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labs@atheron:~/industries/public-safety/ai$ train agent --grounded

AI for report checks

An on-premise assistant that comments and never writes.

In public safety, AI has to be narrow, private and accountable. A report checker that compares a report with your standards and points out what is missing saves supervisors time without writing a word of the officer's account.

// what matters here

What is different in this industry.

01

Comments, never writes

The assistant points out missing elements and inconsistencies. The officer writes and changes the report, and remains its author.

02

Nothing leaves the building

An open-source model on a GPU server inside your agency, with no calls to outside services, so reports stay on your network.

03

Your standards, cited

Every comment refers to the policy or standard it comes from, so an officer can see why it was raised.

04

Measured on returned reports

Past reports supervisors returned become the evaluation set, run before every change and reviewed with your training unit.

// 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

On-premise report checker

Hypothetical. Not a client, not a result.

// The problem

Supervisors return a large share of reports for missing elements, and officers wait days to find out a report needs a change.

// What we would build

An assistant that checks a report against the service's own report standards before it is submitted, pointing out missing elements and inconsistencies for the officer to fix. It never writes the narrative. It runs on an open-source model on a GPU server inside the service, so no report leaves the building.

// What is in it

  • Your report standards and policies indexed as the reference
  • Missing elements and inconsistencies pointed out before submission
  • The officer writes and changes the report; the assistant only comments
  • An open-source model on a GPU server on premises, with no outside calls
  • An evaluation set from past returned reports, run before every change

// Stack

  • Llama or Mistral (open-source)
  • vLLM
  • PostgreSQL with pgvector
  • Next.js
  • Lenovo GPU server on premises

// estimate

Build
≈ US$112,000 to US$171,000, delivered within 22 weeksCAD 159,800 to 242,800
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 3.9 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
0.4 to 0.9 weeks
Penetration test findings closed
0.9 to 1.4 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
Independent penetration test and retest
2 to 4 weeks

How it is paid

Deposit 20%
CAD 31,900 to 48,500
Discovery 1.7%
CAD 2,711.50 to 4,122.50
Specification and evaluation plan 9%
CAD 14,355 to 21,825
Design approved 1.7%
CAD 2,711.50 to 4,122.50
Core features 5.2%
CAD 8,294 to 12,610
Full build 3.5%
CAD 5,582.50 to 8,487.50
Working pilot 27.2%
CAD 43,384 to 65,960
Testing and fixes 1.7%
CAD 2,711.50 to 4,122.50
Penetration test findings closed 8.7%
CAD 13,876.50 to 21,097.50
Launch 1.7%
CAD 2,711.50 to 4,122.50
Production 9.6%
CAD 15,312 to 23,280
Holdback, 30 days after launch (10%)
CAD 15,950 to 24,250
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 it use body-worn camera footage?

Not in this example. It checks the written report against your standards. Any use of video or transcripts would be scoped separately with your policy and privacy teams.

Why an on-premise model?

Reports hold sensitive personal information. Running an open-source model on your own server keeps it on your network and under your control.

Who decides whether the assistant is used?

Your agency, after reviewing the evaluation results and its own policy on AI. We provide the results and the documentation.

// 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.

On-premise AI assistant for checking police and incident reports | Atheron Network Labs