalized for confidentiality.
The cards below describe the use cases we’re set up to deliver. We’ve organized them by sector, but the more honest reading is by shape: replacing the weekly deck, productionizing a model, making unstructured documents behave like data, replatforming a legacy estate. The same handful of shapes show up across very different industries.
This isn’t a portfolio of completed projects on display , it’s a set of representative engagement shapes drawn from field experience and the patterns we’ve codified into our reference architectures. Where a card reflects specific past work and a client has given permission to name them, we’ll discuss that in a private conversation, not on the public site.
Stand up a shared feature store and walk-forward back-testing harness so an investment-research team can move pilot signals into production, and keep shipping new ones without standing engineering involvement.
Replatform a regulatory reporting estate from a legacy on-prem stack onto a governed warehouse, with documented lineage and rebuild-from-source semantics so audit cycles can be defended end-to-end.
Replace a weekly multi-team briefing deck with a live mobile-first dashboard, so leadership briefs from the cockpit and the teams that assembled the deck are freed for higher-value analysis.
A layout-aware extraction and vendor-normalization pipeline across millions of unstructured procurement records, designed to surface consolidation opportunities and a sustainable analyst review workflow.
A near-real-time bed-flow and discharge-readiness dashboard integrating staffing acuity, replacing phone-tree coordination during surge shifts with shared situational awareness.
A document-AI pipeline that extracts and normalizes prior-authorization request fields with confidence scores. High-confidence rows auto-route; low-confidence rows go to a human review queue.
Migrate a multi-business-unit reporting estate from legacy on-prem BI onto Snowflake + dbt + Looker, so report turnaround moves from weeks to hours and analyst capacity is recovered for higher-value work.
A workflow-embedded LLM assist for a customer-facing team, drafting first-pass responses inside the existing CRM, with consistency review built into the loop instead of bolted on after.
A severity and complexity scoring model for incoming claims, with documented assumptions and a challenger model, so routing accuracy can improve without losing reviewability.
An underwriter-facing dashboard surfacing comparable risks and historical loss patterns, designed to shorten cycle times and make decision rationale more consistently documented.
A risk-stratification model for student outcomes, designed for advisor consumption rather than automated intervention, so advisors can catch at-risk students earlier in the term.
A cohort-level enrollment funnel and yield analytics workspace, replacing a patchwork of Excel models with a single, governed view of the admissions pipeline.
We've worked with organizations from emerging programs to multinational enterprises across the sectors below, the patterns repeat more than they differ.
The five reference architectures the use cases above are built on, Decision Intelligence, Agentic AI, Knowledge Intelligence, ML Systems and Data Foundations.
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A focused practice across data, analytics, machine learning and applied AI.
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How the practice shows up across financial services, public sector, healthcare and enterprise.
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