Managed AI for Columbus Nonprofits and Foundations
It is the second week of the month. A grant report is due Friday for a program that ended in March, the year-end appeal needs four versions for four donor segments, and the minutes from the last board meeting are still in somebody’s notebook. All three need the same paragraph about your organization, rewritten again to a different word count. The person doing most of that work also runs your social accounts.
Where it starts
The recurring work worth handing over first
Not the impressive demo. The jobs that come up every week and eat somebody’s afternoon, which is where the hours actually come back.
Grant proposals from your own boilerplate
A template holds your approved organizational background, program descriptions and outcome language. Point it at a specific funder’s questions and word limits and it returns a draft shaped to that application. Staff start from something on the page, and the background section stops being rewritten from memory every cycle.
Grant reports that match what you promised
The workflow reads the original proposal alongside your program data for the period, then drafts the narrative sections in the funder’s report format. It can be built to flag a missing number instead of filling one in, so the person signing the report knows what still has to be looked up.
Appeal and acknowledgment drafts by segment
One appeal becomes four versions for first-time donors, lapsed donors, monthly sustainers and the board, each carrying the same facts in different words. The workspace works from a description of the segment, so names and gift amounts stay in your CRM.
Board packets and meeting minutes
A recording or a set of rough notes becomes minutes in your format, with motions and votes separated from discussion. A second pass produces a one-page summary of what changed since the last meeting. The executive director reviews before anything is circulated.
Volunteer onboarding materials
One program manual becomes the role-specific one-pagers, shift instructions and question and answer sheets each volunteer group needs. When the program changes, the source document is updated once and the derived materials are regenerated, so the orientation packet stops drifting away from what the program actually does.
Impact and annual report language
Program documentation is pulled into consistent outcome language reused across the annual report, the website and the next three proposals. The value is the consistency: the same program described the same way everywhere a funder might read about it.
We run lean and every dollar is restricted or spoken for. What does this cost?
Managed AI is priced per seat plus usage. Each seat carries a monthly usage allowance. Above those sits one number you choose, the ceiling on total spend for the organization. A $250 monthly minimum applies. The published ranges are monthly totals for the whole organization: $250 to $500 for up to 10 seats, $500 to $1,175 for 10 to 25 seats, and $1,175 to $3,800 for 25 to 100 seats. There is no published per-seat rate.
The ceiling is the part a finance committee asks about. The line item cannot move without somebody inside your organization approving the move. A heavy month of use cannot produce a bill nobody approved.
Seats are per person, because that is what licensing is. You do not need one for everyone on the payroll. Count the people who spend the day in documents, email and spreadsheets: the executive director, development, communications, finance, program managers. Direct service staff working a shift, part-time program staff and volunteers usually do not need a seat. Where most of the staff deliver programs, the seat count lands well under the headcount.
Budget is allocated by team and department, never per person. Development carries an allowance, programs carry one, administration carries one. During a heavy grant cycle you can move more allowance to development for two months and move it back after. If you already budget by program and department, the allowance sits on a structure you have.
Our donors give because someone they trust asked them. Won’t AI make our letters sound like everyone else’s?
The honest answer is yes, if you point it at the wrong work. Ask a machine to produce feeling it does not have and you get copy that reads like every other appeal. So decide in advance what it does not touch, and write that decision down before anyone gets a login.
Our recommendation for this sector: keep the major gift ask, the condolence note, the handwritten thank-you from the executive director, and any story told in a participant’s own words out of scope. Those pieces carry the relationship, and producing them faster gains you nothing. Governance is how the line holds. The monthly report shows what kinds of work AI is being used for across the organization, so the rule is something you can check.
What it should touch is the work around those moments. The four segment versions of one appeal. The second and third drafts. The 250-word version of a program description that only exists at 800 words. The acknowledgment letters that have to go out this week and say the correct thing about the correct fund. A template holds your own approved language, so drafts come back in your organization’s words.
On donor data there is a control simpler than any policy. The workspace does not need your donor list to draft a lapsed-donor appeal. It needs a description of the segment. Names, gift amounts and contact details can stay in your CRM, where access is already controlled. For work that does involve your own documents, ScaleSight AI runs on a SOC 2 Type II audited platform, and anything built on those documents stays inside your workspace.
Why not just buy a few ChatGPT accounts and be done with it?
You can, and somebody on your staff probably already has. The monthly cost is small and the early results look fine. The problem arrives later. A template built straight onto one AI company’s model depends on that company holding its pricing and keeping that model available. When either one changes, the template gets rebuilt, and in a small organization that job lands on somebody who already has four others. Inside the ScaleSight AI workspace the same template is re-pointed at a different model and keeps running, and the person using it sees no change.
The second reason is that the best model depends on the job. Reasoning over a funder’s full guidelines, live web research on a foundation’s stated priorities, cheap bulk drafting of social captions and image generation are four different jobs, and a different company leads at each one. In the workspace each job is routed to whichever model does it best. The grants manager never has to know which model is running, or keep a mental list of which tool is good at what.
The third reason is turnover. Work done in a personal account stays in that personal account. When the development director leaves, the method she worked out for drafting a letter of inquiry leaves with her, along with the examples in her chat history. In one workspace the templates belong to the organization and survive the departure.
How does a shared library help us if it was built for other people?
Because most of this work is the same everywhere. Board minutes, meeting summaries, job descriptions, policy drafts, standard operating procedures and newsletter formatting look nearly identical at a food pantry, a trade association and a machine shop. Work of that kind is built once and given to every client. Anything built on your own documents, your own data or a process specific to you stays in your workspace and is never shared with another client.
New templates, agents and workflows ship into your workspace every month out of that shared library, so you inherit what was built before you arrived. Your grant boilerplate, your case for support, your outcome language, your segment definitions and the workflow your grants manager built last quarter stay with you. The test is plain: generic work goes into the library, your work stays with you.
For an organization with no line item for building software, that split is the point. The library is work ScaleSight AI has already built, and it arrives whether or not you could have funded building it. A ten-person organization gets the same templates and workflows as a hundred-person one.
What should be different a year from now?
Here is the outcome. In a year, the best AI ideas in your organization should come from your people, not from us. In practice that looks like the grants manager building their own report template without asking permission, and a program director rewriting the volunteer onboarding workflow because they can see what is wrong with the one they were handed.
The mechanism is ordinary. When somebody works out a better way to structure a program narrative, that method goes into the workspace as a template the rest of the staff uses. One person’s method becomes the organization’s method, and it stays after that person moves on.
When a job is specific enough to need building, that is an AI Build Project: scoped work with a start date and an end date that produces one workflow, template or agent. A grant report workflow that reads your original proposal alongside your program data is one example. Managed AI keeps it running afterward, re-points it when models change, and reports on how it is being used.
ScaleSight AI sits at 1733 W Lane Ave in Columbus, in the same office as TTS Cyber, its sibling managed IT and cybersecurity company under the Koine Cyber group. When a funder’s due diligence form asks about your IT controls, that is the desk it goes to. The service is built for nonprofits and foundations across Central Ohio.
Questions
Asked by nonprofits and foundations
What does ScaleSight AI cost for a nonprofit our size?
Managed AI is priced per seat plus usage, with a $250 monthly minimum. Published monthly totals for the whole organization are $250 to $500 for up to 10 seats, $500 to $1,175 for 10 to 25 seats, and $1,175 to $3,800 for 25 to 100 seats. Each seat carries a monthly usage allowance, and you set a ceiling on total spend, so the number cannot move without your approval.
Does every staff member need a seat?
Only the people who work inside the workspace need a ScaleSight AI seat, which in most organizations means leadership, development, communications, finance and program managers. Direct service staff working a shift, part-time program staff and volunteers usually do not need one. Seats are per person because that is what licensing is, while budget is allocated by team and department.
Can we use this to write grant applications?
Yes. A template holds your approved organizational background, program descriptions and outcome language, then reshapes them to a specific funder’s questions and word limits. A person still edits and signs the application. If a funder asks how AI was used, the monthly usage report gives you something specific to say.
What is the difference between Managed AI and an AI Build Project?
Managed AI runs all year. It gives the whole organization one secure workspace, seats per person with usage allowances set by team, monthly reporting on what kinds of work AI is being used for, and new templates, agents and workflows shipped in every month from the shared library, while anything built on your own documents or process stays with you. An AI Build Project has a start date and an end date and produces one specific workflow, template or agent. The managed service is the base, and a build project is added when a job turns out to be specific enough to need one.
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Tell us what your week actually looks like.
The readiness review starts by finding where AI is already being used in your company, what it is costing, and which recurring work is worth handing over first. You keep the findings either way.