Writing from the work, not about it.
Topics: data architecture, failure patterns, problem framing, production realities. Not trend pieces.
Published when there is something specific to say.
Posts are written by the people who built the systems. Each piece starts with a specific problem — data that lied, a pipeline that failed, a problem statement that needed three rewrites before code was touched.
What we have shipped and what broke first.
Why your training data is the problem.
Three production ML pipelines that degraded within 60 days — and the data labeling decisions made in week one that guaranteed it.
Rewriting the problem statement three times.
A document classification project that stalled for six weeks — not on model performance, but because the client and the team disagreed on what 'classified' meant.
Latency is a product decision, not an engineering one.
How a 400ms inference budget forced a complete rethink of the retrieval architecture — and why that constraint should have been defined in week one.
The pipeline nobody wants to own.
Data ingestion is 70% of the work on most AI projects. It is also the part every stakeholder deprioritizes. Here is what that costs in practice.
Measuring AI against business metrics, not benchmarks.
A model that scored 94% on the internal eval set and moved zero business metrics. The gap between accuracy scores and production value is where most AI projects go wrong.
Integrating into workflows that already exist.
The teams that get the most from AI systems are the ones who insisted the system fit into existing processes. Not the other way around.
New posts appear when there is something specific to report — a system shipped, a failure worth documenting, a pattern that took three projects to see clearly.
Building AI systems and want to compare notes? We are reachable at hello@groundwork.ai.