Expertise

We build the data layer your dashboards, apps, and agents run on.

Data architecture, migration, warehouses, vector retrieval, cloud infrastructure, and governance on Supabase, AWS, GCP, or Firebase, set up so one foundation serves both reporting and AI.

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The shift

The data layer now has to feed AI as well as dashboards.

For most of the last decade, data work meant building toward dashboards: clean inputs, governed access, a single source of truth, and a few well-designed reports for leadership.

AI added a second consumer. Vector databases now sit next to relational ones, embedding pipelines run alongside ETL, and schemas have to support retrieval as well as queries. Dashboards still matter, and they are no longer the only thing the foundation has to feed.

We design one data layer that serves both.

What we build

Six things we build, from architecture to governance.

  • Data architectureHow your data is structured, where it lives, and how it flows between systems, across relational, NoSQL, and vector stores. Most engagements start here because the architecture sets the limit on everything built on it.
  • Data migration and consolidationMoving data between platforms, or pulling many systems into one, with a written plan, validation against the source, and the downstream reports and apps accounted for.
  • Data warehouses and lakesSchema design, partitioning, query patterns, and the operational layer that keeps storage fast and cheap at the volume your business moves.
  • Vector databases and retrievalEmbedding pipelines, retrieval-ready schemas, and hybrid search across vector and relational stores, so your agents find the right context.
  • Cloud infrastructureThe platform under the data. We work on Supabase, AWS, GCP, and Firebase, and recommend the one that fits your team and budget.
  • Data governance and securityRow-level security, access controls, PII handling, and audit trails, applied to the data layer itself as well as the perimeter around it.

How we build

Three steps from audit to running pipelines.

  1. 01Step 1

    Audit

    We go through what you have: where the data lives, how it flows, and where the copies contradict each other.

  2. 02Step 2

    Architect

    We diagram the data layer before building it: where data will live, how systems connect, how access is governed, and what the structure has to support next, relational or vector or both, on the platforms you already run.

  3. 03Step 3

    Build

    We migrate the data, wire the pipelines, set up monitoring, and document the work. From there your team takes over, or we stay on to run it.

Next step

Book a discovery call.

Tell us where your data lives and what it has to support next. You leave the call with a view on the architecture and whether it is a project or the start of a retainer.

Email hello@workiflow.com

30 minutes. No commitment.