By Sagar Shankaran, Founder of CallSphere
Why a registrar's object files can't go to the cloud, and how on-premises AI in 2026 searches 40 years of loan, condition and consultation records in-house.
Key takeaways
It is 8:40 on a Tuesday in March. The registrar at a 40,000-object county history museum has a lender's registrar on hold. Before that lender countersigns the outgoing loan agreement she wants three things: has the 1890s coiled basket ever traveled, what did the condition report say when it came home, and who noted the flaking on the rim.
The answers exist — in a manila object file in the fourth cabinet, in a condition report typed on a Selectric in 1987, and in an email from a preparator who retired in 2014. Your registrar will find all three. It will take her most of the morning, and the exhibition designer loses a day waiting on a case layout that depends on the answer.
Every museum person reading this knows the obvious fix: scan the object files, drop them into one of the AI search tools everyone has been pitched since 2024, ask questions in plain English. People have tried it. Then the collections committee, or the parent university's counsel, or the tribal historic preservation officer you have consulted with for two years asks the question that ends the project: where does that file go when the search tool reads it?
Museums are unusual in how much of their paper is contractually restricted — not "sensitive" vaguely, but restricted by a document somebody signed.
Here is the plain version of what shifted. On-premises AI means the program that reads your files runs on a computer your museum owns, in your own building, so the object file it reads never travels to anyone else's server. Nothing leaves, no copy exists elsewhere, and the clause in the loan agreement stays satisfied because nothing was transmitted to anybody.
The workaround has three parts. First, the collections system — TMS Collections, PastPerfect, Argus, CatalogIt — holds the tombstone record: accession number, maker, dimensions, credit line, location. It does not hold the 1987 condition report, the courier's note, or the 1996 letter from the donor's daughter explaining what the basket was for.
Second, a shared drive holds a folder called something like Collections\Object Files\SCANS - DO NOT MOVE, where the naming convention changed three times under three collections managers. Searching it returns nothing or four hundred results.
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Third, and this is the real system, there is a person. Everyone has a Marilyn — the long-serving collections manager, the volunteer archivist who comes in Thursdays. When she retires, four decades of context walk out with her, because none of it was ever written down anywhere a search box could reach.
flowchart TD
A["Curator asks: has this basket ever been loaned out?"] --> B["Local search reads 41 years of scanned object files"]
B --> C{"Is the record NAGPRA-restricted?"}
C -->|Yes| D["Result withheld; flagged to the tribal liaison, consultation log noted"]
C -->|No| E["Draft answer with accession number and file page cited"]
E --> F["Registrar checks the cited page against the paper file"]
F --> G["Answer emailed to the lender; nothing left the building"]
Two things, both unglamorous. The chips got good enough, and the enterprise world stopped treating on-site as a compromise.
On the hardware side, Qualcomm's Dragonwing-class processors and their peers made local processing practical on a machine that fits on a shelf, draws ordinary wall power, and does not need cooling that would make your facilities manager laugh. In 2024 the on-site version meant a rack, a serious power budget, and a consultant on retainer. In 2026 it means a box the size of the DVD player in your AV closet.
On the credibility side, large enterprises now choose on-site for control and data protection rather than settling for it. Cisco is rolling a personal AI agent out to roughly 90,000 employees with an explicit on-premises emphasis, because they want sensitive material to stay inside — which is your answer when the board chair asks whether this is fringe. It is also cheaper at volume: local processing runs roughly 90% below cloud pricing for repetitive work, and reading thirty thousand scanned pages is repetitive work. That question killed a lot of good projects in 2024 and 2025; in 2026 it finally has an answer a registrar can live with.
Over a few weekends the collections team scans the object files — accession paperwork, condition reports, courier reports, correspondence, old exhibition checklists — and exports the notes fields from the collections system onto the local machine. Nothing is uploaded anywhere.
At 8:41 the registrar types: Has accession 1974.112 traveled, and what did the last condition report say about the rim? Ninety seconds later she has three cited passages — the 1987 report page, the 2003 loan file, the courier's note — each with a file name and page number she can pull from the drawer. She verifies one, quotes it to the lender, and the agreement is countersigned before lunch.
The second use surprises museums more. The AAM General Facility Report is the document every serious lender asks for, and its environmental section is a slog: temperature and relative humidity by gallery, light levels, HVAC run hours, pest monitoring. Your loggers — Conserv, HOBO, whatever the conservator specified — already export that data. The local machine reads a year of it and drafts the section; the facilities manager corrects rather than writes at 11 p.m. the night before a deadline.
Illustrative numbers for a mid-size museum with a registrar, a collections manager and a part-time archivist.
| Line | Assumption | Amount |
|---|---|---|
| Time hunting in files | Registrar + collections manager, 5 hours/week combined | 250 hours/year |
| Loaded hourly cost | $38/hour including benefits | $9,500/year |
| Realistic reduction | 60% of lookup time, not 100% | 150 hours = $5,700/year |
| The box | One-time hardware and setup | $9,000 once |
| Running it | Power, backup, annual service | $1,200/year |
| Scanning | Volunteer and intern hours, 8 months | Sunk, already budgeted |
| Year one | $5,700 saved − $10,200 spent | −$4,500 |
| Year two onward | $5,700 saved − $1,200 spent | +$4,500/year |
Two-year payback on hours alone — and hours are the weak part of the case. The stronger part is the loan you did not lose because the facility report went out on time.
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The box drafts; it does not sign. Nobody should let a machine countersign a deed of gift, approve an outgoing loan, or set a declared value. Those carry the registrar's signature and the director's authority, and an auditor wants a human name on them.
Provenance conclusions stay human, full stop. A gap in ownership history between 1933 and 1945 is a research problem with legal consequences under the HEAR Act, and a summary reading "no red flags found" is worth nothing. The search finds documents; a provenance researcher decides what they mean. NAGPRA consultation is likewise a relationship, not a query — the machine can help you find every mention of a burial context across forty years of field notes before a meeting, but it cannot sit in the meeting, and no tribal representative wants it to.
Handwriting from a 1930s accession ledger still gets misread, so check accession numbers digit by digit. And keep cloud tools for everything public — website copy, collections-online records, membership emails.
The Monday step: pick one drawer — outgoing loan files from the last five years, because that is the one people search most. Scan it, load it, ask three questions you already know the answers to. If it gets all three right with correct page citations, scan the next drawer.
No, and museums that try to do it all first usually stall out. Start with the series pulled most often — outgoing loans, or the last decade of condition reports. A few thousand pages proves whether answers come back with accurate citations; the rest happens at intern speed.
It does not. The local machine reads exported files and scans; it does not need wiring into your collections system, and you do not want it writing into that system anyway. Export the notes and provenance fields, put them beside the scans, and keep the collections system as the record of authority.
That the machine is in your building, on your network, with no outside connection — and you offer to show them the closet. That answer is the entire point of this approach. Then put it in writing in the consultation record, because the promise matters more than the hardware.
If that person already keeps the ticketing terminals and camera recorder alive, yes. One machine to patch, one backup to check, one service agreement — and budget for the service agreement, because the failure you care about is the box dying the week before a loan deadline.
One last thing that sits beside this work rather than inside it. While the registrar is buried in the object file, the front desk phone is ringing about parking, stroller policy, and whether Saturday's timed tickets are gone. CallSphere builds AI voice and chat agents that answer those calls and web chats around the clock, book the visit, and pass real inquiries to a human — so the collections office stays on the loan instead of covering the switchboard.
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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