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Private AI for Nations

Funder reports prepared on a server the Nation owns.

Funder reporting takes staff away from programs. An assistant can prepare the sections that repeat every quarter from your own records, but only if the community stays in control of that data. So it runs on a server the Nation owns, and staff edit and approve everything it prepares.

// what matters here

What is different in this industry.

01

Runs on the Nation's server

An open-source model on a GPU server the Nation owns, with no calls to outside AI services, so community data never leaves.

02

Staff approve every word

The assistant prepares; staff edit and approve. Nothing is sent to a funder without a person's sign-off.

03

Built from your records

Figures come from program records, and narrative sections cite the records they draw on, so staff can check them.

04

Your rules on what it may read

The assistant reads only the records your data governance allows, and sensitive records can be excluded entirely.

// example projects

Priced examples for this service.

Each one is hypothetical, labelled as such, and priced live from our rate card. Open any of them in the estimator and make it yours.

Example projectWeb appAIOpen-source model

Private funder reporting

Hypothetical. Not a client, not a result.

// The problem

Program staff spend days each quarter rebuilding the same participation and spending figures in a different template for each funder.

// What we would build

A reporting workspace that builds each funder's figures from program records, and an assistant on an open-source model running on a server the Nation owns that prepares narrative sections from the Nation's own records for staff to edit and approve. No community data leaves the Nation's server.

// What is in it

  • Participation and spending figures built from program records
  • Templates for each funder, editable by staff
  • Narrative sections prepared from your own records, edited and approved by staff
  • An open-source model on a GPU server owned by the Nation
  • An evaluation set from past reports, run before every change

// Stack

  • Llama or Mistral (open-source)
  • vLLM
  • PostgreSQL with pgvector
  • Next.js
  • Lenovo GPU server owned by the Nation

// estimate

Build
≈ US$112,000 to US$170,000, delivered within 23 weeksCAD 159,500 to 242,400
Hosting
≈ US$176 a monthCAD 250 a month
Support
≈ US$2,500 a monthCAD 3,565 a month
Hardware
≈ US$47,800 to US$197,000CAD 68,000 to 280,800

Prices in your currency are estimates from today's Bank of Canada rate. All invoicing is in CAD or USD.

AI route
Open-source model
Model running cost
≈ US$176 a monthCAD 250 a month

Timeline by milestone

Discovery
2.9 to 3.4 weeks
Specification and evaluation plan
0.4 weeks
Design approved
2.4 to 3.4 weeks
Data governance rules approved by your council or committee
1.4 to 2.4 weeks
Core features
1.4 to 1.9 weeks
Full build
0.9 to 1.4 weeks
Working pilot
3.4 to 5.9 weeks
Testing and fixes
0.9 to 1.9 weeks
Launch
0.9 to 1.4 weeks
Production
0.9 to 1.4 weeks

Outside our hands, and added to the calendar

Hardware delivery, after it is ordered
2 to 6 weeks

How it is paid

Deposit 20%
CAD 31,840 to 48,420
Discovery 1.7%
CAD 2,706.40 to 4,115.70
Specification and evaluation plan 9%
CAD 14,328 to 21,789
Design approved 1.7%
CAD 2,706.40 to 4,115.70
Data governance rules approved by your council or committee 8.7%
CAD 13,850.40 to 21,062.70
Core features 5.2%
CAD 8,278.40 to 12,589.20
Full build 3.5%
CAD 5,572.00 to 8,473.50
Working pilot 27.2%
CAD 43,302.40 to 65,851.20
Testing and fixes 1.7%
CAD 2,706.40 to 4,115.70
Launch 1.7%
CAD 2,706.40 to 4,115.70
Production 9.6%
CAD 15,283.20 to 23,241.60
Holdback, 30 days after launch (10%)
CAD 15,920 to 24,210
GPU time for training and testing, at cost
CAD 300. Billed up front, at cost, outside the milestones
GPU server for AI, for the model
CAD 68,000 to 280,800. Billed up front, at cost, outside the milestones

// questions

Questions we get about this.

Does the assistant send our data to an AI company?

No. It runs on an open-source model on a server the Nation owns. No community data goes to an outside AI service.

Can it learn our language?

Language work belongs to the community and its language keepers. Any training on language materials would be scoped only with their direction and consent.

What if the assistant gets a figure wrong?

Figures come from your records, not from the model. Staff review every section, and errors found become test cases for the next change.

// next

Not quite your project?

Tell us what you have in mind. We will come back to you with a range and the questions that would narrow it. Or book a call and talk it through.

Private AI for funder reporting in Indigenous organizations | Atheron Network Labs