Sefermark designs, builds, and ships the complete data and AI product — pipelines, schemas, orchestration, and agentic AI systems that reason over live warehouse data. Snowflake-centric, cloud-agnostic, Azure-fluent.
Most vendors hand you a diagram and leave. We work across the full lifecycle — designing the architecture, building the pipelines, and staying through deployment so what we design actually ships.
Reliable, observable ELT built on Snowflake — Snowpipe, Streams & Tasks, Dynamic Tables — orchestrated with the tool that fits your team.
Schemas that survive contact with the business. Dimensional, Data Vault, or wide — chosen for your query patterns, not fashion.
Agents that reason over live warehouse data, call tools, and take action — grounded in governance, not duct-taped to the side of your stack.
Streamlit apps that go beyond dashboards — they write back to the database, put humans inside AI workflows, and turn your warehouse into an operational system.
A taste of what modern data tooling can do — a fully interactive 3D city map running on open data (OpenStreetMap), rendered live in your browser. The same techniques power geospatial analytics in Snowflake with GEOGRAPHY types, H3 indexing, and Streamlit apps.
Built with MapLibre GL JS + OpenFreeMap vector tiles (OpenStreetMap data). Click and drag to pan, right-click-drag to rotate, scroll to zoom. Buildings extrude in 3D at street zoom levels.
Every engagement starts with the right pattern for your data shape, latency needs, and team. Here are four we implement most often — all Snowflake-centric, all cloud-agnostic.
Land raw data in Bronze untouched, conform and test it in Silver, and serve business-ready marts from Gold. Implemented with Dynamic Tables or Snowpark Python so every layer is versioned, tested, and rebuildable from raw.
When it fits: analytics platforms, multi-source consolidation, teams that need auditability and the ability to replay history.
Events, CDC feeds, and file drops flow through Kafka or Azure Event Hubs into Snowflake via Snowpipe Streaming, with Streams & Tasks or Dynamic Tables transforming continuously. Minute-level freshness without managing Spark clusters.
When it fits: operational dashboards, fraud/anomaly detection, IoT telemetry, CDC replication from transactional systems.
Retrieval-augmented generation grounded in your governed warehouse: Cortex Search over documents and structured data, an LLM layer (Snowflake Cortex or Azure OpenAI), and an evaluation + guardrail loop so quality is measured, not assumed. Row-level security carries straight through to AI answers.
When it fits: internal knowledge assistants, analyst copilots, customer-facing AI grounded in your data, the retrieval backbone for agentic systems.
An orchestrator agent breaks a goal into steps, calls tools — SQL against Snowflake, Cortex Search for RAG retrieval, external APIs for action — and loops until the task is done. The result is an AI system that reasons over live warehouse data, not static snapshots.
When it fits: analyst copilots, automated data quality pipelines, pipeline monitoring & self-healing, customer-facing AI that acts — not just answers.
Streamlit apps aren't read-only dashboards — they're the interface where humans step inside AI workflows. An agent generates a prediction or draft; the operator reviews it in a purpose-built Streamlit UI; approval or correction writes back directly to Snowflake and triggers the next step downstream.
When it fits: AI output review & approval queues, data labeling & annotation, model-assisted data correction, any workflow where a human decision needs to enter the database cleanly.
Snowflake at the center, with best-of-breed tools around it. We recommend what fits your team and budget — including replacing tools you're overpaying for.
Jonathan Kelly is a data engineer and architect who builds on Snowflake every day. Over [X] years he has designed and shipped data platforms, pipelines, and AI systems for [industries/companies — placeholder], working across the full stack from ingestion to the dashboards and AI apps executives actually use.
Jonathan holds the SnowPro SME distinction — awarded to a small group of subject-matter experts who help author and review Snowflake's own certification exams — alongside both SnowPro Advanced certifications and Microsoft's Azure AI Engineer certification. Snowflake is his specialty; Azure is his home cloud; the architecture is always cloud-agnostic.
His approach: understand the business problem first, design the simplest architecture that solves it, build it with your team so they can run it without him, and stay accountable until it's live in production.
Tell us about your data and AI challenges — we'll get back to you.
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