AI-assisted due diligence for the work humans already perform
Partner, vendor, grantee and subcontractor due diligence often includes a public-source review: staff search organization names, former names, key people, adverse media, fraud concerns, safeguarding issues, political exposure, governance problems, donor history and other public risk signals.
The challenge is not that teams do not know how to search. The challenge is that manual public-source checks are often inconsistent, time-consuming, hard to document, and difficult to repeat.
IntegrityFile uses AI to make this step more structured, comprehensive and reviewable.
AI helps organize the work. Humans remain responsible for judgment.
IntegrityFile does not make eligibility decisions, sanctions determinations, investigation findings, or approval recommendations. It identifies and organizes public-source risk signals so that trained users can review them, document false positives, request clarification where appropriate, and follow their organization's approval process.
Why AI belongs in due diligence
Traditional due diligence depends heavily on human effort. A reviewer may search a few terms, open several links, save notes, and move on. That can work, but it creates problems:
- different staff use different search terms
- local-language searches may be missed
- former names and acronyms may not be checked
- false positives may not be documented
- important source links may not be saved
- risk categories may be applied inconsistently
- reports may not show what was actually checked
- the same partner may be reviewed differently across offices
- periodic monitoring is difficult to maintain manually
AI can improve this process by making the review more systematic.
IntegrityFile uses AI to help search, structure, classify, summarize and document public-source information. The result is a clearer due diligence record that shows what was reviewed, what was found, what was dismissed, and what may require follow-up.
What AI does in IntegrityFile
IntegrityFile uses AI across several controlled steps of the due diligence workflow.
1. Search planning
AI helps generate a structured search plan based on the organization name, country, sector, known aliases, former names, acronym, website, and associated individuals.
The search plan may include:
- official name variations
- acronym variations
- translated or local-language name forms where possible
- former names or rebranding terms
- key people connected to the organization
- country-specific search terms
- risk-category search terms
- adverse-media terms
- donor, audit, regulator, court, procurement and governance terms
This helps reduce the risk that the review depends only on one simple search.
2. Public-source discovery
AI-assisted workflows help identify and organize relevant public-source information from available sources.
Source types may include:
- official organization registries
- company and beneficial ownership registries where available
- charity and NGO registries
- government and regulator records
- donor, audit and inspector-general records
- debarment and ineligibility references
- court and legal records where publicly available
- organization websites and public documents
- annual reports and project pages
- media and adverse-news sources
- local-language public sources where possible
- public web footprint indicators
Source availability varies by country, language, entity type and public-record environment. IntegrityFile does not claim that every jurisdiction has equal source coverage.
3. Risk-category classification
AI helps organize findings into NGO-relevant risk categories.
Reports may group public-source signals under:
- integrity, fraud and misuse of funds
- safeguarding, PSEA and misconduct
- aid diversion, sanctions exposure and terrorism-financing concerns
- political exposure and neutrality risk
- conflict of interest and related-party risk
- entity continuity and rebranding risk
- governance and registration concerns
- human rights and protection concerns
- procurement and vendor performance risk
- legal, regulatory and financial distress
- donor, audit and investigation history
- reputation and public controversy
This helps users review findings through a consistent due diligence lens instead of reading scattered search results one by one.
4. Source summarization
AI helps summarize long or complex public-source materials into short review notes.
For example, a report may show:
- source title
- publication date
- source type
- short summary
- risk category
- possible relevance
- source link
- suggested follow-up question
Summaries are designed to help users triage information faster. They do not replace reading the underlying source where a finding may affect a decision.
5. False-positive support
Public-source due diligence produces many false positives. Similar names, unrelated organizations, translated names, old articles and acronym collisions can create confusion.
IntegrityFile uses AI to help flag possible false positives by comparing:
- organization name
- country
- location
- sector
- associated people
- dates
- website
- registration details
- source context
- name variations
- former names
Users can mark each finding as:
- relevant
- not relevant
- possible false positive
- needs clarification
- escalation required
The report records the reviewer's notes so that the file shows not only what was found, but also why certain results were dismissed.
6. Rebranding and entity-continuity review
One NGO-specific risk is that an organization may operate under a new name after prior problems, leadership disputes, donor concerns, registration issues or reputational damage.
AI helps detect public-source signals such as:
- former names
- new acronyms
- old websites or social profiles
- shared addresses
- shared leadership
- same founders under a new entity
- dormant entities revived for new funding
- similar names to known organizations
- inconsistent registration information
The purpose is not to accuse an organization of wrongdoing. The purpose is to help reviewers ask the right questions about continuity, legal identity, leadership and prior history.
7. Follow-up question generation
AI helps convert findings into practical follow-up questions.
Examples:
- Can the partner confirm whether this former name belongs to the same legal entity?
- Can the vendor explain the referenced contract dispute and whether it has been resolved?
- Can the organization provide current registration documents and board details?
- Can the partner clarify whether the named individual is still part of management?
- Can the organization provide its safeguarding policy and records of corrective action?
- Can management confirm whether this media report relates to the same organization?
This helps teams move from "we found something online" to "here is the specific clarification needed before approval."
8. Report drafting
AI helps generate a structured due diligence report that users can review, edit, export and file.
A report may include:
- entity profile
- identifiers reviewed
- aliases and former names
- associated individuals reviewed
- search terms used
- source types reviewed
- findings by risk category
- false-positive log
- source links and dates
- suggested follow-up questions
- reviewer notes
- final decision field
- report timestamp
- monitoring option
The report is designed to support partner, vendor, grant, procurement and monitoring files.
What AI does not do
IntegrityFile's AI does not:
- approve or reject partners
- make sanctions determinations
- decide vendor eligibility
- make legal conclusions
- conduct investigations
- verify whether allegations are true
- label any person or organization as guilty
- label any organization as unsuitable
- replace formal screening systems
- replace internal approval processes
- replace legal, safeguarding, audit or investigation review
- make automatic adverse decisions
The report is a structured public-source review tool. Final decisions remain with the customer organization.
Human review is built into the workflow
AI can help find and organize information, but due diligence requires human judgment.
Every IntegrityFile report is intended for review by a human user who can assess:
- whether the source relates to the same organization or person
- whether the source is reliable
- whether the information is current
- whether the issue is relevant to the selection decision
- whether the matter is an allegation, finding, dispute, correction or resolved issue
- whether the partner or vendor should be asked for clarification
- whether escalation is required under internal policy
- whether legal, safeguarding, procurement, audit or management review is needed
IntegrityFile supports human review by providing source links, dates, summaries, relevance notes, false-positive fields and follow-up prompts.
How AI improves objectivity
Manual searches can be inconsistent. One reviewer may search only the organization name. Another may search the acronym. Another may include the director. Another may search in English only. Another may search for fraud but not safeguarding or rebranding.
IntegrityFile helps improve objectivity by applying a consistent review structure.
The system uses defined risk categories, documented search logic and standard report fields. That makes it easier to compare reviews across organizations, teams and countries.
Objectivity does not mean the AI is always right. It means the process becomes more consistent, transparent and reviewable.
How AI improves thoroughness
AI is useful in due diligence because public-source information is fragmented. Relevant signals may appear across local media, registry records, donor reports, organization websites, old names, translated names, court references, audit summaries, project pages or social footprint indicators.
IntegrityFile helps reviewers look beyond the first few search results.
AI can help check:
- name variations
- former names
- acronyms
- local-language terms where possible
- associated people
- risk-category terms
- donor and audit terms
- governance and registration terms
- rebranding and continuity terms
- adverse-media terms
- procurement and legal terms
This makes the review broader than a simple manual search while still keeping findings linked to public sources and human review.
How AI improves documentation
A due diligence step is only useful if it can be documented.
IntegrityFile helps create a record showing:
- what entity was reviewed
- who or what was searched
- which categories were checked
- which source types were reviewed
- what findings were identified
- which findings were dismissed
- what follow-up questions were suggested
- who reviewed the report
- when the report was generated
- what decision or next step was recorded
This documentation is often the missing part of manual public-source checks.
How AI supports monitoring
Partner and vendor risk is not static. An organization may be clear at the time of selection, but new public information may appear later.
IntegrityFile can support periodic monitoring by re-checking selected organizations for new public-source signals.
Monitoring may help identify new information related to:
- adverse media
- public investigations
- donor or audit findings
- safeguarding concerns
- governance changes
- legal or regulatory action
- rebranding or name changes
- conflict-of-interest signals
- procurement disputes
- public controversy
Monitoring is not a full investigation. It is a structured way to identify new public-source signals for review.
Responsible AI controls
IntegrityFile uses AI with safeguards designed for due diligence contexts.
Human review
Reports are not final decisions. Users must review findings before action.
Source transparency
Findings are linked to public-source references where available.
Explainable structure
Risk categories, summaries, relevance notes and false-positive fields are shown in the report.
Purpose limitation
The service is designed for legitimate partner, vendor, grant, procurement, monitoring, audit and compliance workflows.
Data minimization
Users should submit only the organization and associated-person information needed for the review.
Proportionality
Individual searches should be limited to people relevant to the due diligence purpose, such as directors, officers, board members, signatories, owners, senior managers and key project personnel.
No automatic adverse decisions
IntegrityFile does not make automatic rejection, exclusion, eligibility or approval decisions.
False-positive handling
Reports include space to document why a result is irrelevant, unrelated or insufficiently matched.
Audit trail
Reports are designed to show what was checked and how findings were handled.
Access control
Reports should be accessed only by authorized users with a need to know.
AI and the future of due diligence
Due diligence is moving from one-time checklist completion to continuous, evidence-based review.
In the past, many teams relied on static forms, manual searches and scattered file notes. That approach is increasingly difficult to sustain when organizations work across complex partner networks, high-risk operating environments, multiple donors, subcontractors, consortium members and rapidly changing public information.
AI makes a better model possible.
The future of due diligence is:
- structured instead of ad hoc
- source-linked instead of undocumented
- repeatable instead of dependent on one reviewer
- multilingual where possible
- risk-category based instead of keyword-only
- monitoring-enabled instead of one-time only
- human-reviewed instead of automatically decided
- audit-ready instead of scattered across notes and screenshots
IntegrityFile is built for that future.
It helps organizations move from manual public-source checks to documented partner integrity reports that are easier to review, easier to file, and easier to repeat.
Why this matters for NGOs and grant makers
NGOs, donors, UN-funded partners and grant makers face specific due diligence challenges.
They may need to review:
- local implementing partners
- community-based organizations
- vendors and suppliers
- grantees
- subcontractors
- consortium members
- downstream partners
- board members
- signatories
- senior officials
- project leads
The risks are also specific. Standard screening tools may focus on sanctions, PEPs or formal watchlists. Those tools are important, but they may not fully cover the public-source issues that matter in NGO partner selection.
IntegrityFile focuses on the areas that are often harder to document:
- safeguarding and PSEA concerns
- aid diversion signals
- rebranding after controversy
- governance disputes
- local media allegations
- donor or audit history
- political exposure
- conflict of interest
- entity-continuity concerns
- procurement disputes
- public reputation issues
This makes IntegrityFile a complement to existing screening and approval systems, not a replacement.
A practical example
A team is preparing to select a local NGO as an implementing partner.
A manual review might include a few web searches and notes in the file.
An IntegrityFile review can help the team document:
- the organization's current name
- possible former names
- acronym variations
- public website and leadership information
- key associated individuals
- public-source risk signals
- adverse-media findings
- governance or registration concerns
- donor or audit references
- false positives
- follow-up questions
- reviewer notes
- final decision field
The team can then use the report to support its own process, ask clarification questions if needed, and keep a documented record in the selection file.
Our AI use principles
IntegrityFile uses AI according to the following principles:
Assist, do not decide
AI supports the review. It does not make final decisions.
Show sources
Findings should be linked to public-source references where available.
Preserve context
Reports should show dates, source types, summaries and relevance notes.
Reduce missed signals
AI helps search across names, aliases, former names, risk categories and local-language terms where possible.
Reduce false positives
AI helps identify possible mismatches, but users make the final review decision.
Document the process
The report should show what was checked and how findings were handled.
Protect people
Individual searches should be proportionate, purpose-limited and relevant to the due diligence workflow.
Support accountability
Reports should help organizations explain and document their review process.
Frequently asked questions
Does AI decide whether a partner is eligible?
No. IntegrityFile does not make eligibility decisions. It provides structured public-source information for human review.
Does this replace sanctions screening?
No. IntegrityFile complements sanctions screening and other formal checks. It supports the open-source and adverse-media due diligence step.
Can AI make mistakes?
Yes. AI-assisted summaries and classifications can be incomplete or incorrect. Users must review the source material and apply their own policies before taking action.
Does a clean report mean there is no risk?
No. A clean report means no relevant public-source signal was identified within the scope of the review. It does not guarantee that no issue exists.
Does a finding mean the allegation is true?
No. A finding means that public-source information was identified and categorized for review. Users must assess the source, context, accuracy, relevance and severity.
Why use AI if human review is still required?
Because AI can help make the process faster, broader, more consistent and better documented. Human review remains essential for fairness, context and final decisions.
Can IntegrityFile monitor partners after selection?
Yes. Monitoring can help identify new public-source signals after selection, onboarding or renewal.
Built for responsible due diligence
IntegrityFile uses AI to strengthen the public-source due diligence step that teams already perform manually.
It helps reviewers search more consistently, organize findings more clearly, document false positives, generate follow-up questions and create a report that can be kept in the partner, vendor, grant or procurement file.
AI makes the process more thorough.
Human review makes it responsible.