Adarga_investigate
AI - Powered Crime Investigation Platform
Adarga
_investigate
AI - Powered Crime Investigation Platform
Adarga_investigate
AI - Powered Crime Investigation Platform
Overview
Overview
Overview
This case study highlights one of several projects I worked on at Adarga.ai, a leading UK artificial intelligence software provider. Adarga works with customers across defence, national security, and commercial organisations to enhance intelligence capabilities. Developed within Adarga’s Rapid Team, this project focused on the quick validation of ideas building Proof of Concepts (PoCs) to explore how AI could streamline complex investigations and help analysts process vast amounts of data more efficiently.
This case study highlights one of several projects I worked on at Adarga.ai, a leading UK artificial intelligence software provider. Adarga works with customers across defence, national security, and commercial organisations to enhance intelligence capabilities. Developed within Adarga’s Rapid Team, this project focused on the quick validation of ideas building Proof of Concepts (PoCs) to explore how AI could streamline complex investigations and help analysts process vast amounts of data more efficiently.
This case study highlights one of several projects I worked on at Adarga.ai, a leading UK artificial intelligence software provider. Adarga works with customers across defence, national security, and commercial organisations to enhance intelligence capabilities. Developed within Adarga’s Rapid Team, this project focused on the quick validation of ideas building Proof of Concepts (PoCs) to explore how AI could streamline complex investigations and help analysts process vast amounts of data more efficiently.
Role
Product Designer
Team
1 Product Designer
1 Product Manager
2 Frontend engineers
2 Backend Engineers
Several Data Scientists
1 Product Designer
1 Product Manager
2 Frontend engineers
2 Backend Engineers
Several Data Scientists
Timeline
PoC: 3 weeks
MVP: 3 months
Skills
Research
Discovery
Ideation
Wireframing
Prototyping
Testing
Role
Role
Role
Product Designer
Team
Team
Team
1 Product Designer
1 Product Manager
2 Front - Ends
2 Back - Ends
Several Data Scientist
1 Product Designer
1 Product Manager
2 Frontend engineers
2 Backend Engineers
Several Data Scientists
1 Product Designer
1 Product Manager
2 Frontend engineers
2 Backend Engineers
Several Data Scientists
Timeline
Timeline
Timeline
PoC: 3 weeks
MVP: 3 months
Skills
Skills
Skills
Research
Discovery
Wireframing
Prototyping
Testing
Background & Context
Background & Context
Background & Context
Investigating Crime in a Digital World
Investigating Crime in a Digital World
Investigating Crime in a Digital World
Crime investigations have come a long way in the digital age. Whether it’s drug trafficking, human smuggling, terrorism, cybercrime, or financial fraud, criminals are getting smarter and use technology to stay ahead of the game.
For investigators in law enforcement, intelligence agencies, and national and private security, the job is tougher than ever. They’re pulling together all kinds of data such as witness statements, surveillance footage, phone records, OSINT (Open-Source Intelligence), HUMINT (Human Intelligence), financial transactions, activity on the dark web, etc, to piece together what’s really happening. But...the problem? The traditional investigative process is slow, siloed, and often overwhelming.
Crime investigations have come a long way in the digital age. Whether it’s drug trafficking, human smuggling, terrorism, cybercrime, or financial fraud, criminals are getting smarter and use technology to stay ahead of the game.
For investigators in law enforcement, intelligence agencies, and national and private security, the job is tougher than ever. They’re pulling together all kinds of data such as witness statements, surveillance footage, phone records, OSINT (Open-Source Intelligence), HUMINT (Human Intelligence), financial transactions, activity on the dark web, etc, to piece together what’s really happening. But...the problem? The traditional investigative process is slow, siloed, and often overwhelming.
Crime investigations have come a long way in the digital age. Whether it’s drug trafficking, human smuggling, terrorism, cybercrime, or financial fraud, criminals are getting smarter and use technology to stay ahead of the game.
For investigators in law enforcement, intelligence agencies, and national and private security, the job is tougher than ever. They’re pulling together all kinds of data such as witness statements, surveillance footage, phone records, OSINT (Open-Source Intelligence), HUMINT (Human Intelligence), financial transactions, activity on the dark web, etc, to piece together what’s really happening. But...the problem? The traditional investigative process is slow, siloed, and often overwhelming.
Drowning in Data
Investigators access huge amounts of data from different sources. Critical info is scattered everywhere, meaning they spend more time digging than actually investigating.
Investigators access huge amounts of data from different sources. Critical info is scattered everywhere, meaning they spend more time digging than actually investigating.
Investigators access huge amounts of data from different sources. Critical info is scattered everywhere, meaning they spend more time digging than actually investigating.
Too Slow, Too Manual
Linking people, places, and organisations is not that easy. It takes too long as traditional techniques such as Pattern of Life Analysis are manual (= slow) and prone to bias.
Linking people, places, and organisations is not that easy. It takes too long as traditional techniques such as Pattern of Life Analysis are manual (= slow) and prone to bias.
Linking people, places, and organisations is not that easy. It takes too long as traditional techniques such as Pattern of Life Analysis are manual (= slow) and prone to bias.
Locked Silos, Limited Collaboration
Legal restrictions, jurisdictional boundaries, siloed databases, and all sort of boring stuff make cross-agency investigations tough.
Legal restrictions, jurisdictional boundaries, siloed databases, and all sort of boring stuff make cross-agency investigations tough.
Legal restrictions, jurisdictional boundaries, siloed databases, and all sort of boring stuff make cross-agency investigations tough.
Proposed Solution
Proposed Solution
Proposed Solution
AI - Powered Investigation Platform
AI - Powered Investigation Platform
AI - Powered Investigation Platform
To tackle the challenges of manual investigations, we propose a platform that optimises the entire investigative lifecycle—from data collection to actionable insights. By integrating automated data gathering, threat detection, predictive analytics, and cross-agency collaboration we want to enable investigators to work faster and more accurately. With a centralised TTP (Techniques, Tactics, and Procedures) knowledge sharing and scalable global data exchange, we want to shift investigations from reactive to proactive, addressing complex, cross-border threats.
To tackle the challenges of manual investigations, we propose a platform that optimises the entire investigative lifecycle—from data collection to actionable insights. By integrating automated data gathering, threat detection, predictive analytics, and cross-agency collaboration we want to enable investigators to work faster and more accurately. With a centralised TTP (Techniques, Tactics, and Procedures) knowledge sharing and scalable global data exchange, we want to shift investigations from reactive to proactive, addressing complex, cross-border threats.
To tackle the challenges of manual investigations, we propose a platform that optimises the entire investigative lifecycle—from data collection to actionable insights. By integrating automated data gathering, threat detection, predictive analytics, and cross-agency collaboration we want to enable investigators to work faster and more accurately. With a centralised TTP (Techniques, Tactics, and Procedures) knowledge sharing and scalable global data exchange, we want to shift investigations from reactive to proactive, addressing complex, cross-border threats.
Design Process
Design Process
Design Process
Design Process
Research
Research
Research
Learning from Intelligence Investigators
Learning from Intelligence Investigators
Learning from Intelligence Investigators
We wanted to understand investigators’ pain points firsthand, but there was a big catch - Intelligence professional are notoriously secretive. The nightmare of an UX Researcher.
We had to adapt to this challenge and do our best, so we leaned on the Pareto Principle for UX: if 5 users can reveal 80% of insights, then a handful of candid conversations with very well selected 'Super Users' (investigators willing to help us), to gave us most of what we needed.
We wanted to understand investigators’ pain points firsthand, but there was a big catch - Intelligence professional are notoriously secretive. The nightmare of an UX Researcher.
We had to adapt to this challenge and do our best, so we leaned on the Pareto Principle for UX: if 5 users can reveal 80% of insights, then a handful of candid conversations with very well selected 'Super Users' (investigators willing to help us), to gave us most of what we needed.
We wanted to understand investigators’ pain points firsthand, but there was a big catch - Intelligence professional are notoriously secretive. The nightmare of an UX Researcher.
We had to adapt to this challenge and do our best, so we leaned on the Pareto Principle for UX: if 5 users can reveal 80% of insights, then a handful of candid conversations with very well selected 'Super Users' (investigators willing to help us), to gave us most of what we needed.
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80%
80%
80%
20%
20%
20%
Don't reinvent the wheel
Don't reinvent the wheel
Don't reinvent the wheel
But help them organise and curate the information the way they already do, just faster and smarter.
But help them organise and curate the information the way they already do, just faster and smarter.
But help them organise and curate the information the way they already do, just faster and smarter.
Swimming in Data
Swimming in Data
Swimming in Data
Investigators pull from a wide range of sources. With so much data finding the right starting point is a nightmare.
Investigators pull from a wide range of sources. With so much data finding the right starting point is a nightmare.
Investigators pull from a wide range of sources. With so much data finding the right starting point is a nightmare.
The side of the Law
The side of the Law
The side of the Law
Outputs must meet strict legal standards. Built-in compliance checks save time and ensure nothing falls through the cracks.
Outputs must meet strict legal standards. Built-in compliance checks save time and ensure nothing falls through the cracks.
Outputs must meet strict legal standards. Built-in compliance checks save time and ensure nothing falls through the cracks.
Don't like to wash dishes
Don't like to wash dishes
Don't like to wash dishes
Automating repetitive and time-consuming tasks frees them up to focus on analysing and interpreting insights.
Automating repetitive and time-consuming tasks frees them up to focus on analysing and interpreting insights.
Automating repetitive and time-consuming tasks frees them up to focus on analysing and interpreting insights.
A picture says it all
A picture says it all
A picture says it all
Maps and visual graphs aren't just nice to have; they're essential for spotting connections, trends, and anomalies.
Maps and visual graphs aren't just nice to have; they're essential for spotting connections, trends, and anomalies.
Maps and visual graphs aren't just nice to have; they're essential for spotting connections, trends, and anomalies.
No fluff, thank you
No fluff, thank you
No fluff, thank you
Investigators need clear, actionable reports with summaries, visual insights, and solid recommendations.
Investigators need clear, actionable reports with summaries, visual insights, and solid recommendations.
Investigators need clear, actionable reports with summaries, visual insights, and solid recommendations.
Competitors
Existing tools on the market offer valuable but fragmented solutions for intelligence investigations. While some focus on data collection (OSINT tools, web scrapers), others specialise in entity resolution, link analysis, or report generation. However, these tools operate in silos, requiring investigators to manually switch between platforms, cross-reference data, and piece together intelligence.
Lack of Integration
No single tool combines data ingestion, entity resolution, hypothesis testing, scenario analysis, and reporting.
Limited AI - Driven Insights
Most tools offer basic automation but lack predictive capabilities to assess risks, test hypotheses, or forecast scenarios.
Workflow Gaps
Investigators must manually curate and structure intelligence, a time-consuming process that creates inefficiencies
Competitors
Existing tools on the market offer valuable but fragmented solutions for intelligence investigations. While some focus on data collection (OSINT tools, web scrapers), others specialise in entity resolution, link analysis, or report generation. However, these tools operate in silos, requiring investigators to manually switch between platforms, cross-reference data, and piece together intelligence.
Lack of Integration
No single tool combines data ingestion, entity resolution, hypothesis testing, scenario analysis, and reporting.
Limited AI - Driven Insights
Most tools offer basic automation but lack predictive capabilities to assess risks, test hypotheses, or forecast scenarios.
Workflow Gaps
Investigators must manually curate and structure intelligence, a time-consuming process that creates inefficiencies
Competitors
Existing tools on the market offer valuable but fragmented solutions for intelligence investigations. While some focus on data collection (OSINT tools, web scrapers), others specialise in entity resolution, link analysis, or report generation. However, these tools operate in silos, requiring investigators to manually switch between platforms, cross-reference data, and piece together intelligence.
Lack of Integration
No single tool combines data ingestion, entity resolution, hypothesis testing, scenario analysis, and reporting.
Limited AI - Driven Insights
Most tools offer basic automation but lack predictive capabilities to assess risks, test hypotheses, or forecast scenarios.
Workflow Gaps
Investigators must manually curate and structure intelligence, a time-consuming process that creates inefficiencies
Competitors
Existing tools on the market offer valuable but fragmented solutions for intelligence investigations. While some focus on data collection (OSINT tools, web scrapers), others specialise in entity resolution, link analysis, or report generation. However, these tools operate in silos, requiring investigators to manually switch between platforms, cross-reference data, and piece together intelligence.
Lack of Integration
No single tool combines data ingestion, entity resolution, hypothesis testing, scenario analysis, and reporting.
Limited AI - Driven Insights
Most tools offer basic automation but lack predictive capabilities to assess risks, test hypotheses, or forecast scenarios.
Workflow Gaps
Investigators must manually curate and structure intelligence, a time-consuming process that creates inefficiencies
Ideation
Ideation
Ideation
Designing the Core Workflow
Designing the Core Workflow
Designing the Core Workflow
We decided to create a workflow that is inspired by proven methodologies used in intelligence and law enforcement investigations, including:
We decided to create a workflow that is inspired by proven methodologies used in intelligence and law enforcement investigations, including:
We decided to create a workflow that is inspired by proven methodologies used in intelligence and law enforcement investigations, including:
F3EAD
Find - Fix - Finish - Exploit - Analyse - Disseminate . Used in military targeting and special intelligence -driven operations. Allows forces to adapt quickly and disrupt enemy networks.
Find - Fix - Finish - Exploit - Analyse - Disseminate . Used in military targeting and special intelligence -driven operations. Allows forces to adapt quickly and disrupt enemy networks.
Find - Fix - Finish - Exploit - Analyse - Disseminate . Used in military targeting and special intelligence -driven operations. Allows forces to adapt quickly and disrupt enemy networks.
PHIA
Professional Head of Intelligence Assessment is used in UK gov and National Security agencies to maintain integrity and analytical standards of intelligence assessments.
Professional Head of Intelligence Assessment is used in UK gov and National Security agencies to maintain integrity and analytical standards of intelligence assessments.
Professional Head of Intelligence Assessment is used in UK gov and National Security agencies to maintain integrity and analytical standards of intelligence assessments.
ACH
Analysis of Competing Hypotheses is used by CIA and law enforcement intelligence units to validate complex hypotheses reducing cognitive biases.
Analysis of Competing Hypotheses is used by CIA and law enforcement intelligence units to validate complex hypotheses reducing cognitive biases.
Analysis of Competing Hypotheses is used by CIA and law enforcement intelligence units to validate complex hypotheses reducing cognitive biases.
The Intelligence Cycle
Intelligence Cycle
Direction - Collection -Processing - Analysis -Dissemination. Used in gov intelligence (M16, FBI or Interpol) to make decisions, respond to threats and enhance NatSec.
Direction - Collection -Processing - Analysis -Dissemination. Used in gov intelligence (M16, FBI or Interpol) to make decisions, respond to threats and enhance NatSec.
Direction - Collection -Processing - Analysis -Dissemination. Used in gov intelligence (M16, FBI or Interpol) to make decisions, respond to threats and enhance NatSec.
Direction-Collection -Processing-Analysis -Dissemination. Used in gov intelligence (M16, FBI or Interpol) to make decisions, respond to threats and enhance NatSec.
Why?
By designing the software around proven intelligence frameworks, we ensure that:
All the investigations follow rigorous, standardised methodologies.
AI enhances structured analysis while reducing bias.
The System is adaptable across law enforcement, defence and national and corporate security.
By designing the software around proven intelligence frameworks, we ensure that:
All the investigations follow rigorous, standardised methodologies.
AI enhances structured analysis while reducing bias.
The System is adaptable across law enforcement, defence and national and corporate security.
By designing the software around proven intelligence frameworks, we ensure that:
All the investigations follow rigorous, standardised methodologies.
AI enhances structured analysis while reducing bias.
The System is adaptable across law enforcement, defence and national and corporate security.
Navigating with Purpose
Navigating with Purpose
Navigating with Purpose
To shape the software’s navigation, I ran two workshops: a "How Might We" session to explore key challenges and opportunities, and a card sorting exercise to organise information intuitively based on investigative workflows.
To shape the software’s navigation, I ran two workshops: a "How Might We" session to explore key challenges and opportunities, and a card sorting exercise to organise information intuitively based on investigative workflows.
To shape the software’s navigation, I ran two workshops: a "How Might We" session to explore key challenges and opportunities, and a card sorting exercise to organise information intuitively based on investigative workflows.
Defining AI-Agents
Defining AI-Agents
Defining AI-Agents
From the beginning, our goal was to design AI agents that enhance, BUT never replace humans.
To ensure our AI agents were truly useful, we analysed the intelligence workflow and pinpointed key pain points where automation could make the biggest impact.
The key insights from our research regarding AI automation were:
Investigators waste time on manual tasks (e.g., entity extraction, hypothesis testing).
AI needs to be explainable. Users won't trust results without transparency.
Scalability is key - the software should work for a wide range of frameworks (mission templates, e.g. Counterterrorism, financial crime…)
From the beginning, our goal was to design AI agents that enhance, BUT never replace humans.
To ensure our AI agents were truly useful, we analysed the intelligence workflow and pinpointed key pain points where automation could make the biggest impact.
The key insights from our research regarding AI automation were:
Investigators waste time on manual tasks (e.g., entity extraction, hypothesis testing).
AI needs to be explainable. Users won't trust results without transparency.
Scalability is key - the software should work for a wide range of frameworks (mission templates, e.g. Counterterrorism, financial crime…)
From the beginning, our goal was to design AI agents that enhance, BUT never replace humans.
To ensure our AI agents were truly useful, we analysed the intelligence workflow and pinpointed key pain points where automation could make the biggest impact.
The key insights from our research regarding AI automation were:
Investigators waste time on manual tasks (e.g., entity extraction, hypothesis testing).
AI needs to be explainable. Users won't trust results without transparency.
Scalability is key - the software should work for a wide range of frameworks (mission templates, e.g. Counterterrorism, financial crime…)
Policy Agent
Policy Agent
Policy Agent
Ensures Compliance & Legal Integrity through Legal Framework selection (e.g., ML, GDPR, counterterrorism laws) and Severity Levels.
Ensures Compliance & Legal Integrity through Legal Framework selection (e.g., ML, GDPR, counterterrorism laws) and Severity Levels.
Ensures Compliance & Legal Integrity through Legal Framework selection (e.g., ML, GDPR, counterterrorism laws) and Severity Levels.
Data Collector Agent
Data Collector Agent
Data Collector Agent
Scrapes OSINT and other databases for relevant documents. Allows custom data source selection and enables real-time ingestion instead of batch processing.
Scrapes OSINT and other databases for relevant documents. Allows custom data source selection and enables real-time ingestion instead of batch processing.
Scrapes OSINT and other databases for relevant documents. Allows custom data source selection and enables real-time ingestion instead of batch processing.
Extraction Agent
Extraction Agent
Extraction Agent
Entity recognition focused on people, locations and organisations, context-aware extraction based on Mission Template (e.g., financial transactions, affiliations for counter-terrorism). Enables manual human validation.
Entity recognition focused on people, locations and organisations, context-aware extraction based on Mission Template (e.g., financial transactions, affiliations for counter-terrorism). Enables manual human validation.
Entity recognition focused on people, locations and organisations, context-aware extraction based on Mission Template (e.g., financial transactions, affiliations for counter-terrorism). Enables manual human validation.
Evaluation Agent
Evaluation Agent
Evaluation Agent
Scores entities based on source reliability, entity history and data consistency. Allows investigators to override AI scores with justifications.
Scores entities based on source reliability, entity history and data consistency. Allows investigators to override AI scores with justifications.
Scores entities based on source reliability, entity history and data consistency. Allows investigators to override AI scores with justifications.
Hypotheses Agent
Hypotheses Agent
Hypotheses Agent
Generates hypotheses based on available data with explainability and allows users to validate or modify them.
Generates hypotheses based on available data with explainability and allows users to validate or modify them.
Generates hypotheses based on available data with explainability and allows users to validate or modify them.
Scenario Agent
Scenario Agent
Scenario Agent
Creation of 'What-if' scenarios based on confirmed hypotheses with probability scoring for different future outcomes. Integrated AI-generated mitigation strategies to prevent threats.
Creation of 'What-if' scenarios based on confirmed hypotheses with probability scoring for different future outcomes. Integrated AI-generated mitigation strategies to prevent threats.
Creation of 'What-if' scenarios based on confirmed hypotheses with probability scoring for different future outcomes. Integrated AI-generated mitigation strategies to prevent threats.
Report Builder Agent
Report Builder Agent
Report Builder Agent
Complies the insights and findings into a structured report enabling investigator customisation and adding a compliance validation before finalising reports.
Complies the insights and findings into a structured report enabling investigator customisation and adding a compliance validation before finalising reports.
Complies the insights and findings into a structured report enabling investigator customisation and adding a compliance validation before finalising reports.
Building Trust in AI - Explainability and Feedback
Investigators need full transparency when working with AI-generated insights—not just because of their job’s high stakes, but also to build confidence in the technology over time. They must stay in control, understanding how AI reaches its conclusions. The platform should provide clear explainability at every step, allowing users to review justifications for credibility scores, adjust AI-generated insights, and even refine the system by providing feedback.
Investigators need full transparency when working with AI-generated insights—not just because of their job’s high stakes, but also to build confidence in the technology over time. They must stay in control, understanding how AI reaches its conclusions. The platform should provide clear explainability at every step, allowing users to review justifications for credibility scores, adjust AI-generated insights, and even refine the system by providing feedback.
The Fun Part
The Fun Part
The Fun Part
Wireframes
Wireframes
Wireframes
Low/Medium - Fidelity
High - Fidelity
PoC - Prototype
PoC - Prototype
PoC - Prototype
Descoping: From PoC to MVP
Descoping: From PoC to MVP
Descoping: From PoC to MVP
During the PoC phase, we created a crime investigation platform as a comprehensive intelligence tool, integrating data ingestion, visual analysis tools, and investigative workflows. We developed and tested the platform with our stakeholders and Intelligence Investigators users, validating the PoC and receiving the green light from stakeholders to build the product. However, to ensure a focused and impactful MVP, we strategically narrowed our scope to a Knowledge Base Builder - a core feature enabling users to create structured profiles for target entities.
This MVP prioritises efficient data ingestion, automated entity extraction, and dossier creation based on the selected mission template, allowing investigators to consolidate intelligence into actionable target profiles. By focusing on this foundational capability, we deliver immediate value while laying the groundwork for future expansions into a full-spectrum investigative tool.
During the PoC phase, we created a crime investigation platform as a comprehensive intelligence tool, integrating data ingestion, visual analysis tools, and investigative workflows. We developed and tested the platform with our stakeholders and Intelligence Investigators users, validating the PoC and receiving the green light from stakeholders to build the product. However, to ensure a focused and impactful MVP, we strategically narrowed our scope to a Knowledge Base Builder - a core feature enabling users to create structured profiles for target entities.
This MVP prioritises efficient data ingestion, automated entity extraction, and dossier creation based on the selected mission template, allowing investigators to consolidate intelligence into actionable target profiles. By focusing on this foundational capability, we deliver immediate value while laying the groundwork for future expansions into a full-spectrum investigative tool.
Results so far
Data Ingestion
90%
Uploaded documents are successfully processed and have entitities extracted within 60 seconds.
85 %
Accuracy in entity extraction, with fewer than 10% of manual corrections per project.
30 %
Increase of the number of documents uploaded and processed per user compared to the manual process.
Target Profile Creation Efficiency
80 %
Less time.
80 %
User satisfaction with the accuracy and format of auto-generated dossiers.
100 %
Accurate citations and references to original source documents.
85 %
Accuracy in entity extraction, with fewer than 10% of manual corrections per project.
Accuracy in entity extraction, with fewer than 10% of manual corrections per project.
Accuracy in entity extraction, with fewer than 10% of manual corrections per project.
30 %
Increase of the number of documents uploaded and processed per user compared to the manual process.
Increase of the number of documents uploaded and processed per user compared to the manual process.
Increase of the number of documents uploaded and processed per user compared to the manual process.
90 %
Uploaded documents are successfully processed and have entities extracted within 60 seconds.
Uploaded documents are successfully processed and have entities extracted within 60 seconds.
DATA INGESTION
Conclusion and Next Steps
Conclusion and Next Steps
Conclusion and Next Steps
Through a structured and iterative development approach, we have successfully transitioned from a broad Proof of Concept to a focused and impactful MVP. By prioritising efficient data ingestion, automated entity extraction, and streamlined Target Profile creation, we have significantly improved the way investigators consolidate and manage intelligence.
The strong adoption metrics and user satisfaction validate our approach, setting the stage for the next phase of development. With Phase 2, we aim to enhance investigative workflows further by introducing advanced entity analysis, enriched data cross-referencing, and AI-driven insights. This evolution will transform our platform from a knowledge management tool into a proactive intelligence system, empowering investigators to uncover critical connections faster and make more informed decisions.
This journey reinforces our commitment to building scalable, efficient, and intelligence-driven solutions for modern investigative challenges.
80%
User satisfaction with the accuracy and format of auto-generated dossiers.
User satisfaction with the accuracy and format of auto-generated dossiers.
100 %
Accurate citations and references to original source documents.
Accurate citations and references to original source documents.
80 %
Less time.
Less time.
TARGET PROFILE CREATION EFFICIENCY
Copyright © 2025 Cristina Amat
Copyright © 2025 Cristina Amat
Copyright © 2025 Cristina Amat
Copyright © 2025 Cristina Amat
Copyright © 2025 Cristina Amat