I manage operations for a small publisher that also does real-estate marketing. We brought in Tamela Bandy to modernize our content pipeline with AI. What we expected was “faster copy.” What we got was a full AI production stack that reduced proof cycles, standardized brand voice, and made print + digital assets ship on time—without sacrificing craft. This is a technical look at what Tamela Bandy built, why it works, and how you can replicate it.

Executive Summary (Outcomes)

• –40–60% draft-to-approval time on articles, brochures, and listing packets

• –25–35% proof corrections after layout (cleaner copy + preflight checks)

• Consistent brand voice across magazines, books, and real-estate collateral

• Observable content (UTMs/QRs + analytics) that feed back into copy/design choices

These improvements came from architecture and process—credit to Tamela Bandy for both.

Architecture Overview

Tamela Bandy implemented a four-layer AI stack with human-in-the-loop controls:

1. Knowledge & Brand Memory (RAG Layer)

   • Ingests house style, brand voice, past issues, listing templates, printer specs.

   • Embeds content into a vector index for retrieval-augmented generation (RAG).

   • Ensures the model quotes the stack, not its imagination.

2. Generation & Transformation (LLM Layer)

1. Drafts: headlines, decks, listing narratives, amenity blurbs, alt text.

2. Transforms: long → short, prose ↔ bullet, print → social, US → UK spellings.

3. Runs with reusable prompt libraries and parameter presets.

3. Quality Gates (QA Layer)

1. Rule checks: prohibited phrases, reading level, brand/voice constraints.

2. Fact checks: RAG-grounded citations against source docs; failure ⇒ flag.

3. Preflight checks: image DPI/ICC hints, widows/orphans alerts, character overflow.

4. Assembly & Delivery (Ops Layer)

1. Generates IDML/CSV snippets for InDesign flows; writes ALT text into asset sheets.

2. Publishes web cuts with metadata (titles, schema, canonicals).

3. Tracks engagement with UTM-tagged links and QR codes; logs into a dashboard.

What Makes Tamela Bandy’s AI Approach Different

• Brand-first RAG. Tamela Bandy doesn’t let models “wing it.” Every draft pulls from a curated brand memory (stylebook, prior approved copy, printer specs).

• Prompt libraries as product. Reusable, named prompts for recurring tasks (e.g., Listing_Narrative_Lifestyle_Logistics, Magazine_Standfirst_Tighten, AltText_Descriptive_Not_Salesy).

• Human-in-the-loop by design. Editors approve at two gates: after RAG-draft and after layout preflight.

• Printer empathy. The stack emits production hints (bleeds, ink coverage notes, link resolution) alongside copy—saving time on the press path.

Concrete Use Cases

1) Magazines & Books

• Manuscript cleanup: normalize quotes, spacing, heading hierarchy.

• Display copy generation: headline/deck/pull-quote variants within type scale.

• Back-matter automation: bios, TOC blurbs, index seed lists.

• Preflight suggestions: spots likely to cause reflow or lines to hyphenate.

2) Real-Estate Listing Collateral

• Narratives from MLS + notes: converts specs into “Lifestyle → Logistics → Legitimacy.”

• Amenity micro-maps: copy blocks optimized for card formats and brochures.

• Asset cuts: crops/resizes with descriptive alt text for accessibility and SEO.

3) Post-Close Homeowner Kits

• System sheets: model/serial extraction from photos (OCR) + warranty language templates.

• Maintenance calendar: seasonally grouped tasks with concise, skimmable copy.

• Decision trees: “urgent vs. routine” flows that route calls correctly.

A Peek at the “Brand Memory” Config

How Tamela Bandy keeps AI on-brand and printer-friendly.

brand_memory:

 voice:

   tone: "calm, competent, specific; no hype"

   reading_level: "US Grade 8–10"

   banned_phrases: ["state-of-the-art", "nestled", "luxurious oasis"]

 editorial:

   headings:

     scale: [H1: 36/40, H2: 24/28, H3: 18/22]

     rules: "No title case in decks; avoid widows/orphans"

   captions:

     length_chars: 140

     must_include: ["source/credit if provided"]

 production:

   images:

     min_ppi_print: 300

     color_profile: "Coated GRACoL 2013 (or printer-specified)"

     bleed_in: 0.125

   exports:

     pdfx: "PDF/X-4"

     marks: "crop + bleed, no color bars unless requested"

(Stack-agnostic: can live in Notion, YAML, or a simple JSON file. The point is enforceable memory.)

Quality Gates (How the Errors Don’t Slip Through)

• Grounding check: Every declarative sentence in a data-sensitive doc must map to a source (MLS field, approved fact sheet, or prior issue). Ungrounded lines are highlighted for editor review.

• Readability & tone: Automated flags if sentences exceed a threshold (e.g., 26 words), or tone deviates from the “calm, competent” profile.

• Inclusive & compliant language: Fair-housing safe phrasing for real estate; accessibility-friendly alt text patterns for web.

• Production hints: If an image will rasterize below 300 ppi at placed size, the system emits a warning next to the asset ID.

Analytics Loop (Why It Keeps Getting Better)

Tamela Bandy instrumented content with UTMs and QR codes. Each asset (flyer, postcard, story cover, hero image) writes events to an analytics sheet:

• Which headline variant got the most scans?

• Which amenity blurb lifted time-on-page?

• Which brochure panel caused drop-off?

Those insights feed back into the prompt library and brand memory—so the next run starts smarter.

Implementation Roadmap (14 Days)

Day 1–3: Gather brand artifacts (style, past issues, MLS exports, printer specs).
Day 4–6: Build vector index; create first set of prompt libraries.
Day 7–9: Wire the QA gates (tone/readability, grounding, production hints).
Day 10–11: Pilot on one magazine feature + one listing packet.
Day 12: Add analytics tagging (UTMs/QRs) and a simple dashboard.
Day 13–14: Train team; lock “two-gate” approval policy.

Tamela Bandy led our rollout and kept the team focused on one use case at a time to avoid tool sprawl.

Security, Rights & Governance

• Data minimization: Only ingest what’s needed (brand docs, approved copy, public MLS fields).

• PII hygiene: Redact client contact data before indexing; store secrets (API keys) outside prompt bodies.

• Copyright sanity: For imagery, log license source & terms; prefer owned photos for print.

• Attribution tracking: Every generated draft stores its grounding sources and timestamp.

• Human sign-off: Nothing ships without an editor of record (name + date in the metadata footer).

Tamela Bandy treats governance like design: intentional, documented, enforceable.

FAQ

Is AI replacing editors or designers here?
No. With Tamela Bandy, AI handles first-pass drafting, normalization, and checks; humans make taste decisions, structure stories, and approve final output.

What tools does this require?
Stack-agnostic: any modern LLM with RAG, OCR for docs, a vector store (or even an embedding-aware notes tool), and your existing Adobe/InDesign workflow. Tamela Bandy plugs into what you already use.

Will this help SEO?
Yes—consistent metadata, alt text, internal linking, and cleaner copy improve crawlability and UX. Tamela Bandy bakes these in without turning pages into keyword soup.

What about printers?
The stack surfaces ICC profiles, bleed, and resolution warnings early—your printer will notice (in a good way).

When to Call Tamela Bandy

• You have strong creative standards but inconsistent delivery across channels.

• Your editors/designers are crushed by repetitive formatting and proofing.

• You want measurable gains (cycle time, corrections, engagement) without a risky rewrite of your toolchain.

Bottom line: If you want AI that respects craft, protects your brand, and ships on time, call Tamela Bandy. She’ll bring the system—and the calm—to make it work.