OSINT Methodology for LP Intelligence

By Dawid, Founder of Altss. Writing about allocator intelligence and fundraising strategy.

Altss is built on open source intelligence (OSINT) — not surveys — to construct LP profiles from public filings, news signals, professional activity, and multi-source verification, with a human verification layer on every profile and allocator self-reporting expanding in 2026. The database covers 9,000+ family offices and — as of February 2026 — comprehensive institutional LP coverage including pensions, endowments, foundations, insurers, sovereign wealth funds, OCIOs, and fund-of-funds, with 1.5M+ verified contacts across 40+ jurisdictions re-verified on a ≤30-day cadence. This article documents each layer of the methodology — what it captures, what it misses, and why GPs evaluating LP data providers should understand the sourcing behind any dataset they use for outreach.

Why should GPs care about how LP data is sourced?

Every fundraising database sells the same promise: accurate contacts, current mandates, reliable coverage. The pitch decks are interchangeable. What differs — and what most buyers never diligence — is the sourcing methodology behind the data.

The LP intelligence market has historically operated on one paradigm: survey-based collection. Platforms like Preqin, PitchBook, and FINTRX send questionnaires to allocators, compile responses, supplement with desk research, and publish on quarterly or semi-annual cycles. Preqin, now part of BlackRock, maintains over 450 multi-lingual analysts for this process. The model works — but it depends entirely on allocator participation. Allocators who don't respond, or who respond infrequently, become coverage gaps.

Altss was built from the ground up on a different foundation: OSINT. Every profile in the database originates from public, verifiable sources — regulatory filings (Form ADV, IRS 990-PF, Companies House), continuous news monitoring, professional signals, and multi-provider contact verification. The platform does not depend on allocator participation to build or maintain coverage. That is the core architectural difference, and it is why Altss can cover 9,000+ family offices — including allocators that legacy databases systematically miss because those allocators never respond to surveys.

OSINT is the foundation. It is not the only layer. Every OSINT-derived profile passes through a human verification layer before it reaches production. Automated systems detect signals; human analysts confirm interpretation, resolve ambiguity, and apply editorial judgment that no algorithm handles reliably — particularly for entity classification, mandate interpretation, and decision-chain mapping. The combination of automated signal detection and human quality control is what produces a verified intelligence product, not just a data feed.

Self-reporting is now being added as a supplementary layer. In 2026, Altss began enabling allocators to claim, update, and enrich their own profiles directly on the platform — adding stated preferences, correcting details, and signaling mandate changes on their own terms. This is not a pivot away from OSINT. It is an additional data channel layered on top of the OSINT foundation. When an allocator self-reports a mandate preference, that stated preference is cross-referenced against what OSINT already shows about their actual behavior. The result is richer profiles that combine observed activity with stated intent — something neither a pure survey model nor a pure OSINT model can produce alone. Self-reporting capabilities will expand throughout 2026, with additional value delivered back to participating LPs.

The scope of this methodology expanded significantly in February 2026, when Altss added comprehensive institutional LP intelligence — pensions, endowments, foundations, insurers, sovereign wealth funds, OCIOs, fund-of-funds, and RIAs with alternatives exposure — to unified search alongside the existing 9,000+ family offices. The same five-layer OSINT methodology applies to institutional allocators. A CalPERS board meeting minute is a Tier 3 news signal. A university endowment's 990 filing is a Layer 1 regulatory source. A sovereign wealth fund CIO speaking at a conference is a Layer 3 event signal. The methodology doesn't change by allocator type — the source mix shifts, but the intelligence cycle is identical.

The OSINT Intelligence Framework documents the full technical specification. This article explains how it works in practice.

What is OSINT in the context of LP research?

Open source intelligence is intelligence derived from publicly or commercially available information. The IC OSINT Strategy 2024–2026, published by the Office of the Director of National Intelligence and the CIA, defines OSINT as information that "addresses specific intelligence priorities, requirements, or gaps." The global OSINT market exceeds $2.5 billion and is growing at double-digit rates annually.

Applied to fundraising: OSINT means building allocator profiles from regulatory filings, corporate registries, press coverage, professional profile activity, and conference attendance — rather than waiting for allocators to respond to surveys. Altss was built purely on this foundation. Every profile originates from OSINT-sourced data. Self-reporting and other supplementary channels enrich profiles that already exist — they do not create them.

OSINT is not scraping. Scraping is bulk data extraction. OSINT is a structured intelligence cycle — collection, cross-referencing, verification, analysis — that produces validated profiles. A scraped contact list is raw data, often stale at capture. An OSINT-derived allocator profile cross-references that data against filings, news mentions, and deliverability checks before it reaches any user.

The distinction is practical, not semantic. A bounced email damages sender reputation. A stale investment mandate wastes meeting capacity. An incorrect title signals that your IR team hasn't done its homework. Every outreach failure traces back to a data quality problem. Data quality is a direct function of sourcing methodology.

How does OSINT-based LP research work?

The methodology operates in five sequential layers. Each builds on the previous. No single layer produces usable intelligence alone — value compounds through cross-referencing and verification.

Layer 1: What do regulatory filings reveal about allocators?

Every institutional allocator leaves a paper trail. The challenge is knowing where to look, what each filing actually reveals, and where each source has known gaps.

SEC filings (United States). Form ADV filings disclose assets under management, fee structures, advisory personnel, and disciplinary history for registered investment advisors. Form D filings reveal private fund formations — identifying new vehicles before they appear in any database. 13F filings show quarterly equity holdings for managers above $100 million in reportable assets. Altss monitors all three filing types continuously, extracting mandate signals, personnel changes, and entity formations as they publish.

What 13F filings miss. 13F data excludes private fund allocations, fixed income holdings, and positions under the reporting threshold. A family office managing $800 million but holding under $100 million in reportable equities will not appear in 13F data. Over-reliance on 13F data produces a systematic coverage bias toward equity-oriented allocators and misses the majority of alternative investment activity. This is one of the most common analytical errors in LP research — and one reason why databases built primarily from 13F filings under-represent the family office universe.

IRS Form 990-PF. Every private foundation in the United States files a public 990-PF annually, disclosing total assets, investment income, grants made, and the names of investment managers with fees paid to each. A foundation that paid $2.3 million in investment management fees is deploying meaningful capital. A foundation whose assets increased by 40% in a single year likely received a major gift and is actively redeploying. Approximately 3,000 foundations with over $100 million in assets represent the relevant allocator universe for most fundraises. See Endowments and Foundations as LPs for detailed analysis.

Known gap: the 990-PF lag. These filings are annual. IRS processing means the data is typically 12–18 months old by the time it's publicly available. A foundation's 2024 filing may not appear until mid-2025. Altss addresses this by cross-referencing 990-PF data with current news and professional signals (Layers 2 and 3), but the underlying lag is inherent to the source.

State corporate registries. New entity formations — LLCs, limited partnerships, trusts — signal capital deployment. A family office forming three new Delaware LLCs in a single quarter is preparing for new investments. Cross-referencing those entities against the family's known investment themes indicates where capital may be directed.

International registries. Companies House (UK), ACRA (Singapore), DIFC (Dubai), and Handelsregister (Germany, Switzerland) provide equivalent filing data. Singapore's Variable Capital Company (VCC) framework — the fastest-growing fund structure in Asia-Pacific — generates public registration records that reveal new family office formations before any survey captures them. As one example: Altss OSINT sweeps of Form ADV updates identified 245 Florida-based family offices by mid-2025, catching relocations weeks before legacy databases reflected the changes.

In total, Altss continuously ingests from 2,500+ sources across 40+ jurisdictions — including SEC EDGAR, FCA Register, CSSF, MAS, ADGM, DFSA, and corporate registries across North America, Europe, MENA, APAC, and Latin America.

What this layer produces: entity structures, AUM indicators, new fund formations, personnel changes, jurisdictional footprints.

What it cannot produce: intent. A filing records what an allocator did. It does not indicate what they plan to do next. That requires Layers 2–5.

Layer 2: What do news and media signals reveal about allocator activity?

Allocator activity is reported before it is filed. A family office co-leading a $500 million infrastructure round appears in the Financial Times before the SEC filing lands. A pension fund CIO keynoting on private credit signals a mandate shift before the allocation committee meets.

OSINT methodology monitors news across three tiers with different lead times and reliability profiles:

Tier 1 — financial wire services. Fastest to report large transactions and personnel moves. When Bezos Expeditions participated in Skild AI's $1.4 billion Series C in January 2026, wire services reported it within hours. The limitation: Tier 1 systematically under-reports activity in emerging markets, smaller allocators, and non-English-language jurisdictions.

Tier 2 — trade and regional press. Captures deals Tier 1 misses entirely. When Novo Holdings acquired a stake in India's Surya Hospitals in January 2026, Indian media broke the story — not Bloomberg. Regional monitoring is the most resource-intensive tier because it requires multi-language processing and editorial judgment about source reliability.

Tier 3 — direct-source announcements. Fund manager press releases, university investment office annual reports, pension fund board meeting minutes (public in most US states), sovereign wealth fund annual reviews. CalPERS board minutes disclosed its adoption of the Total Portfolio Approach in November 2025 — a governance signal relevant to every GP evaluating how endowments and pensions will assess managers going forward. Tier 3 is the highest-fidelity source because information comes directly from the allocator.

What this layer produces: transaction signals, mandate shifts, personnel moves — typically 30–90 days ahead of survey-based database updates.

Where this layer introduces risk: news reports can be inaccurate, speculative, or based on incomplete sourcing. An article stating a family office "is considering" a new mandate is a different quality signal than a filing confirming a completed investment. The methodology weights signals by source tier and corroboration count, but news-derived intelligence is inherently probabilistic.

Layer 3: What do professional and event signals reveal?

Allocators are people. People change jobs, attend events, publish articles, join boards, and update professional profiles. Each action is a signal — though not every signal is equally reliable.

Professional profile changes. When a pension fund's director of private markets updates their title to "Head of Alternatives," that is a mandate expansion signal. When a family office principal adds "board member" at a climate-tech company, that is a sector signal.

Event intelligence. Conference registrations, speaker panels, and attendance records are among the highest-value OSINT signals. An LP attending a private credit conference is evaluating credit managers. An endowment CIO on a venture panel at the ILPA Summit is signaling an active mandate. Altss tracks event intelligence across 200+ LP-relevant conferences globally — the same events profiled in the 2025–2026 Fundraising Roadshow Guide — matched against verified profiles so fundraising teams see who attended and what they discussed.

Thought leadership signals. When allocators publish or speak publicly, they reveal priorities. In January 2026, Julia Thiele-Schürhoff of Stella Vermögensverwaltungs GmbH stated publicly that the family is committed to "managing our wealth responsibly and with a long-term perspective" — preceding a €200 million commitment to responsAbility's emerging markets strategy. Altss tracked the full sequence in the January 2026 Deal Flow report.

What this layer produces: real-time personnel movements, event signals, mandate indicators, relationship mapping between allocators and managers.

Known coverage gap. Professional signal coverage is biased toward allocators with active public profiles. Many of the largest single-family offices deliberately maintain minimal digital presence — no conference attendance, no publications, no professional profiles. For these allocators, Layers 1 and 4 carry most intelligence weight. Any LP database claiming comprehensive family office coverage should be transparent about this structural gap.

Layer 4: How is contact data verified?

Raw intelligence is not useful intelligence. Every data point must be verified before it reaches a fundraising team. This layer is where LP databases differentiate — or fail.

Human verification layer. Before any automated verification runs, OSINT-derived profiles pass through human analyst review. Automated systems are effective at detecting signals — a new Form ADV filing, a title change on a professional profile, a news mention. They are not reliable at interpreting those signals. Is a new entity formation a fund vehicle or an estate planning structure? Is a title change a promotion or a lateral move to a different mandate? Is a news mention about an active investment or a historical reference? Human analysts make these judgment calls. They classify entities, confirm mandate interpretations, resolve conflicting signals, and map decision chains within allocator organizations. This editorial layer is not optional — it is what converts raw signal detection into verified intelligence.

Multi-provider contact verification. After human review, contact data undergoes automated multi-provider verification: email validation against multiple services, SMTP handshake deliverability testing, and confirmation that the person still holds the attributed title and role. Altss maintains a ≤30-day re-verification standard on core profiles, targeting 99%+ deliverability for teams following sender best practices. Teams applying warm-up and frequency best practices consistently see deliverability rates well above industry averages.

Why this matters now. LP contact information decays at approximately 25–35% per year under normal conditions. During the 2025 market transition — layoffs, restructurings, fund closures accelerated across the alternatives industry — Altss verification detected measurably higher contact churn than the prior-year baseline. A list verified six months ago has roughly one in four contacts wrong. At outreach scale, that is hundreds of bounced emails, damaged sender reputation, and wasted pipeline capacity.

Cross-source validation standard. A name in a single source is unverified. A name appearing in a filing, a news article, and a professional profile — with consistent title, institution, and contact information across all three — is verified. The methodology requires minimum two independent sources to confirm any data point before it enters a production profile. Verification operates at three levels: entity verification (does this allocator exist as described?), contact verification (is this person currently in this role?), and signal verification (is this activity current and correctly interpreted?).

Where verification still has limits. Email deliverability testing confirms an address accepts mail. It does not confirm the person reads it, that they are the decision-maker, or that the role description is current as of today. SMTP verification has a known false-positive rate — some addresses accept mail but route to inactive or shared inboxes. No verification methodology, including this one, guarantees real-time accuracy. What it does is reduce the error rate to a level where outreach at scale produces reliable results.

What this layer produces: verified, deliverable contact data with confidence timestamps — a quality-controlled dataset with known accuracy characteristics.

Layer 5: How does signal correlation produce timing intelligence?

The final layer transforms verified data into timing signals. Knowing a family office exists is a starting point. Knowing that the same family office just formed a new investment vehicle, hired a head of alternatives, attended a climate conference, and was quoted discussing co-investment structures — that is a cluster of correlated signals indicating active deployment.

Signal analysis works by correlating events across the preceding four layers:

Entity formation + personnel hire + event attendance = active deployment signal. When these three events cluster within a 90-day window, the probability of an active mandate is high. This correlation is how Altss identified 23 New York-based family offices relocating to Florida in mid-2025 — flagging Form ADV updates within hours, often weeks before press coverage.

CIO change + board reconstitution + consultant engagement = strategy review signal. A pension fund hiring a new CIO, reconstituting its investment committee, and engaging a new consultant is about to change its allocation approach. In January 2026, Mousse Partners (the Arnault family's single-family office, Paris) appointed a new CIO — a timing window Altss flagged for GPs whose thesis aligns with the family's historical mandates.

Asset sale + new entity formation + geography change = reallocation signal. A family that sold a real estate portfolio, formed new Delaware LLCs, and registered a Singapore VCC is restructuring its investment approach. Outreach during this window — with a relevant thesis — is significantly more likely to produce a meeting than a cold approach from a stale directory. The Family Office Targeting Strategy 2026 maps the qualification process that converts these signals into meetings.

Median lag from signal detection to database availability: under 72 hours for personnel changes, mandate shifts, and portfolio events.

What this layer produces: timing intelligence that answers the fundraiser's operational question — who to contact this week, about what, and why now.

Where signal correlation can mislead. Correlated signals are probabilistic, not deterministic. A family office forming a new entity and hiring an investment professional may be deploying capital — or may be restructuring existing holdings, managing succession, or winding down a strategy. Signal correlation raises interpretation probability; it does not eliminate ambiguity. Treat timing signals as prioritization inputs, not as confirmed mandates.

How does this methodology apply to institutional LPs?

The five-layer OSINT methodology was built for family offices — allocators that are structurally difficult to cover through surveys because they have no obligation to respond and many prefer not to. The February 2026 expansion to institutional LP coverage applies the same intelligence cycle to a different source mix.

Pensions and endowments produce the richest filing data of any allocator type. US public pension board minutes are public record in most states. University endowments with over $1 billion in assets publish annual investment reports. 990-PF filings for private foundations disclose manager relationships and fee structures. For these allocators, Layer 1 (regulatory filings) and Layer 2 (Tier 3 direct-source announcements) carry the majority of intelligence weight.

Sovereign wealth funds produce comparatively little filing data but generate high volumes of Tier 1 and Tier 2 news coverage. When a sovereign wealth fund makes a $500 million infrastructure commitment, it appears in financial press within hours. For SWFs, Layers 2 and 3 (news and event signals) are the primary sources; Layer 1 provides jurisdiction and entity structure data.

Insurance companies sit in the middle. General account allocations are reported in statutory filings (NAIC filings in the US). Insurer CIOs attend the same conferences as pension CIOs. The source mix is balanced across all five layers.

OCIOs and fund-of-funds are detectable through a combination of Form ADV filings (Layer 1), conference attendance (Layer 3), and client relationship signals in news coverage (Layer 2). When an OCIO wins a new mandate from a university endowment, that typically generates a Tier 2 or Tier 3 press announcement — which simultaneously updates both the OCIO profile and the endowment profile.

The unifying principle: the OSINT methodology does not change by allocator type. What changes is which layers produce the strongest signals for each segment. The institutional LP taxonomy documents how Altss categorizes and verifies each allocator type.

OSINT vs. survey-based LP databases: structural comparison

The differences are structural, not cosmetic. For platform-specific comparisons, see The Top 5 Family Office & Fundraising Intelligence Platforms.

Survey-based platforms like Preqin, PitchBook, and FINTRX are built on allocator questionnaires and desk research conducted by large analyst teams — Preqin alone maintains over 450 multi-lingual analysts. Public filings supplement those survey responses. Their refresh cycles are typically quarterly or semi-annual, with periodic email campaigns for contact verification. The intelligence they produce reflects stated preferences at the time of the survey — allocation targets, GP feedback, investment mandate descriptions. Their core strength is capturing what allocators say they intend to do. Their core weakness is dependence on allocator response rates: offices that don't respond become coverage gaps, and data ages between survey cycles. Family office depth varies across these platforms, with some reporting 4,000–6,000 offices. Institutional LP coverage has historically been their strongest segment.

Altss is built on OSINT — public filings, media monitoring, corporate registries, and professional signals — as the foundational sourcing layer. Self-reporting, launched in 2026, supplements that OSINT foundation by letting allocators enrich their own profiles. Every profile passes through a human verification layer before production. Monitoring is continuous with ≤30-day re-verification cycles. Contact data is multi-provider SMTP-tested and cross-source validated. The intelligence Altss produces reflects observed behavior combined with stated intent (via self-reporting), real-time changes, and timing signals — typically 30–180 days ahead of survey-based updates. The core strength is capturing what allocators actually do, without depending on their participation. The core weakness is that OSINT cannot capture private intent not expressed in public data — though self-reporting begins to address this gap. Family office coverage stands at 9,000+ globally (3,880 US, 2,630 Europe as of Q1 2026). Institutional LP coverage — pensions, endowments, foundations, insurers, sovereign wealth funds, OCIOs, fund-of-funds, and RIAs — went live in February 2026.

Survey-based data reflects what allocators choose to share. OSINT-derived data reflects what allocators actually do. Self-reporting, layered on top of OSINT, begins to capture both — observed behavior validated by stated intent. The informed GP evaluates vendors by understanding which methodology produced the data, what human quality control sits on top of it, and which questions that combination can and cannot answer.

What does this look like in a real fundraise?

A growth equity GP raising a $300 million Fund II in climate technology needs allocators with active climate mandates and sufficient ticket size — not just family offices, but also endowments, foundations, and pensions with observable clean energy exposure.

A survey-based search returns allocators filtered by "climate" or "sustainability" as a stated preference. Some of those preferences were reported in 2023. Some contacts have changed roles. Some offices shifted mandates entirely.

The OSINT approach works differently. Filing scan identifies family offices that formed new entities with "climate," "energy," or "green" in the entity name within 12 months — and flags foundations whose 990-PF filings show new manager relationships with climate-focused GPs, plus pension funds whose board minutes reference updated allocation targets for energy transition. News monitoring surfaces allocators that participated in climate transactions — Breakthrough Energy Ventures deploying across three structures in January 2026, 8090 Industries and Benson Capital backing maritime decarbonization, Dr. Hans Riegel Holding (HARIBO) leading a sustainable packaging round, and public pension announcements of new climate-infrastructure mandates. Event intelligence flags allocators at climate conferences tracked across the Altss event calendar. Professional signals detect investment professionals who recently added climate or energy transition experience. Verification confirms contacts are current, deliverable, and hold decision-making authority — scored through the LP targeting framework.

The output is a prioritized set of verified allocators — family offices, endowments, foundations, pensions — with confirmed climate mandates, current contacts, and timing signals indicating receptivity. Not a static list filtered by a preference tag from two years ago.

What are the known limitations of OSINT for LP research?

No sourcing methodology — OSINT or survey-based — solves every problem in LP research.

OSINT cannot capture private intent. If a family office is quietly considering a first allocation to venture capital but has not formed entities, hired staff, attended conferences, or made public statements, that intent is invisible to any external methodology. Self-reporting — launched in 2026 — begins to address this gap by enabling allocators to voluntarily share stated preferences that OSINT cannot detect. But self-reporting is supplementary, not foundational. Coverage does not depend on it.

Coverage is jurisdictionally uneven. Countries with robust public filing requirements (United States, United Kingdom, Singapore) produce richer data than those with limited disclosure regimes. OSINT coverage of Middle Eastern family offices relies more heavily on news and event signals because regulatory filing data is scarcer.

Signal interpretation requires judgment. Automated systems detect that a family office formed a new entity. They cannot reliably determine whether that entity is for a new investment, a personal holding, or an estate planning vehicle. Altss applies editorial review at the interpretation stage, but ambiguity is inherent.

Contact data is perishable. Even with ≤30-day re-verification, a person can change roles between cycles. The methodology minimizes this window. It cannot eliminate it.

Fundraising teams should understand these limitations to calibrate expectations — not to discount the methodology, but to use it appropriately. The goal is a systematically better information position than operating without structured LP research.

Privacy, compliance, and ethical boundaries

OSINT operates exclusively within public and commercially available information. It does not involve accessing private systems, breaching security, or collecting data individuals have not made publicly available.

GDPR (EU), CCPA (California), PIPL (China), and a growing patchwork of US state privacy laws impose distinct requirements on data collection, storage, and commercial use. China's PIPL provides no legitimate interests basis; cross-border transfers require security assessments or Standard Contractual Clauses.

Altss maintains compliance infrastructure including documented legal basis assessments, transparency notices, and data handling procedures across jurisdictions. The platform does not offer bulk CSV export, API feeds, or CRM sync. This is a deliberate design constraint — not a technical limitation. Once contact data leaves a controlled environment, it gets resold, repackaged, and used for mass outreach that degrades data quality for every GP. The architecture protects allocator privacy and the long-term usability of the dataset.

Where is LP intelligence methodology heading?

Four developments will define the next phase of LP research infrastructure:

Filing digitization is accelerating. SEC EDGAR modernization, Singapore's expanding VCC registry, and the EU's push toward centralized corporate registries will increase the volume and timeliness of public filing data available for OSINT processing. More structured data means faster signal detection and fewer manual interpretation steps.

Privacy regulation is tightening. GDPR enforcement is intensifying. US state privacy laws are proliferating. The compliance cost of maintaining LP contact data across jurisdictions will continue rising. Platforms that treat compliance as an afterthought face increasing legal and reputational risk.

Allocator digital footprints are expanding. Family offices that historically maintained zero public presence are increasingly appearing at conferences, publishing thought leadership, and registering entities across jurisdictions. This expands the OSINT-addressable universe — though the most private allocators will remain difficult to cover through any external methodology.

Allocator self-reporting is becoming a two-way value exchange. The traditional model — vendors ask allocators for data, allocators get nothing in return — is breaking down. Altss is building self-reporting as a value exchange: allocators who claim and enrich their profiles gain visibility to relevant GPs and control over how they're represented. This creates a data channel that survey-based platforms have always relied on, but built on top of OSINT rather than instead of it. The combination — observed behavior as the foundation, stated intent as enrichment, human verification as quality control — is where LP intelligence converges.

The vendors that produce the most reliable LP intelligence will be those combining disciplined OSINT methodology with human verification, transparent disclosure of what their data can and cannot tell you, and increasingly, direct allocator participation that enriches rather than replaces public-source intelligence.

Frequently asked questions

What does OSINT stand for? Open source intelligence — intelligence derived from publicly or commercially available information. The term originated in national security and is now applied across corporate due diligence, competitive intelligence, and LP research. The Altss OSINT Intelligence Framework documents the full methodology.

Is OSINT the same as web scraping? No. Scraping is bulk data extraction. OSINT is a structured methodology: collection, cross-referencing against multiple independent sources, verification, and analysis. Scraped data is raw and unverified. OSINT-derived data is quality-controlled before reaching any user.

What public sources does Altss monitor? SEC filings (Form ADV, Form D, 13F), IRS 990-PF filings, state and international corporate registries (Companies House, ACRA, DIFC, Handelsregister), financial and regional press, conference registrations and speaker lists, professional network activity, and direct corporate announcements — 2,500+ sources across 40+ jurisdictions.

How often is Altss data verified? Core profiles undergo re-verification on a ≤30-day cadence. Every email and phone number is multi-provider deliverability tested. Median lag from signal detection to database availability: under 72 hours. Data points that cannot be verified against minimum two independent sources are flagged or removed — never surfaced as confirmed.

Is OSINT-based LP research legal? Yes. OSINT operates within publicly available information, consistent with GDPR, CCPA, PIPL, and applicable privacy laws. Altss maintains documented legal basis assessments across US, EU, UK, GCC, APAC, and LatAm jurisdictions.

How does Altss compare to Preqin or PitchBook for family office data? Preqin and PitchBook use survey-based methodology with 450+ analysts conducting desk research. They capture stated preferences and allocation targets — refreshed quarterly or semi-annually. Altss uses OSINT to monitor public signals continuously and capture observed behavior in near-real-time. Altss covers 9,000+ family offices versus typical survey-based coverage of 4,000–6,000. As of February 2026, Altss also provides institutional LP coverage (pensions, endowments, foundations, insurers, SWFs, OCIOs, fund-of-funds) applying the same OSINT methodology. The methodologies are complementary — they answer different questions about the same allocators. For a detailed comparison, see Top 5 LP Intelligence Platforms.

Does Altss cover institutional LPs beyond family offices? Yes. As of February 2026, Altss provides unified coverage of both family offices and institutional allocators — including public and corporate pensions, university and healthcare endowments, private and community foundations, insurance companies, sovereign wealth funds, OCIOs, fund-of-funds, and RIAs with alternatives exposure. Institutional profiles follow the same verification standards as family office profiles: mandate alignment, decision-chain routing, verified contacts, and ≤30-day refresh cycles. See the institutional LP taxonomy for classification methodology.

Can fundraising teams do OSINT-based LP research independently? Partially. Any team can monitor SEC filings, read 990-PFs, and track news. The challenge is doing it systematically across thousands of allocators with automated cross-referencing and verification. The First-Time Fund Manager Playbook includes guidance on starting manual OSINT research during early fundraising.

Does Altss use human analysts or is it fully automated? Both. Automated systems handle signal detection at scale — monitoring filings, news, professional profiles, and contact deliverability across 2,500+ sources. Human analysts handle what automation cannot: entity classification, mandate interpretation, decision-chain mapping, and resolving conflicting signals. Every OSINT-derived profile passes through human verification before it enters production. The human layer is not a spot-check — it is a required step in the intelligence cycle.

Can allocators update their own Altss profiles? Yes. As of 2026, Altss enables allocators to claim, update, and enrich their profiles directly — adding stated preferences, correcting details, and signaling mandate changes. Self-reported data is layered on top of the OSINT foundation and cross-referenced against observed behavior. This creates richer profiles without making coverage dependent on allocator participation. Self-reporting capabilities will expand throughout 2026 with additional value delivered to participating allocators.

What are the main limitations of OSINT for LP research? Coverage varies by jurisdiction — countries with robust filing requirements produce richer data. OSINT cannot capture private intent with no public expression, though self-reporting (launched 2026) begins to address this gap. Contact data decays even with frequent verification. Signal interpretation involves judgment calls — handled by human analysts, not purely automated — that can still be incorrect. These limitations apply to any external research methodology, not just OSINT.

Altss maintains OSINT-verified, human-reviewed intelligence on 9,000+ family offices and institutional LPs (pensions, endowments, foundations, insurers, SWFs, OCIOs, fund-of-funds) with 1.5M+ verified contacts globally. The methodology described in this article is the same one running in production. To evaluate whether it fits your fundraising workflow, see the platform.