Add the AI axis: plain English as an option, analysis as the end state

Cost is the argument against Splunk and control is the argument against
Cribl. AI is the third, and the difference there is not a feature
comparison, it is where the model runs.

Plain-English querying shipped in Phase 7 and stays an option rather than
a replacement for writing a query: every generated query compiles through
the same IR and executor as a hand-written one, with the same tenant
scoping, cost guardrails and audit logging. The model suggests, it does
not get a private path to the data.

AI-assisted analysis and explanation is the end state and is not built.
Authoring answers "how do I ask this"; the valuable question is "what
does this mean" -- what changed in a result set, why an alert fired and
what preceded it, summarising an incident from the records around it.
Recorded on the status page as an end-state goal rather than a numbered
phase, because it is a property the product keeps rather than a thing to
finish and tick off.

Local is the non-negotiable part, and it is worth stating as position
rather than as a bullet: the default runs qwen2.5-coder through Ollama on
the customer's own hardware, Apache-2.0 weights chosen so Phase 6's
licence work survives contact with the model, and the cloud adapter is
opt-in and off by default. Logs are the most sensitive unstructured data
most organisations hold -- credentials in stack traces, customer
identifiers, internal topology -- so an assistant that reads them is
either running where the data already is, or it is a data-egress decision
wearing a helpful interface.

The constraint it imposes is stated too, because it bounds what can be
promised: a 7B model on a customer's hardware will not match a frontier
model, and the honest claim is not that it is as clever but that it is
good enough at a bounded task and runs somewhere you control. Analysis
features have to be designed to that budget rather than assuming an API
is one call away.

docs/status.md

@@ -24,6 +24,7 @@ verification procedure and its results.
 | 9 | Routing: multiple destinations and delivery guarantees | Planned |
 | 10 | Archive and replay, retention and tiering | Planned |
 | 11 | Fleet: central agent configuration | Planned |
+| — | AI-assisted analysis and explanation, on a local model | End-state goal |
 
 Phases 8-11 are a second axis rather than a continuation of the first:
 0-7 built the destination, and those four build the road to it. The