Engineering, in the field.

Planning teams relied on spreadsheets and experience — unable to react to volatile demand or explain forecast swings to leadership.

We built an ensemble of gradient-boosted and deep time-series models inside an MLOps pipeline with automated retraining, then added an LLM layer that turns forecasts into plain-language scenario narratives.

Forecasts now refresh automatically as new data lands, planners explore what-if scenarios in seconds, and every number traces back to its drivers.

Institutional knowledge was scattered across wikis, PDFs and email, and off-the-shelf chatbots leaked answers across permission boundaries.

We engineered an agentic RAG system with hybrid vector + keyword retrieval, per-document access control mapped to the client's identity provider, and an automated evaluation harness scoring answer quality and citations on every release.

Employees get cited, permission-aware answers in seconds, and the evaluation suite catches regressions before they ever reach users.

Millions of sensor readings per hour arrived faster than the business could analyze them, and optimization ran overnight — far too slow to act on.

We built streaming data pipelines, a time-series warehouse and an optimization service, surfaced through real-time dashboards and an alerting layer engineers trust.

Operations moved from overnight batch to real-time decisions, with anomalies flagged the moment they appear.

A founding team had a validated idea but no engineering org, and needed a production-grade, sellable product without accumulating technical debt.

We designed the architecture, built the multi-tenant backend and Next.js frontend, and embedded AI copilots into the core workflows — shipping continuously behind a clean CI/CD pipeline.

The product went from concept to a scalable, multi-tenant platform with AI woven through the experience, ready to onboard customers.

Teams manually keyed data from thousands of contracts and claims — slow, costly and error-prone, and impossible to scale with volume.

We combined layout-aware document models with LLM extraction and a human-in-the-loop review queue, with confidence scoring that routes only uncertain cases to people.

The bulk of documents now flow straight through, staff review only edge cases, and every extraction carries a confidence trail for audit.

Static, rule-based merchandising couldn't keep up with a changing catalog or individual intent, leaving relevance and conversion on the table.

We built a feature store, trained recommendation and ranking models, and served them behind a low-latency API with continuous evaluation and A/B testing.

Every session now receives ranked, personalized results in milliseconds, with models retrained as customer behavior shifts.