By Sagar Shankaran, Founder of CallSphere
Why an AI model that “improves” a foreclosure notice costs a community weekly a republication and an affidavit — and what a newsroom-tuned model does instead.
Key takeaways
It is 9:10 on a Tuesday morning at a 6,400-circulation county weekly, and the legals clerk is retyping a mortgage foreclosure notice that a law office in the county seat sent over at 4:52 the previous afternoon. It runs 41 column inches, carries a legal description — "Lot 14, Block 3, of the Original Town of Fairview, according to the recorded plat thereof" — a sheriff's sale date, and a run instruction: publish three consecutive weeks commencing the week of August 10. She has done thousands of these. The publisher, who has been reading about AI all year, asks her to run the next one through a chat assistant to "clean it up first."
It comes back cleaner. It also comes back wrong. "Said premises" is now "the property." "To wit" is gone. The paragraph repeating the parcel number a second time — redundant to any editor, load-bearing to the statute — has been merged into the one above it. Nobody catches it until the attorney's paralegal calls Thursday. The notice has to run again from week one, the affidavit of publication that was already notarized is worthless, and the sheriff's sale gets pushed.
Local news and niche publishing runs on a private vocabulary that looks, to a general-purpose model, like a pile of typos. A trade-tuned model is an ordinary AI model that has been trained further on one industry's own documents, words and rules, so it treats "cutline," "agate," "draw" and "said premises" as the technical terms they are instead of as errors to correct.
Here is what a newsroom hands a model in a normal week, and what the untuned version does with it. "TK" in a draft means a fact is still coming — the general model deletes it or expands it to "to be confirmed," which then prints. "‑30‑" at the bottom of a story means end of copy; the model treats it as a stray number and strips it. "Cutline" gets rewritten to "image caption description," which breaks the paginator's search when she is placing photos in InDesign. A budget line — the one-sentence summary on the daily budget — gets treated as a financial figure. "6 col x 10.5" gets helpfully converted to inches. "Double truck" gets flagged as slang to remove. It does not know that a jump line has to match the hed on the jump page word for word, or that the flag is the nameplate on page one, not a warning.
On the business side it is the same story. "Draw" is how many copies you print. "Returns" are unsold single copies back from the racks. "In-county" is a postage rate class under the USPS rules governing your Periodicals permit, not a geography note. "Qualified requester" is the status a niche B2B title documents for a BPA Worldwide audit. Ask a general model to tidy your publisher's statement and it will smooth those into synonyms no auditor recognizes.
Most of this is annoying. One part is expensive. Public notices — foreclosures, sheriff's sales, delinquent tax lists, probate notices, municipal minutes, bid solicitations, sample ballots — are set by statute in every state and are a meaningful share of gross revenue at most community weeklies. The wording is not editorial. It is a legal instrument your paper republishes and then certifies with a notarized affidavit of publication saying this exact text ran on these exact dates in a newspaper of general circulation.
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Change a word and you have certified something untrue. In practice: rerun the notice at your own cost, void and reissue the affidavit, absorb the delay in the underlying legal proceeding, and explain yourself to the attorney's office that sends you 30 percent of your legals. The qualification itself — continuous publication, paid subscribers, the Periodicals permit — is what makes your paper eligible for that revenue. It is not a place for a model to exercise judgment.
flowchart TD
A["Attorney emails notice at 4:52pm"] --> B["Legals clerk sets it in the ad system"]
B --> C["Trade-tuned check: statute wording, run dates, legal description"]
C --> D{"Any statutory text altered?"}
D -->|Yes, flagged| E["Clerk restores original wording"]
E --> C
D -->|No| F["Publish 3 consecutive weeks"]
F --> G["Affidavit of publication generated and notarized"]
Through 2024 and 2025 the answer to a model that did not know your words was to retype your words in front of it every time — a house-style sheet pasted into every session, which nobody on deadline keeps doing. In 2026 vertical models trained on a single industry's own material became a distinct category of product rather than a research curiosity. The reason is unglamorous: general models miss the vocabulary, the units, the abbreviations and the edge cases a trade takes for granted, and the misses cluster exactly where the money is.
Two other things made it practical for a shop your size. Running these models got roughly ten times cheaper than in 2025, so you can afford to pass every legal notice, obituary and classified line ad through a check instead of spot-checking. And the goal-driven products — Claude Cowork, launched 12 January 2026, and ChatGPT Work, launched 9 July 2026 — are built for people who are not developers. Your legals clerk can set this up, not a consultant.
The useful setup for a paper is narrow: the model may compare, flag and format. It may not rewrite statutory copy. It reads the incoming notice against the version you set, tells you which characters differ, checks the run dates against the statute's required number of consecutive weeks, verifies the legal description matches the attorney's email exactly, and confirms the column inch count you will bill from.
Assume a weekly that runs 22 public notices a week at a statutory rate of $8.50 per column inch, averaging 16 column inches each. That is roughly $3,000 a week in legals, about $150,000 a year — ordinary for a county seat paper. Now assume the error rate on manual setting and re-keying is two-tenths of one percent of notices, which is about one bad notice every ten weeks.
| Line | Assumption | Amount |
|---|---|---|
| Notices per year | 22 per week × 50 weeks | 1,100 |
| Bad notices per year | 0.2% error rate | 2.2 |
| Republication cost each | 16 col in × $8.50 × 3 weeks, absorbed | $408 |
| Clerk and editor time each | 3.5 hours at $24 loaded | $84 |
| Affidavit reissue and courier | notary, mailing, follow-up | $45 |
| Direct cost per year | 2.2 × $537 | $1,181 |
Eleven hundred dollars will not save your year. The number that matters is the one you cannot table: the law firm that quietly sends its notices one county over. At 16 column inches, three weeks, $8.50 an inch, a single firm sending 40 notices a year is roughly $16,000 of your highest-margin revenue. That is the real return on a check costing a few dollars a month.
The model does not decide whether your paper qualifies as a newspaper of general circulation under your state's statute. It does not decide whether a notice that arrived Monday at 4:52 can still make Wednesday's paper — that is a press-time judgment involving the paginator, the plates and a driver. It cannot sign an affidavit; a person with a notary seal does that, after actually looking at the printed page.
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It will also be confidently wrong about local proper nouns: township and cemetery names, the spelling of a family that has been here a hundred and forty years, the two Millers on the school board. Those go in a house list a human maintains. And no model touches the one thing your paper sells that nobody else can: the editor's judgment about what leads page one.
Do not start with the newsroom. Start with 40 legal notices you already published. Feed each one to the tuned model with the attorney's original email beside it and ask one question: does the published text differ from what was submitted, and where. You will likely find two or three discrepancies from the past year that nobody caught, which is both alarming and exactly the point. That is your baseline, it took an afternoon, and it tells you whether to turn the check on.
For editorial copy, often yes — hed and dek style, cutline format, datelines. For statutory copy, no, because the failure mode is not that it forgets your style, it is that it thinks it is improving legal wording. You want a setup that is structurally forbidden from rewriting the notice, not one that has been asked nicely.
The check runs beside your system, not inside it. Your clerk still sets the notice where she always has; what changes is that the submitted file and the set version get compared before it goes to the page. If your vendor offers a tuned assistant inside the product, ask one question: what happens when it disagrees with the attorney's wording. If the answer is "it corrects it," walk away.
Same shape, lower stakes, higher volume. Obituaries carry their own vocabulary that general models mangle: "preceded in death by," "in lieu of flowers," visitation versus service, and the funeral home's billing account. A tuned setup keeps the family's wording intact and only checks the fields you bill from — word count, photo yes or no, run dates.
It helps most where copy is repetitive and rule-bound: meeting stories built from agendas and minutes, police blotter items, calendar listings, real estate transfers, court dispositions — the pieces your one full-time reporter resents most. Feature writing and interviews are the job; protect the hours they take.
The other cost buried in legals is the calls: attorneys' assistants and municipal clerks asking whether the notice ran, what the deadline is for next week, and where the affidavit is. Those come in at 8:30 in the morning and 4:45 in the afternoon, exactly when your front counter is thinnest. CallSphere builds AI voice and chat agents that answer the business line, take the details and capture the request 24/7, so the clerk can call back with an answer rather than fielding the same three questions all day. The statutory wording still belongs to your clerk and the affidavit still belongs to your notary — the agent just stops the phone from setting your afternoon on fire.
Written by
Sagar Shankaran· Founder, CallSphere
LinkedInSagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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