Framework Summary

Open-source intelligence—OSINT—is the systematic collection, processing, and analysis of publicly available information to produce actionable intelligence. For decades, this methodology has been the foundation of government and defense intelligence operations. The same framework applies directly to private markets fundraising: identifying which limited partners to engage, when to engage them, through what channel, and with what message.

This framework codifies the OSINT methodology that Altss applies to allocator intelligence. It explains the intelligence cycle, source evaluation standards, verification protocols, signal detection approaches, and targeting workflows that separate systematic LP research from ad hoc prospecting.

Altss monitors 2,500+ sources across 40+ jurisdictions. The median lag from signal detection to platform availability is under 72 hours for personnel changes, mandate shifts, and portfolio events. Contact verification runs on a 30-day refresh cycle with a 99%+ deliverability target for teams following sender best practices.

The goal is not to provide a list of allocators. The goal is to provide a methodology for generating actionable intelligence that improves fundraising outcomes.

Why OSINT Methodology Matters for Capital Formation

Fundraising failure is rarely a pitch problem.

It is usually a targeting problem, a timing problem, or a data quality problem. Fund managers reach the wrong allocators, reach the right allocators at the wrong time, or reach them with information that is stale, incomplete, or incorrect.

The 2024-2025 fundraising environment amplified these failures. According to industry data, median time-to-close for institutional commitments extended to 14-18 months. LP inboxes reached saturation as the number of funds in market exceeded historical norms while deployment pace slowed. Allocators became more selective—and more sensitive to poorly-timed or poorly-researched outreach.

Traditional LP databases were designed as directories. They list names, titles, and contact information—often self-reported, infrequently updated, and disconnected from current allocator behavior. The result is outreach that arrives late, lands in the wrong inbox, or references investment mandates that shifted months ago.

OSINT methodology addresses these failures by treating LP research as an intelligence discipline rather than a data retrieval exercise.

Intelligence is not information. Intelligence is information that has been collected, processed, analyzed, and verified to support a specific decision.

For fundraising, the decision is: which allocators should I engage, when, through what channel, and with what message.

Answering that question requires:

Systematic collection across multiple source types—regulatory filings, personnel changes, portfolio events, conference attendance, media coverage, and governance disclosures.

Rigorous verification against independent corroboration—not accepting self-reported data at face value, but confirming claims against primary sources.

Continuous monitoring for changes—recognizing that allocator behavior is dynamic and static snapshots decay rapidly.

Structured analysis that converts raw data into actionable insight—not just "who exists" but "who is active now, why, and how to reach them."

The methodology creates the edge. The platform delivers it at scale.

The Intelligence Cycle Applied to LP Research

Every systematic OSINT operation follows a five-phase intelligence cycle. NATO codified this framework; intelligence agencies worldwide apply it. The cycle provides structure, ensures completeness, and creates feedback loops that improve collection over time.

Phase 1: Planning and Direction

Planning defines the intelligence requirements. For fundraising, requirements center on the "6 W's and 2 H's":

Who are the target allocators—family offices, endowments, sovereign wealth funds, pensions, foundations, funds of funds?
What are their mandates, constraints, and preferences—strategy fit, geography, ticket size, first-time manager posture?
When do they make allocation decisions—LP decision cycles, IC calendars, board schedules?
Where are they located and where do they deploy capital—North America, Europe, MENA, APAC, LatAm?
Why might they be interested in this fund—mandate alignment, timing, relationship history?
Which sources can provide reliable information—regulatory filings, news, personnel databases, event registrations?
How do they evaluate managers—operational due diligence, reference checks, IC process?
How much capital can they deploy—check size ranges, commitment pacing constraints?

Requirements should be specific and prioritized. A requirement like "find family offices" is too broad to guide effective collection. A requirement like "identify single-family offices in the Gulf region with healthcare sector exposure, ticket sizes between $5-20M, and activity signals in the past 90 days" provides the specificity needed for targeted collection.

Phase 2: Collection

Collection gathers raw information from defined source categories. The critical principle from intelligence tradecraft: focus on collecting sources, not just information. A source that provides one useful data point today may provide ten useful data points over the next year if monitored systematically.

Regulatory sources form the foundation of high-confidence allocator intelligence:

SEC Forms ADV, 13F, D—investment adviser registration, institutional holdings, private placement activity
FCA Financial Services Register—UK authorization status and permissions
CSSF fund registrations—Luxembourg AIFMD reporting
MAS Financial Institutions Directory—Singapore fund manager licensing
ADGM and DFSA registers—Middle East regulatory disclosure
Corporate registries across jurisdictions—beneficial ownership, director appointments, entity formations

Personnel sources provide timing signals on mandate shifts and decision-maker changes:

Professional network changes—role transitions, new appointments
Board disclosures—committee memberships, governance roles
Conference speaker lists—industry engagement and thought leadership
Hiring patterns—team expansion signals mandate growth

Portfolio and transaction sources reveal active deployment behavior:

Investment announcements—new commitments, fund closes
Co-investment activity—direct participation alongside fund commitments
Secondary transactions—portfolio rebalancing and liquidity events
Direct investment press releases—family office and institutional direct deals

Event sources indicate market engagement:

Conference registrations and attendee lists
Speaking engagements and panel participation
Industry association memberships

Media and public statement sources provide context on investment thesis and priorities:

Interviews and public remarks
Authored articles and thought leadership
Press coverage of allocation activity

Philanthropic and governance sources reveal family wealth structures:

Foundation filings (Form 990-PF)—grants, board members, investment holdings
Board memberships and charitable commitments
Family governance disclosures

Altss continuously ingests from 2,500+ sources across these categories, covering 40+ jurisdictions with emphasis on North America, Europe, MENA, APAC, and Latin America.

Phase 3: Processing and Exploitation

Processing converts raw collected information into usable formats. This includes entity resolution, deduplication, normalization, and initial quality assessment.

Entity resolution is particularly challenging for family office intelligence. A single family may operate through multiple legal entities across jurisdictions, use different naming conventions, and appear in sources under varying descriptions.

Connecting "Smith Family Holdings LLC," "Smith Capital Partners," and "The Smith Family Foundation" to the same single-family office requires systematic matching logic—not manual review.

Entity resolution at Altss uses:

Legal entity identifier cross-referencing
Principal and director matching across registrations
Address and registration jurisdiction correlation
Investment activity pattern matching

Without rigorous entity resolution, intelligence fragments across disconnected records—and fundraising teams miss the complete picture of allocator behavior.

Phase 4: Analysis and Production

Analysis examines processed information, adds context, identifies patterns, and produces finished intelligence with explicit judgments. Analysis answers the "so what" question—why this information matters for the fundraising decision at hand.

For LP intelligence, analysis includes:

Mandate fit assessment: Does this allocator's stated or revealed mandate align with the fund's strategy, geography, and return profile? Investment mandates are often documented in investment policy statements for institutions but must be inferred from behavior for family offices. The best mandate data separates confirmed constraints from inferred patterns and ties them to evidence and recency.

Timing assessment: Is this allocator currently deploying capital, or are they in a holding pattern? Have they recently closed commitments that create capacity, or are they fully allocated? What does their commitment pacing suggest about near-term activity? The LP decision cycle varies by allocator type—institutions may take 18-24 months from first meeting to commitment, while principal-led family offices can move in weeks.

Access assessment: What is the path to the decision-maker? Is there a warm introduction available, or does the approach require cold outreach? Who influences the decision beyond the primary contact—investment committee members, consultants, existing GP relationships?

Signal assessment: What recent activity signals suggest openness to engagement? Personnel changes, mandate shifts, portfolio events, and conference attendance all provide timing intelligence that separates productive outreach from wasted effort.

Phase 5: Dissemination

Dissemination delivers finished intelligence to decision-makers in formats that support action. For fundraising teams, this means LP profiles that include not just contact information but mandate context, timing signals, relationship paths, and engagement recommendations.

A useful LP profile answers:

What does this allocator invest in—and is my fund a fit?
When are they likely to make decisions—and is now the right time?
Who makes decisions—and how do I reach them?
Why would they be interested—and what angle resonates?
What has changed recently—and what signal triggered this opportunity?

Phase 6: Evaluation and Feedback

The sixth phase—often overlooked—is evaluation and feedback. This continuous improvement loop refines collection methods based on intelligence effectiveness.

Which sources produced the most actionable leads?
Which verification methods caught the most errors?
Which signals correlated with actual meetings?
Which timing indicators predicted commitment velocity?

Feedback closes the loop and improves the next cycle. Without evaluation, intelligence operations stagnate. With it, they compound.

Source Evaluation: Adapting the Admiralty Code for LP Intelligence

Not all sources are equal. Not all information from reliable sources is accurate. Distinguishing signal from noise requires systematic source evaluation.

The Admiralty System, developed by British Naval Intelligence during World War II and standardized by NATO, provides the universal language for intelligence quality assessment. It uses a two-character alphanumeric rating that evaluates sources and information separately.

This separation is the critical principle: a reliable source can provide inaccurate information, and an unreliable source can occasionally provide valid intelligence.

Source Reliability Scale (A-F)

A – Completely Reliable: Tried and tested source with consistent accuracy. For LP intelligence: direct regulatory filings (SEC EDGAR, FCA Register, CSSF), audited disclosures, verified submissions from allocators themselves.

B – Usually Reliable: Minor doubt, but consistent validity over time. For LP intelligence: established commercial data with documented verification processes, reputable trade publications with editorial standards, primary source press releases.

C – Fairly Reliable: Doubts exist, but past information has often proven valid. For LP intelligence: professional network data, conference registration lists, aggregators without full source transparency.

D – Not Usually Reliable: Significant authenticity concerns. For LP intelligence: unverified web scrapes, outdated directories, sources with commercial incentives that may compromise accuracy.

E – Unreliable: History of inaccuracy. For LP intelligence: self-reported data without verification, sources with documented error rates exceeding industry standards.

F – Cannot Be Judged: Unknown source requiring additional assessment before reliance.

Information Credibility Scale (1-6)

1 – Confirmed: Corroborated by multiple independent sources with logical consistency. For LP intelligence: a mandate change confirmed by regulatory filing, press announcement, and direct conversation.

2 – Probably True: Not confirmed, but logical and consistent with other known intelligence. For LP intelligence: a ticket size inferred from multiple past commitments with similar characteristics.

3 – Possibly True: Not confirmed, reasonably logical. For LP intelligence: a sector preference inferred from portfolio composition without direct confirmation.

4 – Doubtful: Possible but illogical with no corroboration. For LP intelligence: a stated mandate that contradicts revealed investment behavior.

5 – Improbable: Contradicts other known information. For LP intelligence: contact information that bounces, roles that have changed, mandates that have closed.

6 – Cannot Be Judged: Insufficient basis for evaluation.

Application to Fundraising Decisions

A1 rating—verified disclosure from a trusted regulatory source, confirmed by multiple independent sources—warrants immediate action. This is the threshold for high-confidence outreach.

B2 rating—reputable data with probable but unconfirmed accuracy—supports high-confidence decisions with verification. This is the threshold for prioritized outreach with confirmation.

Anything rated D4 or below should be monitored only. Never act on low-confidence intelligence without additional corroboration—the cost of a bounced email or wrong contact far exceeds the cost of additional verification.

Verification Protocols

The foundational verification principle from intelligence tradecraft: maintain a source ledger with two unrelated corroborations for each critical fact.

The emphasis on unrelated is critical. Sources citing the same original report do not constitute independent corroboration. A press release picked up by three news outlets is one source, not three.

Verification for LP intelligence operates at three levels:

Entity-Level Verification

Does this allocator exist as described? Is the legal entity active? Is the registration current? Is the stated AUM consistent with observable evidence?

Entity verification uses:

Regulatory filings and registration status
Corporate registry records
Legal entity identifier systems
Cross-referencing multiple identifier databases

Contact-Level Verification

Is this person currently in this role? Is this contact information deliverable? Is this the decision-maker or an intermediary?

Contact verification requires recency. B2B contact data decays at documented rates:

2-3% monthly decay for contact information
22-30% annual decay across contact databases
37% annual change rate for email addresses specifically
43% annual change rate for phone numbers

These decay rates mean quarterly or monthly verification cycles are insufficient for high-value targeting. Continuous re-verification—catching role changes, departures, and contact updates as they occur—is the operational standard for fundraising teams that prioritize deliverability.

Altss runs multi-provider email verification and bounce testing on decision-maker contacts with a target refresh cycle of 30 days or less. The deliverability target is 99%+ for teams that follow sender hygiene best practices.

Intelligence-Level Verification

Is this mandate still active? Is this timing signal current? Is this relationship path still valid?

Intelligence verification requires ongoing monitoring, not point-in-time checks. A mandate confirmed six months ago may have shifted. A relationship contact may have moved. A timing signal may have passed.

Every field in an allocator profile should carry:

Last verified timestamp—when was this information confirmed?
Source attribution—what evidence supports this claim?
Confidence level—is this confirmed, inferred, or stated but unverified?

The audit trail makes claims defensible—critical when decision-makers dispute roles, ownership, or mandate signals.

Signal Categories and Detection

Signals are the raw material of timing intelligence. They answer the question: why should I reach this allocator now, rather than six months ago or six months from now?

Signals convert static directory data into dynamic, actionable intelligence.

Personnel Signals

CIO or investment head appointments frequently precede mandate reviews. New leadership often means new allocation priorities, new manager relationships, and openness to new conversations. A new CIO at an endowment may review the entire GP roster within their first year.

Investment committee restructuring signals governance changes that may affect decision timelines and approval processes. New committee members may champion strategies that predecessors overlooked.

Team expansion—hiring investment professionals, adding sector specialists—signals capacity to evaluate new opportunities. An allocator staffing up for private credit is signaling intent to deploy in private credit.

Departures create relationship gaps. When a portfolio manager who championed a manager leaves, the manager's position may weaken—or create an opening for competitors.

Key person risk cuts both ways: the same dynamics that create risk for GPs when key people leave create opportunity when key people arrive.

Altss monitors personnel changes across investment committees, CIO offices, and portfolio teams, surfacing changes within 24-72 hours of public disclosure.

Mandate Signals

New allocation sleeves—adding a strategy, sector, or geography—create immediate deployment needs. An endowment adding a dedicated emerging manager program is actively seeking Fund I and Fund II managers.

Investment policy statement updates signal strategic direction shifts. An allocator revising ESG policy, adjusting risk tolerances, or expanding geographic scope is signaling changed priorities.

Liquidity events—distributions from existing holdings, secondary sales, portfolio company exits—create capacity for new commitments. Strong DPI from existing funds often precedes new deployment activity.

Overcommitment or undercommitment relative to pacing models signals future deployment behavior. An allocator running below target is actively looking; one running above target is waiting for distributions before new commitments.

Portfolio Signals

New commitments reveal current mandate in action. A sovereign wealth fund committing to three healthcare growth funds signals healthcare growth as an active mandate—far more reliably than stated preferences.

Co-investment activity indicates appetite for direct exposure alongside fund commitments. Allocators making co-investments are signaling both capacity and willingness to move quickly on the right opportunities.

Secondary sales suggest portfolio rebalancing that may precede new primary commitments. An allocator selling older vintage positions is creating capacity for fresh deployment.

Event Signals

Conference attendance indicates active market engagement. An allocator who was absent from industry events for two years and suddenly appears on three conference registrations is signaling something.

Speaking engagements and panel participation often precede active deployment—allocators tend to raise their profile when they want deal flow. A family office principal speaking at a venture conference is advertising receptiveness.

Industry association activity—ILPA committee participation, pension association engagement—signals institutional seriousness and active involvement in the allocator community.

Media and Public Statement Signals

Interviews and public remarks often preview allocation themes months before formal announcements. A CIO discussing interest in climate tech in a podcast interview is providing actionable intelligence.

Authored articles and thought leadership indicate sector focus and investment philosophy. Allocators writing about emerging markets infrastructure are telling you what they want to see.

The Altss OSINT Infrastructure

Altss was built as an OSINT-native platform from the ground up. The methodology described in this framework is not an overlay on a traditional directory—it is the foundation of how Altss collects, processes, verifies, and delivers allocator intelligence.

Source Coverage

Altss continuously ingests from 2,500+ sources across 40+ jurisdictions. Source categories include:

Regulatory sources: SEC EDGAR, FCA Register, CSSF disclosures, MAS directory, ADGM and DFSA registers, Companies House, and corporate registries across major financial centers in North America, Europe, MENA, APAC, and Latin America.

News and media sources: Wire services, trade publications, regional business press, and specialized private markets coverage—monitored continuously for allocation announcements, personnel changes, and strategic developments.

Personnel sources: Professional network changes, appointment announcements, board disclosures—tracked systematically for investment decision-makers across all allocator types.

Event sources: Conference registrations, attendee lists, speaking schedules—providing visibility into who will be where, and when.

Portfolio sources: Investment announcements, fund closes, transaction disclosures—revealing active deployment behavior in near-real-time.

Entity Resolution

Altss maintains entity resolution logic that connects disparate records to unified allocator profiles. A family operating through multiple vehicles, using different naming conventions, appearing in different jurisdictions—all resolve to a single, comprehensive profile.

This is critical for family office intelligence, where the same family may operate through a single-family office, investment holding companies, foundations, and personal investment vehicles.

Verification Infrastructure

Every contact record passes through multi-provider verification:

Email deliverability testing using multiple verification services
Role verification against source documentation
Recency scoring based on last verification timestamp and source freshness

The target is 99%+ deliverability for teams following sender best practices, with re-verification on a 30-day cycle or faster for high-priority contacts.

Signal Detection

Altss surfaces signals in near-real-time:

Personnel changes flagged within 24-72 hours of public disclosure
Mandate shifts detected from regulatory filings, press announcements, and portfolio activity
Event attendance visibility showing which allocators are registered for upcoming conferences
Portfolio events—commitments, exits, secondaries—captured from transaction announcements

The median lag from signal detection to platform availability is under 72 hours for most signal types.

Coverage

9,000+ verified family offices across North America, Europe, MENA, Asia-Pacific, and Latin America
Full institutional LP coverage including pensions, endowments, foundations, sovereign wealth funds, insurance companies, and funds of funds
Private wealth channels including RIAs and OCIOs
1.5 million+ verified decision-maker contacts

From Intelligence to Action: The Targeting Workflow

Intelligence without action is academic. The purpose of OSINT methodology is to improve fundraising outcomes—more meetings, better-fit LPs, shorter cycles.

Step 1: Define Requirements

Start with the fund's specific needs. Strategy, geography, ticket size, LP type preferences, timeline constraints.

Requirements should be specific enough to filter effectively but broad enough to capture opportunities. "European family offices interested in growth equity" is a starting point. "Single-family offices in DACH region with technology sector exposure, ticket sizes $10-25M, and activity signals in past 180 days" enables precision targeting.

Step 2: Identify Candidates

Use intelligence to build a candidate list. Filter by:

Mandate fit—strategy alignment, geography match, ticket size compatibility, LP type, first-time manager posture.

Timing signals—recent activity, personnel changes, mandate shifts, conference attendance.

Access quality—relationship paths, warm introduction availability, shared connections.

Step 3: Prioritize

Not all candidates are equal. Prioritize by fit strength, timing relevance, and access quality.

A high-fit allocator with strong timing signals and a warm path should be at the top of the outreach queue. A moderate-fit allocator with no timing signals and cold access should wait—or receive lower-intensity engagement.

Prioritization prevents the most common fundraising failure mode: spreading effort equally across unequal opportunities.

Step 4: Research

Before outreach, build a complete picture. Review:

Mandate history and recent commitments
Team composition and decision-maker profiles
LP decision cycle and investment committee process
Recent signals and what triggered them
Relationship paths and introduction opportunities

Concrete example: A growth equity fund targeting European family offices identifies a signal—a German SFO's CIO joined from a US endowment three months ago. Research reveals: the prior endowment had 15% allocation to growth equity, the new CIO authored articles on technology investing, the family's operating business is in industrial automation. The angle: growth equity in industrial software, referencing the CIO's prior work and the family's sector expertise. This is not a generic pitch—it's intelligence-informed engagement.

Find the angle that makes your fund relevant to their current priorities—not your standard pitch.

Step 5: Engage

Outreach should reflect the intelligence. Reference specific signals—the recent hire, the mandate expansion, the conference attendance. Demonstrate that you have done the work to understand their context.

Generic outreach signals generic effort. Signal-informed outreach signals diligence—the same quality allocators look for in the managers they back.

Step 6: Monitor

After engagement, continue monitoring. Track responses, update profiles with new information, watch for additional signals.

The allocator who doesn't commit today may commit in two years—if you maintain the relationship and re-engage at the right moment. Continuous monitoring ensures you see that moment when it arrives.

Timing: The Dimension Static Databases Miss

Most LP databases are static directories. They tell you who exists. They do not tell you who is active now.

Timing is the dimension that separates intelligence from information. The same allocator who was a strong target six months ago may be fully committed today. The allocator who showed no interest last year may have just hired a new CIO with a mandate to diversify.

Timing intelligence requires continuous monitoring:

What changed in the last 30 days?
What changed in the last 90 days?
What signals suggest activity in the next 90 days?

Static lists decay. By the time a quarterly update arrives, the information may already be stale. Family offices move faster than update cycles. Personnel changes, mandate shifts, and deployment decisions happen continuously.

Altss was built around timing. Signals surface within hours or days, not months. The median lag from signal detection to alert is measured in days, not quarters.

For fundraisers, this means arriving early—before the mandate is fully committed, before the inbox is full of competing pitches, before the allocator has made decisions about which managers to meet.

Based on Altss platform data from Q3-Q4 2025:

Allocators with timing signals convert to meetings 2.8x more frequently than allocators without recent activity indicators
First-mover advantage compounds—teams reaching allocators within 14 days of a personnel change see 40% higher response rates than those arriving 60+ days later
Signal density predicts deployment velocity—allocators with 3+ recent signals (personnel + event + portfolio) move to commitment 2.1x faster than those with single signals
Contact decay accelerates during market transitions—Altss verification detected 34% higher contact churn in H1 2025 than the prior year average, driven by GP-side layoffs and allocator team restructuring

Data Quality: The Foundation of Trust

Poor data quality destroys fundraising efficiency. Every bounced email damages sender reputation. Every wrong contact wastes time. Every stale mandate creates friction.

Data quality is not a feature. It is the foundation.

Completeness

A profile with a name and email is not complete. A complete profile includes:

Legal entity and registration status
AUM range and commitment capacity
Investment mandate scope and constraints
Investment committee structure and decision process
Recent activity and timing signals
Key personnel with verified contact information
Relationship paths and warm introduction opportunities

Completeness enables targeting. Incomplete profiles force guesswork.

Accuracy

Accuracy requires verification. Self-reported data is not verified. Scraped data is not verified. Only data that has been checked against independent sources and passed deliverability testing is verified.

Altss maintains verification timestamps on every field. Users can see when information was last verified and assess confidence accordingly.

Freshness

Freshness is not the same as accuracy. Data can be accurate at the time of collection and stale six months later. Freshness requires continuous re-verification, not periodic batch updates.

The Altss refresh target is 30 days for contact verification and near-real-time for signal detection. This matches the pace at which allocator information actually changes.

Consistency

Consistency means the same standards apply across the entire dataset. A family office in Singapore is verified to the same standard as one in New York. An emerging manager allocator is researched with the same rigor as a large institution.

Consistency enables comparison. Inconsistent data creates false confidence in some records and false skepticism in others.

Compliance and Ethics

OSINT methodology operates within legal and ethical boundaries. The "open" in open-source intelligence means publicly available information—not hacked, leaked, or improperly obtained data.

Legal Framework

The regulatory landscape for investor data has shifted materially since 2023, creating new compliance requirements for OSINT operations.

GDPR applies to personal data of European individuals, including business contacts. The legitimate interest basis permits processing for direct marketing but requires documented assessment:

Purpose must be legitimate and specific
Processing must be necessary with no less intrusive alternative
Individual rights must not override the legitimate interest

The ICO's December 2022 guidance clarified that business contacts have different but still protected privacy expectations—a distinction that affects how investor intelligence can be collected and used.

CCPA B2B exemption expired January 1, 2023. California business contact data is now subject to full privacy requirements including notice at collection, privacy policy disclosure, and opt-out rights. This was the first major jurisdiction to apply comprehensive consumer privacy law to B2B contact information—and other states have followed.

State privacy laws proliferated in 2024-2025. Virginia, Colorado, Connecticut, Utah, and Texas enacted comprehensive privacy statutes. Fund managers with LP outreach across multiple states now face a patchwork of requirements that OSINT operations must navigate.

China's PIPL provides no legitimate interests basis for data processing. Cross-border transfers require security assessments or Standard Contractual Clauses, with March 2024 exemptions permitting transfers necessary for contract performance or involving fewer than 100,000 individuals annually.

Singapore PDPA, UAE data protection regulations, and other jurisdictional rules create additional requirements for cross-border data use.

Altss maintains compliance infrastructure including documented legal basis assessments, transparency notices, and data handling procedures that meet requirements across jurisdictions.

Ethical Standards

The ethical standard for OSINT is straightforward:

Collect only from publicly available sources
Do not misrepresent identity or intent
Respect confidentiality when requested
Operate with transparency about methods

Prohibited activities include pretexting or impersonation, social engineering to manipulate disclosure, unauthorized access to non-public systems, using data from breaches or illegal sources, and any activity that would violate reasonable expectations of privacy.

Stewardship

Data stewardship protects allocators and preserves the ecosystem.

Altss operates as an in-platform system:

No bulk CSV export
No open API for mass data extraction
No resale to list brokers or placement agents

This model protects allocator goodwill. When every fund manager receives the same list and sends the same generic pitch, allocator inboxes burn out and response rates collapse.

Controlled access and signal-driven timing ensure that outreach remains relevant and allocators remain receptive—preserving the ecosystem for everyone.

Building an OSINT-Native Fundraising Operation

Adopting OSINT methodology is not a tool purchase. It is an operational transformation.

Shift from Lists to Intelligence

Stop treating LP data as a static list to be worked through. Start treating it as a dynamic intelligence picture to be monitored, analyzed, and acted upon based on signals.

Lists expire. Intelligence adapts.

Invest in Analysis Capacity

Collection without analysis is noise. Build the capacity—whether internal or through platform tools—to convert raw data into actionable insight.

What does this signal mean for our fund?
Why does this change matter for our targeting?
What should we do differently based on this intelligence?

Close the Feedback Loop

Track what works. Which signals correlated with meetings? Which sources provided the best leads? Which verification methods caught errors? Which timing indicators predicted commitment velocity?

Use feedback to improve the next cycle. Without evaluation, intelligence operations stagnate. With it, they compound.

Maintain Discipline

OSINT methodology requires discipline. Document requirements. Follow verification protocols. Update assessments when new information arrives.

Discipline creates repeatability. Repeatability creates scale.

Common OSINT Failures in Fundraising

Understanding how OSINT methodology fails is as important as understanding how it succeeds. The most common failure modes:

Over-Reliance on Single Sources

Teams that depend on one data provider, one regulatory filing type, or one news source miss intelligence that only emerges from cross-referencing. A personnel change that appears in one source may take weeks to propagate to another. Systematic collection across multiple source types catches changes faster and validates accuracy through corroboration.

Treating All Signals Equally

Not all signals predict the same behavior. A conference registration is a weak signal—many allocators register for events they don't attend. A new commitment announcement is a strong signal—it confirms active deployment. Effective OSINT operations weight signals by predictive power, not just recency.

Verification Gaps at Scale

As contact lists grow, verification discipline often weakens. Teams that verify the first 50 contacts manually may skip verification on contacts 500-1000. But data accuracy matters at every scale—one bounced email to a principal damages sender reputation the same whether it's the first outreach or the thousandth.

Analysis Paralysis

The opposite failure: collecting so much intelligence that analysis becomes impossible. Teams drown in data without converting it to action. Effective OSINT operations define clear requirements upfront and filter ruthlessly based on those requirements. More data is not always better data.

Ignoring Negative Signals

OSINT reveals not just opportunities but also dead ends. An allocator who just committed to three funds in your strategy may be at capacity. A family office whose principal just retired may be in transition. Negative signals save time by de-prioritizing outreach that won't convert.

Measuring OSINT Effectiveness

What gets measured improves. Key metrics for evaluating OSINT-driven fundraising:

Source Metrics

Source hit rate: What percentage of sources collected produced usable intelligence?
Signal-to-noise ratio: How many raw signals required filtering to produce one actionable lead?
Source freshness: What is the average latency from real-world event to source capture?

Verification Metrics

Deliverability rate: What percentage of email outreach reached intended recipients?
Contact accuracy rate: What percentage of role/title information was confirmed accurate at time of outreach?
Decay detection rate: What percentage of contact changes were caught within 30 days?

Conversion Metrics

Signal-to-meeting ratio: What percentage of signal-triggered outreach converted to meetings?
Timing advantage: How many days before competing outreach did signal-driven engagement arrive?
Fit accuracy: What percentage of meetings were with allocators who matched targeting criteria?

Feedback Metrics

False positive rate: What percentage of high-priority targets turned out to be poor fits?
Missed opportunity rate: What percentage of eventual commitments came from allocators not in the original target list?
Cycle improvement: How have conversion rates changed quarter-over-quarter?

Conclusion

OSINT methodology transforms LP research from guesswork into discipline. It replaces static lists with dynamic intelligence, sporadic updates with continuous monitoring, and generic outreach with signal-driven engagement.

The framework is not complicated:

Define specific intelligence requirements
Collect systematically from diverse sources
Process and resolve entities accurately
Analyze to produce actionable insight
Verify against independent corroboration
Disseminate to support decisions
Evaluate and improve continuously

The organizations that will excel at fundraising are those treating LP research as a systematic intelligence discipline—with defined methodologies, documented processes, measurable quality standards, and continuous feedback loops.

Altss was built to operationalize this methodology:

9,000+ verified family offices with comprehensive profiles
2,500+ sources across 40+ jurisdictions monitored continuously
30-day contact verification cycles with 99%+ deliverability targets
Signal detection within 72 hours for personnel, mandate, and portfolio changes
Compliance infrastructure supporting GDPR, CCPA, and cross-border requirements
Stewardship model protecting allocator goodwill through controlled access

The result is allocator intelligence that arrives early, verified, with timing signals that convert to meetings.

The OSINT Intelligence Framework for Private Markets Fundraising