Your data already knows the answer.

Ingestion, normalization, enrichment, and storage, built so the data arrives clean and stays that way. We design Postgres schemas, type-safe access with Drizzle and Zod, and the reconciliation and idempotency work that keeps a pipeline correct when a source goes quiet or sends the same record twice.

Your SaaS tools, internal services, and databases each hold a piece of the picture. We connect them: REST and GraphQL APIs, webhooks, third-party services, and the auth and rate-limit handling that keeps the seams from breaking. The goal is one system of record, not eight tabs.

Some data cannot wait for a nightly batch. We build WebSocket streaming, event handlers, and monitoring loops for the workflows that need to react the moment something changes, with the fallbacks and reconciliation that keep a live system honest when a feed drops.

The work that should happen on its own: queue-backed jobs for anything that runs long or needs to retry, cron-driven services for anything on a schedule. We build the background processing so reports, syncs, and handoffs happen without the reminder email, and surface it so you can see what ran.

Data is only useful when someone can reach it. We turn pipelines into product surfaces: dashboards, admin tools, and reports your team actually opens. When the question is open-ended, we add vector search and semantic retrieval over your operational data, so people find the answer instead of the spreadsheet.

A pipeline you cannot see is a pipeline you cannot trust. Every system we ship carries structured logging, monitoring, and the reconciliation checks that catch a bad number before it reaches a report. When something drifts, you hear it from the dashboard, not from a customer.