Capabilities
Data & AI: from fragmented sources to governed intelligence in production
We build data platforms and AI systems where lineage, quality, and evaluation are as important as model accuracy—because your regulators, customers, and finance partners will ask for proof, not demos.
Lakehouse
patterns across major vendors
RAG
production patterns with eval harnesses
Lineage
by design—not retrofitted
MLflow
registry & promotion gates we implement
Analytics platforms that CFOs and engineers share
The failure mode of many analytics programs is duplicate pipelines, conflicting metrics, and a data team that becomes a bottleneck. We implement semantic layers, data contracts in CI, and ownership models that make producers accountable for quality—not consumers for detective work.
Snowflake, Databricks, BigQuery, and Fabric are composed with cost controls tied to workload tiers and query patterns finance can interpret.
Streaming and real-time operational intelligence
Kafka, Flink, and CDC patterns connect operational systems to analytics without batch windows that hide fraud or inventory truth. Schema evolution and compatibility rules prevent silent breakage when producers change.
Backpressure, dead-letter handling, and replay strategies are designed before peak events—not discovered during them.
Feature stores and production ML
Training-serving skew erodes trust in models. We align offline stores, online serving, and monitoring so drift is detected with business context—not only statistical tests disconnected from outcomes.
Model promotion includes performance on holdout slices, fairness checks where applicable, and rollback criteria tied to customer-facing metrics.
LLM applications with enterprise discipline
Retrieval-augmented generation requires document governance, chunking strategies, and evaluation suites that track answer quality over time. We implement safety filters, human escalation paths, and cost governance so token spend does not surprise finance mid-quarter.
Red-teaming and adversarial testing are part of the release path—not optional consulting add-ons.
Governance catalogs and privacy
Collibra, Alation, and open-source catalogs are wired into developer workflows so metadata is captured when systems change—not months later. PII tagging propagates to access policies and retention jobs.
Cross-border transfers and subprocessors are documented with technical controls that match legal agreements.
Operating model for data products
We stand up product trio governance for critical datasets: owner, steward, and consumer representatives with SLAs for freshness and incident response.
Roadmaps connect data investments to revenue hypotheses, risk reduction, and regulatory deadlines so prioritization debates happen with shared facts.
Evaluation harnesses
Offline metrics, online A/B, and human review loops scaled to your risk appetite.
Data quality in CI
Great expectations-style gates and anomaly detection on critical pipelines.
GPU economics
Right-sized inference, batching strategies, and reserved capacity where models are steady-state.
MLOps maturity
Registry, deployment automation, and monitoring aligned to model risk management expectations.