AI projects don't fail because of bad models.
They fail because of the data underneath them.
I'm a data engineer with 7+ years building infrastructure at Big Tech scale, now working directly with startups and scale-ups that need that same engineering discipline but can't yet justify a full-time hire. I design the pipelines. I build them. I'm the one who's still there when something breaks at 2am.
Everyone funds the dashboards, the models, the AI demo. Almost no one funds the pipeline underneath it, until it breaks.
Who you'll work with
One senior engineer on every engagement, from the first audit to production handoff. There's no bench of juniors to staff you with and no account manager relaying messages in between.
Leon Liang
Founder, Aeolus Data
Why one senior engineer beats a bench of juniors.
Large integrators sell you strategy decks and staff the delivery with juniors learning on your dime. I don't do handoffs. The person who scopes your engagement is the person who writes the code, reviews the pull requests, and answers when something breaks in production. That's the whole model.
Automated data quality checks, automated testing, infrastructure-as-code, and versioned pipelines, so failures show up in a test suite instead of a stakeholder's inbox.
Semantic chunking, metadata tagging, and vector-store sync so retrieval-augmented generation and agentic systems retrieve the right context instead of hallucinating.
Governed data flows that link a model's output back to raw ingestion and every transformation in between, built for GDPR, HIPAA, and SOC 2 exposure, not just for a demo.
Co-creation, not a black box: I work directly with your engineers so the skills and the pipeline stay with you when the engagement ends.