External Publication

How AI Helps Solve Cold Cases

TrueCrime.World April 28, 2026

AI is changing how law enforcement approaches unsolved crimes. With over 250,000 cold cases in the U.S. and limited resources, investigators often struggle to manage overwhelming amounts of evidence. Artificial intelligence addresses this by:

Digitizing old evidence : AI tools convert VHS tapes, handwritten notes, and other outdated formats into searchable digital files.
Spotting connections : By analyzing patterns in data, AI links cases and identifies suspects faster than traditional methods.
Saving time : Tasks that take investigators years can now be done in hours using AI systems.
Providing insights : AI highlights inconsistencies in witness statements, maps social connections, and even reconstructs degraded DNA samples.

While AI doesn't replace detectives, it supports their work by organizing evidence, identifying leads, and allowing them to focus on solving cases. However, human oversight is critical to ensure accuracy and fairness. This combination of technology and expertise is already leading to new progress in decades-old investigations.

How AI Accelerates Cold Case Investigations: Key Statistics and Impact

How AI Is Solving Cold Cases: Ash Ghaemi of Crime Owl AI on the Future of Cold Case Investigations

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How AI Organizes Decades of Evidence

Cold case investigations often face a massive hurdle: managing an overwhelming amount of evidence collected over decades. These older cases are filled with materials in outdated formats - think handwritten notes, VHS tapes, audio cassettes, faxes, photographs, and paper reports. Much of this evidence sits untouched in storage, undigitized and unindexed, making it nearly impossible to search efficiently.

Enter AI-powered Digital Evidence Management Systems (DEMS). These tools tackle the problem by converting analog and outdated formats into searchable digital files. Impressively, they support over 255 media formats , from legacy video codecs to scanned documents and analog-to-digital conversions. Once digitized, AI tools use "watch folders" to automatically ingest and process any new files added to the system.

Converting Historical Case Files to Digital Format

Using technologies like Optical Character Recognition (OCR) and Large Language Models (LLMs) , AI platforms transform scanned handwritten notes, faxes, and police reports into searchable text. They also transcribe audio recordings from old cassettes in up to 82 languages and analyze analog surveillance footage, tagging objects, faces, and vehicles for easy reference.

A real-world example? In June 2025, the Meskwaki Nation Police Department took on the challenge of organizing over a decade’s worth of fragmented data tied to the 2015 disappearance of Rita J. Papakee. Led by Lt. Jason Gudenkauf, the department employed TRULEO's AI to process a wide range of evidence, including witness interviews, phone records, social media tips, and body-worn camera footage. The AI generated a 27-page report that pieced together a unified timeline and highlighted discrepancies in witness statements. This led the FBI to redeploy cadaver dogs to previously unexplored areas.

Creating Searchable Evidence Dashboards

Once the evidence is digitized, AI consolidates it into a unified dashboard - a single, searchable interface where investigators can access everything from police reports and lab results to jail calls, photographs, and social media data. Instead of sifting through hours of footage or countless file boxes, detectives can run a single query. For instance, searching "blue sedan" might pull up video frames, witness mentions in transcripts, and tagged photos - all in one place.

This streamlined process saves investigators an incredible amount of time. Anchorage Police Chief Sean Case highlighted this efficiency after implementing the Closure AI tool in January 2026 under a five-year, $375,000 contract :

"It gets (investigators) up to speed on that case file in a matter of hours versus a matter of weeks".

Additionally, the system ensures the chain of custody is preserved through SHA-256 hash-based tamper detection, keeping digitized evidence admissible in court. By transforming scattered files into an organized, searchable database, AI frees investigators to focus on solving cases rather than managing archives. The next step? Using AI to uncover patterns and generate actionable leads, pushing these investigations even further.

How AI Identifies Patterns and Generates Leads

When evidence is digitized and structured, AI steps in to analyze years - sometimes decades - of data to uncover connections that might escape human investigators. This technology meticulously examines every detail in thousands of reports. As Mark de Graaf, Director of the Netherlands Forensic Institute, aptly said:

"Algorithms have no tunnel vision".

Connecting Evidence Across Different Cases

AI leverages Natural Language Processing (NLP) to make sense of unstructured text, such as handwritten notes, witness accounts, and police reports. It extracts critical details like names, dates, locations, and relationships. Meanwhile, computer vision processes historical CCTV footage and crime scene photos to spot recurring elements such as vehicles, weapons, or clothing. It can even detect language patterns, such as slang or dialects, to map social connections across cases. Neural networks take it a step further, analyzing facial features or estimating a suspect's height from low-quality footage.

Using predictive analytics , AI identifies clusters of criminal behavior, pinpointing patterns in locations and timelines. This narrows the pool of potential suspects by over 160 times compared to traditional methods. For DNA analysis, machine learning reconstructs degraded or partial profiles by recognizing patterns in damaged strands, increasing identification success rates for compromised DNA samples by up to 70%.

AI doesn’t just connect dots between cases; it also highlights subtle details that human reviews might miss.

Finding Overlooked Leads

AI excels at uncovering subtle clues that might otherwise go unnoticed. For example, topic modeling identifies recurring themes or names across unrelated interviews or case files. Sentiment analysis picks up on emotional cues - such as anger, deceit, or tension - in written or recorded statements, shedding light on interpersonal dynamics and motives that investigators may have overlooked. Additionally, speaker diarization separates voices in old audio recordings, enabling investigators to isolate and track specific individuals across hours of footage.

This approach has already led to breakthroughs. In 2021 , the Douglas County Sheriff's Office solved the 1985 murder of Roger Dean using investigative genetic genealogy combined with AI-assisted pattern matching. After 35 years of dead ends, the technology linked crime scene DNA to Michael Shannel Jefferson through distant relatives. Jefferson was convicted in 2025. In another case, Homeland Security Investigations worked with U.K. police to crack a child exploitation cold case. With AI-powered facial recognition scanning thousands of cases, they identified and arrested the suspect within two weeks.

Anthony Tassone, CEO of TRULEO, summed it up perfectly:

"AI doesn't replace the instincts, judgment, or experience of a seasoned detective... What it can do is comb through 100% of the evidence... and provide speed, structure, and recall at scale".

AI Tools Used in Cold Case Investigations

Across the United States, law enforcement agencies are turning to specialized AI platforms to revisit cold cases and uncover fresh leads. These tools not only act as repositories for evidence but also analyze it in ways that reveal connections that might otherwise go unnoticed during manual reviews.

Main Platforms and Their Features

Veritone iDEMS (Investigative Digital Evidence Management System) consolidates various types of evidence - ranging from old VHS tapes and handwritten notes to modern CCTV footage - into a single, searchable interface. Butch Rabiega, AI Program Director at the Cold Case Foundation, highlighted its impact:

"Veritone's platform is a game-changer for us. It allows our highly specialized team of experts to do the work of a much larger force, cutting through the noise to identify and link the critical pieces of evidence that can break a case open."

Longeye employs semantic AI to perform thematic searches, identifying concepts like "violence" or "drug dealing" across media types, including video, audio, and social platforms. For smaller agencies, the platform is accessible, costing around $5,000 annually for a team of 20 officers.

Closure is designed to help new detectives quickly familiarize themselves with cold cases, streamlining the onboarding process.

TRULEO excels at summarizing cases by categorizing tips and spotting inconsistencies in witness statements from older files. Meanwhile, VIDIZMO offers automatic transcription in 82 languages and ensures evidence integrity with SHA-256 hash detection.

These tools are already reshaping investigations, as demonstrated by real-world cases.

Real Examples of AI Implementation

The effectiveness of these platforms is evident in recent cold case breakthroughs.

In October 2025, the Redmond Police Department used Longeye to analyze 60 hours of jail phone recordings in mere minutes - a task that would typically take an investigator over a week. The AI flagged a hidden confession where a suspect admitted to firing two shots and concealing a shell casing under a deck. Police Chief Darrell Lowe emphasized the role of AI, stating:

"AI will not replace the need for actual officers or investigators. But it will enhance their effectiveness."

Similarly, in April 2026, San Mateo County investigators leveraged Longeye to sift through 537 jail phone calls in a homicide case. The software identified a critical 15-second segment where a suspect confessed to a location-based detail buried within hours of audio. In another case, the same agency cross-referenced 300,000 cell tower records to pinpoint three devices that were present at two separate crime scenes within an hour of each other.

These examples highlight how AI tools are accelerating investigations and uncovering evidence that might otherwise remain hidden.

Challenges and Limitations of AI in Cold Cases

AI has made impressive strides in assisting with cold case investigations, but it’s not without its flaws. These limitations highlight the continuing need for human expertise to guide and validate AI’s contributions.

AI as a Support Tool, Not a Replacement

AI excels at processing large datasets and identifying patterns, but it lacks the nuanced judgment and intuition that humans bring to complex cases. Aaron Zelinger, CEO of Closure, summarized this concern well:

"We do not want AI automating law enforcement decisions."

Another major hurdle is the infrastructure required to make AI effective. Ali Rind, Product Marketing Executive at VIDIZMO, explained:

"The bottleneck is not intelligence. It is infrastructure."

Many agencies still rely on decades of analog evidence, which must be digitized before AI can even begin its work. This process is both time-consuming and resource-heavy, making it a significant barrier to AI adoption.

Transparency is another critical issue. Advanced machine learning models often operate as "black boxes", meaning their decision-making processes are opaque. This lack of clarity can lead to undetected errors that compromise investigations. AI also struggles with understanding contextual nuances like irony, sarcasm, or evolving slang. This can result in innocent statements being flagged as suspicious.

Because of these challenges, human oversight remains vital to ensure AI’s findings are accurate and actionable.

Why Human Verification Is Necessary

AI can generate leads, but those leads must be thoroughly vetted by human investigators before they’re used in court or to guide major decisions. Ali Rind emphasized this point:

"AI-generated transcripts, object tags, and summaries are investigative aids, not standalone evidence - since courts require human verification."

Skipping this verification step can have serious consequences. For example, independent audits of gunshot detection systems revealed that 80% to 90% of alerts were unrelated to actual gunfire. Facial recognition technology has also faced scrutiny, with alarming statistics showing that 8 out of 10 individuals wrongfully arrested due to faulty matches were Black.

Christopher Rigano, Senior Computer Scientist at NIJ's Office of Science and Technology, highlighted the value of AI when paired with human expertise:

"AI-based algorithms can potentially be used as a 'second pair of eyes' to increase the accuracy of expert human facial examiners and to triage data to increase productivity."

However, investigators must remain vigilant against automation bias - the tendency to accept AI-generated results without question. Treating AI outputs as investigative leads rather than definitive evidence ensures a more responsible and accurate approach.

Conclusion

Law enforcement agencies are reshaping how they tackle cold cases by incorporating advanced AI tools into every step of the process. From digitizing old evidence to finding connections that were previously overlooked, AI turns decades of scattered information into searchable, analyzable data. For example, AI can process and cross-reference evidence from 27 cases in just 30 hours - a task that would take a single investigator an unimaginable 81 years to complete manually. By converting legacy media, spotting patterns across unrelated cases, and generating fresh leads from massive piles of tips, AI is helping detectives address the staggering backlog of over 250,000 unsolved homicides across the country.

That said, AI doesn’t replace human judgment - it amplifies it. Anthony Tassone, CEO of TRULEO, explains it best:

"It's not about replacing the detective. It's about giving them a bird's-eye view of their case - and the ability to zoom in exactly where they need to look."

By automating tedious tasks like organizing data and managing documentation, AI allows investigators to focus on the critical aspects of their work - using their instincts, experience, and empathy to uncover the truth.

This partnership between technology and seasoned detectives is already making a difference. A compelling example is the Meskwaki Nation Police Department’s progress in the Rita J. Papakee case. AI-driven analysis helped redirect FBI resources to previously unsearched areas, breathing new life into the investigation.

As AI continues to evolve - with advancements in multilingual support, sentiment analysis, and cross-referencing capabilities - its impact on cold case investigations will only deepen. While it can’t solve cases on its own, it ensures that no lead is forgotten, helping investigators move closer to justice faster than ever before.

FAQs

What types of cold-case evidence can AI digitize and analyze?

AI is capable of analyzing various types of cold-case evidence, including paper reports, interview records, evidence photos, transcripts, DNA samples, and incomplete case files. When these materials are digitized, AI transforms them into searchable data, making it easier for investigators to identify patterns and connections that might have been overlooked.

How do investigators verify AI-generated leads before acting on them?

Investigators confirm the reliability of AI-generated leads by comparing them with existing evidence, seeking input from experts, and applying various investigative methods. These steps help ensure the leads are trustworthy and practical before moving forward.

How can agencies reduce bias and false matches from AI tools?

Agencies can reduce bias and prevent false matches in AI tools by implementing strict validation processes, working with diverse and representative datasets, and consistently monitoring and improving algorithms. These practices improve accuracy and promote fairness in AI-powered investigations.