Coach
Context-aware sales coaching
Answers are composed deterministically from the seeded content library and the selected account's state — the same inputs always produce the same guidance. The service interface is designed so an LLM-backed implementation can replace the rule engine without changing this page.
Account context
Workload
Incumbent
Positioning for this account
No account selected — guidance is generic. Select an account for tailored coaching.
Rule-based
One-sentence frame
For this account, position this as the real-time serving layer for infrastructure observability — Observability spend grows faster than infrastructure, and teams sample or drop telemetry to control the bill.
Why now
- Incident response requires querying the last few minutes across every service with no sampling.
- Typical scale in this pattern: 1–100 TB/day, short hot windows, long tail retention.
No incumbent identified
Find the incumbent before building the frame. Without a boundary statement, the proposal reads as duplicate spend.
Proof to lead with
- Cloudflare: One of the largest public examples of ClickHouse running customer-facing analytics at internet scale.
- Lyft: Large-scale marketplace operations backed by real-time analytics rather than batch reporting.
- Gala Games: Game economy and platform telemetry analysed in real time.
Business outcomes to name
- Material reduction in observability run rate
- No sampling means faster root cause during incidents
- Cardinality freedom for per-customer and per-tenant debugging
Composed from: use-cases, competitors, customers.