Stop letting AI guess what your data means.
Your agents do not need another chatbot. They need a readiness layer that turns messy columns, tribal knowledge, and unstructured text into trusted, ranked, machine-readable context.
AI readiness report
Lending Club taxonomy
Agent blocked from using weak context: language_tone did not prove enough outcome signal.
The actual problem
AI did not break your data stack. It exposed the parts humans were quietly fixing.
400,000 tables and the agent picks a staging table.
Six definitions of revenue, all technically defensible.
A deprecated column looks right, runs fast, and lies quietly.
The real signal is buried in tickets, reviews, notes, and messy text.
What we built
A readiness layer that tells agents what to trust before they answer.
We turn taxonomy validation into machine-readable trust: every field gets a tier, exposure rule, evidence count, strongest metric, and trust score.
Design the taxonomy
AI proposes meaningful fields from a small, capped sample instead of dumping raw columns into a prompt.
Classify at scale
Rules handle bulk classification for zero runaway LLM cost. AI is reserved for the cases that need judgment.
Prove the signal
Every field is tested against the business outcome. Weak fields are dropped before agents can rely on them.
Expose only trusted context
Tier 1 fields become agent-ready. Tier 2 needs review. Tier 3 stays hidden by default.
The output
Not another dashboard. A trust contract for AI.
Validated fields rank first
Weak context is hidden by default
Every answer can cite the context it used
Taxonomy exports stay compatible with the modern data stack
0
fields exposed without validation
$5
default hard cap for external LLM spend
3x+
target spread for fields worth trusting