This page is the deliverable, read before you buy it.
AnswerEcon is a fixed-scope audit of your knowledge corpus. Below is a working specimen of what you receive: your maturity placement, your coverage against real ticket themes, your machine-ready percentage, and the roadmap, as a Context Readiness Report and an executive readout all based on the philosophies in the Answer Economics essay.
This page is the deliverable.
# the report's front matter: every CRR ships with one
--- report: crr-2026-041 series: context-readiness-report status: specimen # figures illustrative, structure real issued: 2026-08-11 inputs: [corpus, ticket-themes] findings: 4 deliverables: [context-readiness-report, executive-readout] standard: https://machinereadyknowledge.com/mrk-1.0.schema.json doctrine: https://contextkeeping.com/answer-economics
Machine-readiness
STATUS · PARTIALThe reader of your knowledge is increasingly a machine assembling an answer for a human who never sees the article page. Measured against the four clauses of Machine-Ready Knowledge:
# scored against mrk-1.0 · machinereadyknowledge.com
Analyst's note: 31% is a typical first-audit figure, not a failing grade. It is, however, the honest denominator your AI answers draw from today.
clause_scores: # percent of articles passing each MRK-1.0 clause self_contained: 44 applicability: 31 stable_ids: 62 freshness: 27 # weakest clause first: that's phase 1 of the roadmap
Coverage vs. actual ticket themes
3 GAPS FOUNDA corpus audit means nothing against an imaginary workload. We cluster your actual ticket themes and score coverage against each:
# themes clustered from 1,172 tickets/mo · coverage per cluster
| Ticket theme | Vol/mo | Coverage | Observation |
|---|---|---|---|
| Auth & API keys | 412 | READY | strongest cluster; citations resolve |
| Billing & proration | 287 | PARTIAL | plan tiers unstated |
| Data export | 198 | GAP | top unanswered cluster, 17 questions/wk |
| SSO & provisioning | 154 | PARTIAL | last verified 14 months ago |
| Rate limits | 121 | GAP | lives in one engineer's head |
SPECIMEN: figures illustrative. Your report is built from your ticket data.
Maturity placement
SELF-ASSESSSix levels, from institutional knowledge to agentic loops. You cannot skip levels. Place yourself:
# interactive placement: selection updates the record below
L02 → next ring is L03. Your capture loop is the bottleneck: AI drafting with a human gate takes capture from 20 minutes to under one. That's the move, not a chatbot.
What the findings are worth
VALUEEvery deliverable exists to move a number you already report, measured in blended cost per answer, not deflection.
# value model, as data, basis: blended cost_per_answer, never deflection
The honest denominator: know exactly what fraction of your corpus your AI can safely answer from, per clause, before you buy another AI layer.
Placement precedes prescription: invest where the bottleneck actually is, not where the vendor says it is.
Your top unanswered cluster, named. In the specimen: data export, 198 tickets a month, 17 unanswered questions a week.
Phase 1 chosen by data, not opinion: the specimen's freshness score of 27 makes the first move obvious.
The business case, pre-written for your leadership, including why assisted metrics invert as shift-left succeeds.
value_model: # specimen: illustrative rates; your report computes from your ticket data basis: blended-cost-per-answer # not deflection example_cluster: data-export tickets_per_month: 198 move: specialist-1to1 to self-serve payback: audit fee recovered inside month 5 # one cluster alone deliverable_value: - context-readiness-report: honest denominator for AI answers - maturity-placement: invest at the actual bottleneck - coverage-audit: top unanswered cluster, named - roadmap: phase 1 chosen by data - executive-readout: business case pre-written
Fixed scope. Fixed price. 30 days.
QUEUE CLOSED · WAITLIST OPEN- Context Readiness Report: the full written findings, yours to keep
- Maturity placement: across six dimensions, evidence attached
- Coverage audit: against your actual ticket themes
- Machine-ready %: measured against the open standard, not estimated
- Executive readout: the roadmap, prioritised and sequenced
AnswerEcon runs one engagement at a time. The queue is currently closed, join the waitlist.
"The specimen above is the real instrument: twenty-plus years in the knowledge industry, applied to your corpus."
Jason O'Donnell · author & practitioner
# the engagement, as data
engagement: name: answerecon scope: fixed price: fixed duration_days: 30 deliverables: - context-readiness-report # the full written findings - maturity-placement # six dimensions, evidence attached - coverage-audit # vs. your actual ticket themes - machine-ready-percent # measured against the open standard - executive-readout # the roadmap, prioritised operator: name: Jason O'Donnell role: author & practitioner years_in_industry: 20+ queue: closed # one engagement at a time waitlist: hello@contextkeeping.com # subject "AnswerEcon waitlist", or the form on this page discipline: https://contextkeeping.com standard: https://machinereadyknowledge.com