Services

Who it's for

Organizations scoping where data and AI capabilities stand today and what it would take to move

Problems it solves

• •Unclear current state across data, governance, and AI capabilities
• •Competing internal opinions on where to invest first
• •AI initiatives stalling before they reach production

Typical deliverables

• •Maturity scoring across data foundations, AI development, deployment, governance, and workforce
• •Top use cases worth pursuing, with feasibility notes grounded in observed systems
• •Top readiness gaps blocking those use cases, with what it takes to close each
• •Executive readout and written brief

Business outcomes

• •An evidence-based picture of where you sit on the maturity curve
• •A prioritized path forward, sequenced by dependency rather than ambition
• •Internal alignment on what to build first and why

Who it's for

Teams that need reliable data infrastructure before anything else can compound on top of it

Problems it solves

• •Disconnected systems and shadow spreadsheets
• •No centralized warehouse or lake
• •Manual data movement and brittle ETL
• •Quality assumed at the dashboard, not measured at the pipeline

Typical deliverables

• •Warehouse and lake design (Snowflake, BigQuery, Redshift, PostgreSQL)
• •ELT/ETL pipeline development (Airflow, Prefect, Dagster)
• •dbt transformation layer with tests and lineage
• •Data modeling using Kimball and Data Vault patterns

Business outcomes

• •Centralized, reliable infrastructure that downstream work can stand on
• •Quality measured continuously, not retroactively
• •Architecture that scales with the business, not against it

Who it's for

Organizations whose data carries regulatory, contractual, or operational risk

Problems it solves

• •No clear ownership of data assets
• •Access controls inconsistent across systems
• •Audit trails missing or incomplete
• •Compliance treated as a checklist rather than an operating practice

Typical deliverables

• •Data classification and sensitivity-based handling policies
• •Role-based access control across systems
• •Audit logging and lineage for high-consequence decisions
• •Approval workflows that survive an actual audit

Business outcomes

• •Defensible position on regulatory and contractual obligations
• •Governance that holds up under stress, not just on paper
• •A foundation that AI can be trusted on top of

Who it's for

Operators and finance leaders who need decisions, not dashboards

Problems it solves

• •No clear KPIs or reporting standards
• •Manual reporting that consumes leadership cycles
• •Self-service that produces inconsistent answers
• •Insights that arrive too late to act on

Typical deliverables

• •KPI framework definition aligned to business objectives
• •Dashboards in Power BI, Tableau, or Plotly Dash
• •Embedded analytics where applicable
• •Automated reporting and alerting

Business outcomes

• •A single, trusted version of the operational truth
• •Decision-grade visibility, not vanity metrics
• •Self-service that produces the same answer twice

Who it's for

Teams ready to move from descriptive reporting to predictive and prescriptive analytics

Problems it solves

• •Reactive decision-making that misses leading signals
• •No statistical or experimental capability in-house
• •Models built ad hoc and abandoned shortly after
• •Difficulty validating whether a change actually helped

Typical deliverables

• •Forecasting models for demand, revenue, and capacity
• •Segmentation, clustering, and propensity scoring
• •Classification and regression models with reproducible pipelines
• •A/B testing and experimental design

Business outcomes

• •Predictive insights that change the cadence of decisions
• •A repeatable experimentation loop, not one-off analyses
• •Models that survive past their first deployment

Who it's for

Organizations sitting on institutional knowledge that is hard to access

Problems it solves

• •Process expertise locked in heads, not systems
• •Search across documents, databases, and tickets that does not actually answer the question
• •AI experiments that stall on hallucination, leakage, or trust
• •No way to attribute an AI answer back to a verifiable source

Typical deliverables

• •Internal copilots tuned to your data, with source attribution
• •Retrieval-augmented generation across structured and unstructured systems
• •Search and summarization tools with citation
• •Agentic workflows scoped to bounded operational tasks
• •Vector and knowledge-graph architectures

Business outcomes

• •Answers in seconds, traceable to their origin
• •Institutional knowledge that compounds rather than walks out the door
• •AI that operators are willing to actually use

Who it's for

Organizations that need senior data leadership without a full-time hire

Problems it solves

• •No senior voice in tool, vendor, or architecture decisions
• •Lack of strategic direction across data initiatives
• •Hiring and mentoring gaps on a growing team
• •Executive reporting that does not reflect operational reality

Typical deliverables

• •Strategy and governance leadership
• •Hiring, mentoring, and team design
• •Vendor and tool selection
• •Quarterly analytics strategy reviews
• •Executive reporting and stakeholder communication

Business outcomes

• •Strategic direction held to over time, not abandoned mid-quarter
• •A team that can sustain the work after engagement ends
• •Executive-level visibility into what data is and is not doing for you

Who it's for

Teams looking to build durable internal data and AI capability

Problems it solves

• •Limited data literacy across the business
• •Unclear KPI definitions across teams
• •Engineers under-equipped for modern data tooling
• •Confusion about where AI does and does not apply

Typical deliverables

• •BI literacy training tailored to role
• •KPI definition workshops
• •Data engineering fundamentals
• •Applied AI and LLM use-case workshops

Business outcomes

• •Shared vocabulary across business and technical teams
• •Clear, agreed-upon definitions for the metrics that matter
• •A team that can carry the work forward without you