Question + risk triage
Cluster comments and DMs into questions, objections, proof requests, risk and language themes. Humans approve taxonomy changes and public response.
AI Deployment · separate practice
Make the invisible work faster. Keep the public judgement human.
The useful AI work happens behind the feed: source retrieval, transcript processing, asset tagging, comment classification, research queues, versioning and QA. Public claims, point of view and approval stay with named people.
Governed sourcesHuman approvalClient-owned logsScoped per deployment
Governed queue · liveComment: “Does this work with the product we launched last quarter?”
Classify → retrieve approved claim → draft answer → route reviewer → publish or decline → log decision.
The model can assist. The named owner decides.
01 · Where AI belongs
Use models where latency and repetition are the problem.
The deployment begins with the work that eats time: finding the right source, coding hundreds of comments, turning one recording into searchable material or keeping claims current across a large asset library.
01ListenCollect.Comments, DMs, transcripts, analytics and source material enter a governed queue.
02ClassifyStructure.Questions, objections, proof gaps, risk and repeated language receive reviewable tags.
03RetrieveGround.Approved claims, product facts and prior decisions become easy to find.
04AssistDraft.Variants, outlines, summaries, cuts and responses are prepared for review.
05ApproveDecide.A named person owns truth, voice, risk and final publish authority.
02 · Source boundary
A fast answer is still wrong when the source is wrong.
The deployment needs an explicit permission model. Public documents, private client knowledge, regulated claims, stale product notes and confidential records cannot be treated as one undifferentiated corpus.
ApprovedProduct factsCurrent spec, release notes, public claims, named owner.
ConditionalCustomer languageConsent, privacy and intended reuse recorded.
RestrictedRegulated claimsSource, reviewer and expiry date required.
ExcludedPrivate or stale materialNever retrieved into public-draft workflows.
03 · Classification desk
Turn a thousand comments into the five things the programme can act on.
The model helps structure the queue. The taxonomy stays readable to the people who have to make the next decision.
Community queue · 1,284 itemsRepresentative interface
Incoming“Is the new version safe to use after retinol nights?”
Detected: product-fit question · specialist review required · repeated 34 times · source freshness check pending.
412Questionsbrief or answer
267Objectionsproof gap
91Risk itemsroute
38Repeated phraseslanguage bank
Human decisionPause draft generation. Route the claim to the approved specialist, then create a reusable answer only after the source and wording are cleared.
04 · Channel uses
The deployment changes by the work behind each channel.
Instagram
DM and comment intelligence.
Theme coding, claim retrieval, creator asset tagging and recurring question detection.
LinkedIn
Source-backed executive drafting.
Interview retrieval, proof registers, reference checks and institutional voice separation.
YouTube
Transcript and library intelligence.
Chapter candidates, source search, clip discovery, catalogue tagging and claim QA.
X
Live source retrieval.
Watchlists, source classification, reference retrieval, correction logs and response routing.
05 · Governance
Plausible is not publishable.
Every assisted workflow needs an inspectable source, permission, prompt or instruction, reviewer and final decision. The audit trail is part of the product.
Sources
What was allowed in?
Corpus, freshness, access boundary and excluded classes.
Generation
What did the model do?
Task, prompt/instruction, model version and retrieved material.
Review
Who checked it?
Named owner, claim check, voice check, specialist review when needed.
Decision
What shipped?
Final copy, rejected variants, escalation and the reason for the release decision.
06 · Deployment path
Start with one expensive workflow, then prove the gain.
A broad AI transformation is hard to evaluate. A bounded workflow has a baseline, a reviewer, a latency measure and a clear failure mode.
Phase 01
Baseline the work.
Time, rework, error classes, owner, source systems and the parts that cannot be delegated.
Phase 02
Build the governed assist.
Source permissions, retrieval, taxonomy, prompt/instruction, review queue and logging.
Phase 03
Evaluate against the human baseline.
Latency, correction rate, source fidelity, reviewer load and the public-quality threshold.
06A · Evaluation desk
A workflow earns automation only after it beats the current process on the failure that matters.
Speed alone is a weak deployment case. We define a human baseline, a set of known hard examples, the mistakes that would block release and the reviewer effort the workflow still needs. The model can change later; the evaluation contract remains.
Evaluation matrixExample dimensions · thresholds set per workflow
Dimension
Question
Failure example
Release action
Source fidelity
Can every material claim be traced?
Plausible claim with no approved source
Block / retrieve again
Classification
Did the item enter the right queue?
Safety complaint tagged as ordinary feedback
Escalate / correct taxonomy
Voice
Did the assist preserve meaning and boundary?
Adds certainty the source did not contain
Reject / revise instruction
Reviewer load
Did the workflow remove work or create checking?
Fast draft requires line-by-line reconstruction
Do not automate this step
Pilot menu
Start where repetition is expensive and judgement can stay visible.
Source retrieval
Find the approved note, transcript, filing or research fragment behind a draft. The assist shortens retrieval; the named author owns the judgement.
Transcript intelligence
Search hours of recordings for thesis turns, proof moments, clean clip boundaries and recurring audience questions without pretending the extraction is the edit.
Source watch
Watch approved primary feeds, route material changes and prepare a source packet. Publishing remains behind the live-desk verification and risk lane.
07 · Commercial shape
Scoped per deployment.
The work depends on the source systems, permissions, integrations, review complexity and the number of workflows. We price after the workflow map, not before it.
A useful first deployment
+One painful workflow with a measurable baseline
+Approved sources and named reviewers
+One launch owner and a bounded set of outputs
+Clear success and stop conditions
Bad starting point
—“Automate content” with no workflow or owner
—No source permissions
—Public auto-publishing without a review gate
—No human baseline to evaluate against
08 · Questions
The model is not the approver.
Do you auto-publish AI output?+
No. Public output stays behind an approved human release gate.
Can you work with our existing models and tools?+
Yes, where the toolchain supports the required permissions, retrieval, logging and review. The deployment is designed around the workflow rather than a preferred vendor.
What do you measure?+
Workflow-specific measures such as latency, rework, correction rate, source fidelity, reviewer load and public-quality pass rate.
Who owns the workflow and logs?+
The client. Source libraries, taxonomies, templates, decision logs and approved outputs remain in client-controlled systems.
Evaluation desk
A workflow is ready only when the people using it can see where it fails.
Before production use, compare model-assisted work against the current human baseline. Measure retrieval accuracy, unsupported claims, edit distance, review time, escalation rate and whether the output changes the intended action.
Evaluation · response workflow v0.8Representative interface
Test set120 historical customer questions with approved source answers.
Run the same set through retrieval, draft and routing. Reviewers score source match, unsupported addition, tone drift, risk lane and total correction time.
96%Source matchtarget threshold
<2%Unsupported claimstop condition
-41%Review timevs baseline
100%Risk routedrequired
Release gateShip the workflow only when the failure modes are visible, the reviewer knows the escape hatch and reverting to the manual path is easy.
Integration map
Keep the model replaceable. Keep the operating record.
The durable asset is the source library, permissions, taxonomy, evaluation set, decision log and integration contract. Models can change without forcing the social operation to forget how it works.
01SourcesKnowledgeDAM, drive, product docs, claims library, transcripts and approved public sources.
02SignalsInputsComments, DMs, analytics, review themes, briefs and current platform observations.
03WorkflowAssistClassification, retrieval, summarisation, variant prep, tagging and queue prioritisation.
04OwnersApproveNamed reviewer, risk lane, source receipt, decision state and exception handling.
05RecordLearnVersion, evaluation result, publish outcome and the next workflow change.
AI Deployment
Bring the workflow that eats the most hours.
We will show where a governed model helps, where it does not, and who signs off.