SuperTrained vs. Building In-House

Buy the first, build the rest

If you have a strong engineering team, building in-house is a real option. The question is whether it's the best use of their time right now.

The key question:

Is AI your core product, or a tool that supports your core product?

Where this option works

+Full control over architecture and data
+No vendor dependency or lock-in
+Engineering team builds domain expertise in AI
+Can integrate deeply with proprietary systems

Where it falls short

–AI/LLM expertise is scarce and expensive to hire
–Prompt engineering, evaluation, and tuning are non-obvious skills
–First agent takes 3-6 months instead of 3 weeks
–Core product roadmap suffers while team experiments

Choose this when

If AI is your core product, not a productivity tool, you should build and own the capability internally. Full stop.

Choose SuperTrained when

If AI agents support your business but aren't the product itself, let us build the first one. Your engineers learn from our approach and can maintain or extend from there.

REAL EXAMPLE

The Situation

We needed an autonomous content pipeline for SnowThere that could research, write, and review content daily without human intervention.

The Result

We built the entire pipeline in weeks, not months: daily cron jobs, multi-source research, Claude-powered generation, and a three-agent editorial panel. Runs on $5/day.

$5/dayOperating cost