Architecture engagements built around the outcome you own.

Cloud & AI Strategy Roadmaps

A roadmap is only useful if it survives the first budget review. We build sequenced, costed plans that connect the outcomes your leadership already committed to with the platform decisions required to reach them — including the decisions to defer.

You leave with a target state, a phased path to it, an honest cost model, and a first ninety days specific enough to start on Monday.

• Current-state assessment across cloud estate, data platforms, and AI initiatives
• Target-state reference architecture with documented tradeoffs and alternatives considered
• Phased roadmap sequenced by business value, dependency, and risk
• Cost modeling, licensing analysis, and build-versus-buy recommendations
• Capability and operating-model gaps, with a plan to close them
• Executive-ready narrative plus the technical detail your architects need

Enterprise Data & Analytics Architecture

When two teams answer the same question two different ways, the problem is architectural. We design data foundations — ingestion, storage, modeling, and semantics — so that trustworthy numbers are the default rather than the result of heroics.

Our designs favor patterns your team can operate: clear domain boundaries, explicit contracts between producers and consumers, and lineage that answers “where did this number come from” without a forensic exercise.

• Lakehouse, warehouse, and hybrid platform architecture
• Medallion or domain-oriented layering with explicit data contracts
• Dimensional and semantic modeling for consistent, reusable metrics
• Ingestion, orchestration, and data quality frameworks
• Master data, reference data, and lineage strategy
• Performance, partitioning, and cost-efficiency review

Generative AI & Agentic Systems

The distance between a compelling demo and a system you would put in front of customers is mostly architecture: retrieval quality, grounding, orchestration, tool boundaries, evaluation, and the controls that keep the whole thing explainable.

We design and build assistants and agentic workflows that hold up under real users, real data volumes, and real audit questions — and we are equally willing to tell you when a simpler, deterministic solution is the right call.

• Use-case qualification and value assessment before a line of code is written
• Retrieval-augmented generation: chunking, embeddings, indexing, and grounding strategy
• Agent orchestration, tool and function design, and safe action boundaries
• Evaluation harnesses, regression suites, and human-in-the-loop review
• Prompt, context, and cost management at production scale
• Responsible AI controls: data handling, guardrails, traceability, and audit logging

MLOps & Productionization

Models create value only after they are deployed, monitored, and retrainable by someone other than their author. We install the engineering discipline that turns data science output into a supported production service.

The goal is unglamorous and decisive: reproducible pipelines, versioned artifacts, automated promotion, and monitoring that catches drift before your business does.

• Reproducible training and inference pipelines with versioned data and code
• Model registry, artifact management, and promotion gates across environments
• CI/CD for data and model workloads, including automated testing
• Feature engineering strategy and reuse across teams
• Drift, quality, latency, and cost monitoring with actionable alerting
• Incident response, rollback paths, and retraining runbooks

Power BI, Fabric & Databricks Modernization

Report sprawl is a governance problem wearing a reporting costume. We consolidate duplicated datasets into governed semantic models, rationalize workspaces, and put capacity and cost on a footing you can forecast.

The difference here is coverage across all three platforms. Most advisors are strong on the Microsoft side or the Databricks side and hand off at the boundary — which is exactly where lakehouse modernizations stall. We design the Databricks layer, the Fabric layer, and the Power BI layer as one architecture, so the handoffs between them are deliberate rather than discovered late.

• Power BI estate assessment: datasets, workspaces, refresh patterns, and duplication
• Governed semantic model design with certified, reusable metrics
• Microsoft Fabric adoption strategy: OneLake, lakehouse, warehouse, and pipelines
• Databricks lakehouse design: Delta Lake, medallion layering, and Unity Catalog governance
• Databricks-to-Power BI connectivity, with the modeling and performance tradeoffs made explicit
• Capacity, cluster, and warehouse sizing with cost forecasting across platforms
• Row-level security, sensitivity labeling, and deployment pipelines
• Report performance tuning and self-service enablement standards

Architecture Reviews & Governance

An independent review is the cheapest risk mitigation available to a technology organization. We assess what you have built — or what a vendor built for you — against resilience, security, cost, and operability, and we write down what we find in plain language.

Where governance is the gap, we stand up something proportionate: standards that engineers will actually follow, a review forum that unblocks rather than delays, and decision records that keep institutional memory intact.

• Independent architecture and solution reviews with prioritized findings
• Security posture, identity, and data protection assessment
• Resilience, availability, and disaster recovery review against stated objectives
• Cloud cost drivers, waste identification, and FinOps practices
• Architecture standards, reference patterns, and decision records
• Review board design and technical debt remediation planning

Fractional Chief Architect Services

Many organizations need senior architecture leadership without needing — or being able to justify — a full-time chief architect. We embed on a recurring basis to own the architecture function: standards, roadmap, review cadence, vendor evaluation, and the mentorship that grows your own senior engineers.

Typical arrangements run a set number of days per month on a rolling term, with a named principal and continuity you can plan around. The measure of success is that your internal team increasingly does not need us.

• Ownership of architecture strategy, standards, and roadmap
• Chairing design reviews and architecture decision forums
• Vendor and platform evaluation with documented selection criteria
• Technical advisory to executive leadership and the board
• Mentorship and capability building for internal architects and engineers
• Flexible monthly commitment with a named principal, not a rotating bench