Nebula talent graph as signal before sourcing

#aiengineering #engineeringtelemetry #nearshoreengineering #distributedengineering

Resume search flattens an engineer into job titles and tool names. That breaks fast in AI work, bc two ppl can list the same stack while only one can reason through model failure, data risk, system boundaries, and ownership when production moves.

Nebula starts with a graph, not a pile of profiles. It connects role depth, skill adjacency, market context, evaluation evidence, and delivery conditions. Axiom Cortex adds reasoning and cognitive-fit evidence, so the decision can explain why someone fits the work, not only where a keyword matched.

The Nebula AI Talent Graph page shows that chain from talent intelligence into evaluation, topology, onboarding, governance, and telemetry. For a CTO building AI systems, it maps what must stay connected before access is granted and code enters the delivery loop.

We use LATAM as the operating environment where the Nebula model has to hold up across time zones, compliance, review, devices, and ownership. The graph finds possible fit. The evidence decides whether it is real.

https://teamstation.dev/nebula-ai-talent-graph