# The Client Playbook: Six Steps

- Link: https://jcody.com/ai-projects/client-playbook/
- Published: 2026-11-10T10:34:41-05:00
- Author: Jeffrey Cody

[AI Projects](https://jcody.com/ai-projects/) › Client playbook

**If a client handed me their data estate tomorrow,** whether a marketing department’s
media library, a manufacturer’s engineering shares or an agency’s twenty years of
drives, this is the engagement. Every step has already been run at scale, on worse
hardware.

## Six steps

### 1. Week 1: rules of engagement and a metadata map

Agree the risk ladder and approval format before touching anything. Mount every 
source read-only. Walk metadata only: names, sizes, dates. **Deliverable:** a sized
estate map and a schedule set by the slowest device.

### 2. Weeks 1 to 3: fingerprint everything, in parallel

One hash pass per disk, all disks at once, written to a database in real time. Each
volume passes gates: counts match, every file is hashed, a random independent re-
hash agrees. Measured rates were 30 to 100+ MiB/s per spinning disk. **Deliverable:**
a live console the client can watch.

### 3. Week 3: the copy map and the risk register

Copies per content, single-copy exposure by owner and project, reclaim potential
in TB and dollars, and broken or unplayable files. This build found 1,517 videos
missing their index atom. **Deliverable:** a ranked work order, not a slide. See
[the copy map](https://jcody.com/ai-projects/copy-map/).

### 4. Week 4: protect, then reclaim

Copy single-copy data to a second home first, verified by hash. Pick golden records
by health and completeness. Reclaim only where a hash survives on at least two other
disks, with written approval for each batch. **Deliverable:** a trim manifest and
a ledger of every removal.

### 5. Weeks 4 to 6: compress with lineage and gates

GPU encoding with triage rules, quality gates and a metadata parity gate. Every 
compressed child is linked to its parent hash. **Deliverable:** space returned, 
with proof of quality.

### 6. Then: make it searchable

Hybrid retrieval over the clean library: path, transcript and visual similarity,
with every result keyed by fingerprint. Only after that, evaluate whether fine-tuning
adds value. **Deliverable:** a library a person or an agent can search without losing
track of identity.

## Weekly scorecard

 * Percent of bytes fingerprinted.
 * Single-copy terabytes, trending down.
 * Verified reclaim, in terabytes and dollars.
 * Defects caught before damage.
 * Rows changed without approval (target: zero).
 * Owner hours spent. The walk-away ratio so far is 30 minutes of decisions for 
   4.5 hours of safe work.

## What I need from you

 * Read-only access to the sources, and a named person who can approve.
 * A rough list of what matters most to the business.
 * Patience for week 1, because I will ask a lot of questions before I touch anything.

## What is not claimed

Retrieval is designed, not in production. Fine-tuning is deferred. The agent message
bus is designed, not built. The GPU lane is on hold. Client names are not published.

[Tell me what you are sitting on](https://jcody.com/contact/?need=ai-projects)

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