Yes. AI can analyze police reports today, including old scanned and handwritten ones. It pulls out names, plates, addresses and dates, links them across sources, and answers your questions in plain language, citing the source document. What it can't do is replace an analyst's judgment. Treat what it gives you as a lead to verify, not a conclusion. Your agency also needs to keep control of the data and follow the rules that govern criminal justice information.
Isn't AI for police reports mostly about writing them?
Largely, yes. Search the topic and much of what you'll find is report-writing software. Axon's Draft One, announced in April 2024, drafts report narratives from body-worn camera audio, and an officer has to review and approve every draft. Axon also said U.S. officers can spend up to 40% of their week, or 15 hours, on what amounts to data entry. In early 2025, the DOJ's COPS Office named Axon and Truleo as the two companies then offering this kind of generative report tool.
In July 2025, the Electronic Frontier Foundation argued that Draft One didn't save the AI's first draft, making it hard to tell which words came from the AI and which came from the officer.
Writing new reports faster is a different job from making sense of the reports you already have. A police department or sheriff's office can have years of narratives in its records system, plus scanned incident reports, handwritten field notes, tip-line spreadsheets and call recordings. Connections between cases often sit in that backlog. That's where AI analysis, not AI writing, helps.
What can AI actually do with existing police reports?
Today, a well-built system can:
- Read documents nobody can search. Scanned PDFs, handwritten forms, Word files, audio and messages become structured data instead of images in a folder.
- Extract the details investigators care about. People, license plates, addresses, dates, locations, vehicles and incident types.
- Cross-reference across sources. It can find the same plate in a handwritten report from years ago, a recent records entry and a spreadsheet of tips, even if those systems were never connected.
- Answer questions in plain language. For example: "Which reports since January mention a white pickup near the 400 block of Main Street?" The answer should list the actual documents, not just a summary.
- Produce the output command staff need. Briefings, dashboards and presentations, in seconds instead of hours of copy-and-paste.
For a real-time crime center, a detective unit or a crime analyst, this turns "I think we've seen this guy before" into a search you can actually check.
What can't AI do with police reports?
Know the limits before you buy:
- It doesn't read perfectly. Bad scans, faded ink, unusual handwriting, local abbreviations and radio codes all cause mistakes. Every fact it extracts should link back to the page it came from so a person can check it.
- It doesn't identify suspects or establish probable cause. Two reports with the same name may be about two different people. A match is a reason to look closer, not evidence.
- It inherits the gaps in your records. If certain incidents were under-reported or written up inconsistently, the analysis will reflect that.
- It doesn't replace data governance. Retention schedules, sealed and expunged records, access rules, public-records requests and discovery obligations still apply. Your agency has to comply with the federal, state and local legal framework that applies to it, so involve your legal counsel and records custodian early.
- It only knows what it can reach. If a source isn't connected, it isn't in the answer.
Report-writing AI vs. report-analysis AI: what's the difference?
| Report-writing AI | Report-analysis AI | |
|---|---|---|
| Main input | Audio from a new incident (body-worn camera or officer dictation) | Existing reports, scans, handwritten notes, databases, spreadsheets |
| Output | A draft narrative | Answers, links between records, reports and dashboards, with sources |
| Main user | Patrol officers | Detectives, crime analysts, RTCC staff, command staff |
| Time frame | Today's call | Years of backlog |
| Human check | The officer approves the narrative | The analyst checks the source before acting |
What should a police department ask before using AI on its records?
- Where does the data live? Will reports, names and plates stay on your servers, or go to a vendor's cloud?
- Does every answer cite its source? If you can't click through to the original page, you can't defend the result.
- Does it work with what you already have? Look for connections to your current databases, file shares and spreadsheets with no migration.
- Is every query logged? Supervisors and auditors should be able to see who searched for what, and when.
- Who decides? The system should suggest. People should decide.
- Can we test it on our own records first? A demo on clean sample data tells you very little about your 2014 handwritten reports.
- How will the deployment meet our security and legal requirements? Ask any vendor to document this in writing for your environment.
How does Axentra OmniData analyze police reports?
OmniData is Axentra's AI data analyst for governments. Its capabilities fit this kind of backlog:
- Reads what's piling up. Scanned documents, handwritten papers, PDF, Word, audio, calls and WhatsApp messages become structured data.
- Connects without replacing anything. It links to databases, APIs, Excel, CSV and shared folders, without migrating or replacing systems. Virtually any other system can be connected through APIs and custom integrations.
- Cross-references everything by person, license plate, address or date.
- Answers in natural language with data, charts and the sources behind each answer, so analysts can check the answers. Plain-language querying is confirmed in Spanish. If your team will work in English, test that during the pilot.
- Builds executive reports, dashboards and presentations in seconds.
- Runs on-premise. It's installed in your own data center. The data stays in your infrastructure, with no dependence on a vendor's cloud.
People stay in charge: OmniData points to records, and your analysts and investigators decide what they mean. Axentra also commits to logging searches for audit.
Why OmniData over the other options?
Agencies usually weigh three alternatives:
- Manual review with spreadsheets.
- The records system's built-in search. It covers text already captured there, but usually not scans or outside files.
- A general-purpose cloud AI chatbot. It reads uploaded PDFs well, but processes the data on the vendor's servers.
| Criterion | Axentra OmniData | Manual review and spreadsheets | General-purpose cloud AI chatbot |
|---|---|---|---|
| Reads scans, handwritten notes, PDF and Word | ✓ Structured data | ✓ Page by page | ✓ File by file |
| Reads audio, calls and WhatsApp | ✓ Turns them into data | ⚠ Listen and transcribe by hand | ⚠ Depends on product |
| Plain-language questions | ✓ Data, charts and sources per answer (Spanish confirmed) | ✗ Manual search | ⚠ Only on what you upload |
| Connects to databases and file shares | ✓ No migration | ✗ Copy and paste | ⚠ Depends on product |
| Other systems via APIs | ✓ Custom integrations | ✗ Not applicable | ⚠ Depends on product |
| Links person, plate, address, date | ✓ Across all connected sources | ⚠ Slow, relies on memory | ⚠ Only what you give it |
| Reports, dashboards, presentations | ✓ In seconds | ✗ Hours of work | ⚠ Only from what you upload |
| Where the data lives | ✓ On-premise, your servers | ✓ In-house | ✗ Vendor's cloud |
| Searches logged for audit | ✓ Axentra commitment | ⚠ No systematic log | ⚠ Depends on product |
| Test on your real data | ✓ Free pilot | — | ⚠ Means uploading it to the cloud |
Bottom line: manual review protects the data but doesn't scale. A cloud chatbot reads well but moves the data out of your infrastructure. OmniData gives you both: AI reading, cross-referencing and reporting, with the data on your own servers.
Specifically:
- Compared with manual review, OmniData reads scans, handwritten notes, audio and WhatsApp messages, links everything by person, plate, address or date, and generates the report or dashboard in seconds instead of hours, with the sources behind each answer.
- Compared with your records system's search, it doesn't compete with it or replace it. It can connect to that system through APIs and custom integrations and adds what its search doesn't cover: scans, handwritten papers, spreadsheets, audio and other systems integrated through APIs.
- Compared with a general-purpose chatbot, reading a single PDF may be comparable. What sets OmniData apart is that it connects to your systems with no migration, cross-references across every source and runs in your own data center. It's also built specifically for government.
Report-writing tools like Draft One do a different job and can coexist with OmniData.
How can we start?
Start small and use your own records. OmniData offers a free pilot with your institution's real sources. Pick one question your analysts struggle to answer today and connect the relevant reports and databases. Then judge the results by whether each answer can be traced to its source.
If you'd like to see what your backlog looks like once you can search it, talk to our team.