OSINT Methodology for LP Intelligence: The 2026 Edition

OSINT Methodology for LP Intelligence: The 2026 Edition#

The LP intelligence market is built on a broken model: surveys that allocators ignore, quarterly updates that go stale within weeks, and coverage gaps that systematically miss the most important family offices and institutional investors — Altss replaces that with OSINT-driven, continuously refreshed profiles verified by human analysts across 40+ jurisdictions, now covering 30,000+ institutional investors, RIAs, and family offices with a sub-30-day update cycle.

Why Sourcing Methodology Matters More Than Sales Decks#

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 depth and nuance that OSINT alone cannot capture, such as forward-looking allocation plans, internal investment committee dynamics, and relationship preferences. Self-reported data is flagged as such, so GPs know exactly which layer of the intelligence stack they are viewing.

The Survey Problem: Why Legacy Databases Miss the Most Important Allocators#

The survey-based model has three structural weaknesses that no amount of analyst hours can fully fix: non-response bias, staleness, and entity misclassification.

Non-Response Bias#

Surveys depend on willing participants. The allocators most likely to respond to Preqin or PitchBook surveys are those with dedicated marketing teams, public-facing investment mandates, and institutional infrastructure. The allocators least likely to respond are precisely the ones GPs most want to reach: single-family offices with no public presence, endowments run by a two-person team, sovereign wealth funds with strict confidentiality policies.

Consider the numbers. Preqin claims coverage of 4,000+ family offices. PitchBook claims 3,500+. FINTRX claims 8,000+. Altss tracks 9,000+ family offices globally — and the difference is not exaggeration. It is methodology. The legacy databases simply cannot find allocators that do not respond to surveys. Altss finds them through public filings, property records, board registrations, and news mentions that no survey would ever capture.

A concrete example: the Mittelstand family offices in Germany. These are typically structured as private holding companies, not labeled "family office" in any public registry. They do not respond to English-language surveys. They do not have websites advertising their investment mandates. They appear in OSINT signals — board seats in portfolio companies, property acquisitions, charitable foundation registrations — that legacy databases never connect to the family office entity. Altss identifies these entities through multi-source triangulation: a GmbH filing in Munich, a foundation registration in Liechtenstein, a news mention of a direct investment in a Mittelstand tech company. No survey would ever reach them.

Staleness#

Survey data is snapshot data. Preqin publishes updates quarterly. PitchBook updates on a similar cadence. FINTRX claims "real-time" updates but relies on manual research cycles that average 60-90 days for any given profile.

In private markets, a quarter is an eternity. A pension fund can change its co-investment mandate in a single board meeting. A family office can hire a new CIO who shifts the entire allocation strategy. A sovereign wealth fund can announce a new strategic partnership that opens a direct investment channel. By the time the survey catches up — if it catches up at all — the opportunity is gone.

Altss operates on a sub-30-day refresh cycle for LP data. That means every profile in the database is reviewed and updated at least once per month. For high-activity allocators — those with recent fund commitments, personnel changes, or mandate shifts — the refresh cycle can be as short as 7 days. The system detects signals continuously; human analysts prioritize updates based on signal intensity and allocator importance.

The difference is measurable. In a 2025 internal audit, Altss found that 23% of institutional LP profiles on legacy platforms had at least one material error — wrong contact, outdated mandate, misclassified entity type — compared to 4% on Altss. The primary driver: staleness. Legacy platforms simply do not refresh profiles fast enough to catch the changes that matter.

Entity Misclassification#

Survey-based databases classify allocators based on how allocators classify themselves — or, more often, based on how a junior analyst interprets a survey response. The result is systematic misclassification that leads GPs to waste outreach on the wrong allocator type.

A family office that operates through a registered investment advisor (RIA) structure might appear as an RIA in PitchBook, even though its investment behavior is purely family-office. A pension fund that outsources its private equity allocation to an OCIO might appear as a direct investor, even though the OCIO makes all decisions. A foundation that invests through a donor-advised fund might appear as a foundation, even though the DAF sponsor controls the allocation.

Altss solves this through entity resolution — a multi-source classification system that examines legal structure, investment behavior, regulatory registration, and professional signals to determine the correct allocator type. A family office that files Form ADV as an RIA is classified as a family office, not an RIA, because the OSINT signals — board composition, investment history, ownership structure — confirm the family office identity. A pension fund that uses an OCIO is flagged as "OCIO-dependent," with the OCIO entity linked in the profile.

The result: GPs using Altss see the allocator as it actually operates, not as it appears on a survey form.

The OSINT Stack: Six Layers of Intelligence#

Altss builds LP profiles from six distinct OSINT layers, each capturing a different dimension of allocator behavior. No single layer is sufficient. The intelligence product emerges from the intersection of all six.

Layer 1: Regulatory Filings#

Regulatory filings are the most reliable source of structured LP data — when you can find them. The challenge is that filings are fragmented across 40+ jurisdictions, each with its own format, language, and accessibility.

Altss ingests filings from:

• SEC Form ADV (US): Mandatory for RIAs managing over $110M. Contains AUM, client types, custody arrangements, and disciplinary history. Used to identify family offices operating as RIAs and to verify AUM for institutional allocators.
• IRS Form 990-PF (US): Mandatory for private foundations. Contains grant-making activity, investment income, and asset allocation. Used to track foundation investment behavior and identify family offices that operate through foundations.
• Companies House (UK): Mandatory filings for UK companies, including family offices structured as limited companies. Contains director names, ownership structures, and financial statements.
• Handelsregister (Germany): German commercial register. Used to identify Mittelstand family offices and GmbH-structured allocators.
• Registro Imprese (Italy): Italian business register. Similar coverage for Italian family offices and foundations.
• ORBIS / Bureau van Dijk: Cross-jurisdictional corporate registry data. Used for entity resolution and ownership mapping.
• ESMA registers (EU): Alternative investment fund manager registers. Used to identify allocators that manage third-party capital.
• Monetary Authority of Singapore registers: Used for Singapore-based family offices and institutional allocators.
• Dubai International Financial Centre (DIFC) registers: Used for Middle East family offices and sovereign wealth funds.

Each filing type requires different parsing logic. Form ADV is structured but uses inconsistent naming conventions across advisors. Form 990-PF is semi-structured, with investment data buried in schedules that vary by foundation size. Companies House filings are structured but often incomplete for investment-related data.

Altss uses automated parsers for each filing type, trained on 50,000+ historical filings. The parsers extract key fields — AUM, asset allocation, investment committee members, recent commitments — and feed them into the profile construction pipeline. Human analysts review every parsed filing for accuracy, particularly for edge cases like multi-strategy allocators or entities that file under multiple names.

The regulatory filing layer is particularly valuable for tracking AUM changes. When a pension fund files an updated Form ADV with a 15% AUM increase, that signal triggers an immediate profile update — no need to wait for a survey response.

Layer 2: News Monitoring#

News is the highest-frequency OSINT signal for allocator behavior. A single news article can reveal a new mandate, a personnel change, a co-investment opportunity, or a strategic shift that no filing would capture for months.

Altss monitors 10,000+ news sources across 15 languages, including:

• Financial press: Financial Times, Wall Street Journal, Bloomberg, Reuters, Nikkei, Handelsblatt, Il Sole 24 Ore, Expansion
• Trade publications: Private Equity International, Buyouts, Secondaries Investor, Infrastructure Investor, PERE, Agri Investor, Family Capital, Campden FB
• Regional press: Local business journals covering pension funds, endowments, and family offices in secondary markets
• Regulatory news: SEC announcements, FCA updates, ESMA consultations
• Academic and institutional press: University endowment news, foundation annual reports, pension fund board meeting minutes

The news monitoring system uses natural language processing (NLP) to extract entities, relationships, and events. When a news article mentions "California Public Employees' Retirement System (CalPERS) commits $500M to buyout fund," the system identifies CalPERS as the allocator, $500M as the commitment amount, and buyout as the strategy. It then checks the existing CalPERS profile for consistency — does the commitment match the current mandate? Is the fund manager already in the database? Is the commitment size within normal range?

Human analysts review every extracted event before it enters the profile. The NLP system is good at identifying entities and amounts; it is terrible at interpreting context. A news article that says "CalPERS reduces private equity allocation by 10%" is straightforward. An article that says "CalPERS considers reducing private equity allocation" is not — it is speculation, not fact. The analyst determines which is which.

The news layer is also the primary source for personnel changes. When a family office hires a new CIO, the news often breaks before any filing is updated. Altss captures that signal, updates the profile, and flags the change for GPs who have the allocator in their target list.

Specific examples of news-driven intelligence:

• January 2026: *Financial Times* reports that the Ontario Teachers' Pension Plan is increasing its co-investment allocation to 30% of private equity portfolio. Altss updates the OTPP profile within 24 hours, flagging the co-investment opportunity for GPs.
• March 2026: *Campden FB* publishes a profile of a Singapore-based single-family office that has never appeared in any LP database. Altss identifies the entity, constructs a profile from the article and cross-referenced public filings, and adds it to the database within 48 hours.
• May 2026: *PERE* reports that a Middle Eastern sovereign wealth fund is launching a dedicated real estate platform. Altss captures the signal, updates the SWF profile, and creates a new entity profile for the real estate platform.

Layer 3: Professional Activity Signals#

Professional activity signals capture allocator behavior that never makes the news. These are the small signals — conference attendance, speaking engagements, board memberships, advisory roles — that reveal where allocators are focusing their attention and who they are willing to meet.

Altss monitors:

• Conference speaker lists: SuperReturn, IPEM, PEI Forum, Milken Institute Global Conference, Sohn Conference, Family Office Forum, Campden Wealth events. Allocators who speak at these events are signaling openness to new relationships.
• Conference attendee lists: Where available, Altss ingests attendee lists from major conferences. An allocator who attends Infrastructure Investor Global Summit is likely focused on infrastructure — a signal that complements the stated mandate.
• Board memberships: Allocator representatives who sit on portfolio company boards are signaling hands-on investment approach and potential co-investment appetite.
• Advisory roles: Allocators who serve on advisory boards of fund managers, industry associations, or academic institutions are signaling network access and willingness to engage.
• Professional social media: Public LinkedIn profiles of allocator personnel. Altss monitors for job changes, new connections, and posted content that reveals investment focus.

The professional activity layer is particularly valuable for emerging managers. A first-time fund manager cannot compete with Blackstone for meetings at SuperReturn. But they can identify allocators who are speaking at the conference, research their mandates, and target outreach based on the allocator's publicly stated interests.

Example: A GP raising a climate tech fund discovers through Altss that a European pension fund's CIO is speaking on a panel at IPEM titled "Allocating to Climate Tech: A Practitioner's Perspective." The GP uses this signal to tailor outreach — referencing the panel, acknowledging the CIO's expertise, and positioning the fund as aligned with the CIO's stated interests. The meeting converts into a commitment.

Layer 4: Contact Verification and Enrichment#

Contact data is the most fragile layer of any LP database. Email addresses change. Phone numbers go dead. Decision-makers move to new allocators. A database with perfect coverage and accurate mandates is useless if the contact information is wrong.

Altss uses a multi-provider verification system that cross-references contact data across six sources:

• ZoomInfo: Business contact database. Used for initial contact discovery and verification.
• Lusha: Contact enrichment platform. Used for phone number and email verification.
• Apollo.io: Sales intelligence platform. Used for email verification and professional context.
• Hunter.io: Email finder and verifier. Used for domain-specific email discovery.
• LinkedIn Sales Navigator: Professional network data. Used for role verification and job change detection.
• Internal verification: Altss's own verification system, which checks contact data against public filings, news mentions, and professional signals.

Every contact in the database is verified against at least three sources before it is published. If a contact's email address appears in ZoomInfo and Lusha but is not confirmed by any public source, the contact is flagged as "unverified" until a human analyst confirms it through direct outreach or public record.

The verification system also detects job changes. When a LinkedIn profile updates to a new allocator, the system flags the change, updates the old allocator profile, and creates a new contact record for the new allocator. GPs who had the allocator in their target list receive a notification: "Your contact at XYZ Pension Fund has moved to ABC Family Office. Updated contact information is available."

Specific metrics: Altss maintains a 92% email deliverability rate for verified contacts, compared to industry averages of 60-70% for survey-based databases. The difference is the multi-provider verification system and the sub-30-day refresh cycle that catches changes before contacts go stale.

Layer 5: Entity Resolution and Relationship Mapping#

Entity resolution is the intelligence layer that connects the dots between disparate OSINT signals. A single allocator may appear under multiple names, legal structures, and jurisdictions. A family office might operate through a holding company, a foundation, and an RIA — all with different names and registrations. A pension fund might have separate entities for public equity, private equity, and real estate allocations.

Altss uses a graph-based entity resolution system that links related entities through shared ownership, personnel, and investment behavior. The system identifies:

• Parent-subsidiary relationships: A sovereign wealth fund that operates through multiple investment vehicles
• Sibling relationships: Multiple family offices controlled by the same family
• Personnel overlap: The same investment professional appearing across multiple allocator entities
• Investment overlap: Multiple allocators co-investing in the same fund or portfolio company

The entity resolution system is trained on 150,000+ private-markets entities and their relationships. It uses a combination of rule-based matching (exact name matches, tax ID matches) and machine learning (name similarity, address similarity, personnel overlap scoring) to identify relationships that would be invisible to a human analyst.

Example: A GP researching a Saudi Arabian family office discovers through Altss that the family office is linked to a Dubai-based holding company, a London-based RIA, and a Geneva-based foundation — all controlled by the same family. The GP can now target the entire allocator network, not just the single entity originally identified.

Layer 6: Human Verification and Editorial Judgment#

The final layer is human. Every OSINT-derived profile passes through a human verification process before it reaches production. Automated systems detect signals; human analysts confirm interpretation, resolve ambiguity, and apply editorial judgment.

The human verification process has four stages:

1. Signal aggregation: Automated systems collect all OSINT signals for a given allocator and present them in a structured format.
2. Signal verification: A human analyst reviews each signal for accuracy and relevance. Is the news article reliable? Does the filing match the allocator's current structure? Is the professional signal still current?
3. Profile construction: The analyst constructs the allocator profile from verified signals, adding context and interpretation that no algorithm can provide. This includes entity classification, mandate interpretation, decision-chain mapping, and relationship identification.
4. Quality review: A second analyst reviews the completed profile for consistency, completeness, and accuracy. Profiles that fail quality review are returned to the construction stage.

The human verification layer is particularly important for:

• Entity classification: Determining whether an entity is a family office, pension fund, endowment, foundation, or sovereign wealth fund — often ambiguous from filings alone.
• Mandate interpretation: Interpreting investment mandate descriptions that are vague, outdated, or contradictory across sources.
• Decision-chain mapping: Identifying who actually makes investment decisions — the CIO, the investment committee, the board, the family patriarch — and mapping the decision process.
• Relationship identification: Identifying relationships between allocators that are not captured by automated entity resolution.

The cost of human verification is significant. Altss employs 40+ full-time analysts across three continents, each specializing in specific allocator types and jurisdictions. But the investment produces a qualitatively different product: verified intelligence, not just data.

Coverage Expansion: From Family Offices to Institutional LPs#

Altss began with family office coverage — 9,000+ entities tracked globally — and expanded to institutional LPs in February 2026. The institutional coverage now includes:

• Pension funds: 3,500+ public and private pension funds across 40+ jurisdictions, including the largest by AUM (CalPERS, ABP, CPP Investments, GIC, NPS, GPIF)
• Endowments and foundations: 2,000+ university endowments and private foundations, including the Ivy League endowments and major philanthropic foundations (Gates Foundation, Wellcome Trust, Ford Foundation)
• Insurance companies: 1,500+ insurers with private markets allocations, including general accounts and separate accounts
• Sovereign wealth funds: 200+ SWFs tracked globally, including the largest by AUM (Norway GPFG, ADIA, China Investment Corporation, Kuwait Investment Authority, Qatar Investment Authority)
• OCIOs: 500+ outsourced CIO firms and investment consultants that manage allocator portfolios
• Fund-of-funds: 1,000+ fund-of-funds and secondary funds that invest in private markets

The institutional coverage expansion uses the same OSINT methodology as the family office database. Regulatory filings are the primary source — Form ADV for US-based allocators, equivalent filings for non-US allocators (FCA filings in the UK, AMF filings in France, BaFin filings in Germany). News monitoring and professional signals provide the high-frequency updates. Human verification ensures accuracy.

The result: 30,000+ institutional investors, RIAs, and family offices tracked on a sub-30-day refresh cycle, with 150,000+ private-markets entities in the relationship graph.

What OSINT Misses: The Limitations of Public Sources#

OSINT is powerful, but it is not perfect. Every layer has blind spots that GPs must understand.

Regulatory Filing Limitations#

Regulatory filings are only as good as the filing requirements. In jurisdictions with weak disclosure requirements — many Middle Eastern and Asian allocators — filings provide minimal information. Some allocators file under multiple names or jurisdictions, making entity resolution difficult. Others file with significant delays — Form 990-PF can be filed up to 12 months after the fiscal year end.

Altss compensates by cross-referencing multiple filing types and supplementing with news and professional signals. But for allocators in low-disclosure jurisdictions, the OSINT profile may be thinner than for allocators in high-disclosure jurisdictions like the US or UK.

News Monitoring Limitations#

News monitoring captures only what gets reported. Most allocator activity — particularly for smaller family offices and endowments — never makes the news. Even when it does, the reporting may be inaccurate, incomplete, or biased.

Altss compensates by monitoring a broad range of sources, including regional and trade publications that cover allocator activity overlooked by mainstream financial press. But the news layer is inherently incomplete for allocators that operate below the media radar.

Professional Signal Limitations#

Professional signals — conference attendance, board memberships, social media activity — are voluntary. Allocators who choose not to attend conferences, serve on boards, or maintain public LinkedIn profiles generate no professional signals. This is particularly common among single-family offices that prioritize privacy.

Altss compensates by using professional signals as a supplementary layer, not a primary source. The core profile comes from regulatory filings and news; professional signals add context and timeliness.

Contact Verification Limitations#

Contact verification depends on the quality of the underlying data sources. ZoomInfo and Lusha are excellent for US and European contacts but weaker for Asia and the Middle East. LinkedIn Sales Navigator is global but depends on individual allocators maintaining their profiles.

Altss compensates by using a multi-provider system that cross-references sources across jurisdictions. For allocators in low-coverage regions, the verification system relies more heavily on public filings and direct outreach.

Human Verification Limitations#

Human verification is expensive and slow. Altss prioritizes verification for high-priority allocators — those with large AUM, active mandates, or recent news signals — and uses automated systems for lower-priority allocators. The result is a tiered verification system: verified profiles for the most important allocators, automated profiles for the long tail.

GPs should understand which tier an allocator profile belongs to. Altss flags verification status clearly: "Verified by human analyst" vs. "Automated profile, pending verification."

Practical Guidance for Fund Managers#

The OSINT methodology produces a fundamentally different LP database. But GPs need to use it differently too. Here is practical guidance for getting the most out of OSINT-driven LP intelligence.

Diligence Your Data Provider#

Before buying any LP database, ask the provider:

• What is your sourcing methodology? Survey-based, OSINT-based, or hybrid?
• What is your refresh cycle? Quarterly, monthly, weekly, or continuous?
• How do you verify contacts? Single source, multi-provider, or human verification?
• What is your coverage rate for non-responsive allocators? How many allocators have never responded to a survey but are in your database?
• Can you provide a sample of allocator profiles with sourcing metadata? Show me the specific filings, news articles, and professional signals that support each data point.

Most providers cannot answer these questions. Altss can, because the methodology is built to be transparent.

Target Allocators, Not Just Entities#

The entity resolution layer reveals that most allocators are networks, not single entities. A family office may have three investment vehicles, two foundations, and a holding company — all investing in private markets. A pension fund may have separate entities for different asset classes, each with its own decision-makers.

Use the relationship graph to target the entire allocator network, not just the entity that appears in a filing. If the pension fund's private equity entity is not accepting new relationships, the real estate entity might be — and the same CIO may oversee both.

Use News Signals for Timing#

News signals reveal when allocators are actively deploying capital. A pension fund that announces a $500M commitment to buyout is signaling that it has capacity for more. A family office that hires a new CIO is signaling a potential mandate shift. A sovereign wealth fund that launches a new platform is signaling a strategic pivot.

Time your outreach to these signals. A GP who reaches out to a pension fund within 30 days of a large commitment is more likely to get a meeting than one who reaches out six months later, when the allocation cycle has moved on.

Verify Before You Reach Out#

Even the best OSINT database has errors. Before sending an email or making a call, verify the contact information through your own research. Check the allocator's website. Confirm the contact's role through LinkedIn. Call the allocator's main line and ask for the investment team.

The multi-provider verification system is a starting point, not an endpoint. GPs who verify before outreach achieve 3x higher response rates than those who rely solely on the database.

Track Your Results#

The OSINT methodology produces measurable results. Track your outreach by allocator type, contact source, and verification status. Which allocator types respond best? Which contact sources produce the highest conversion rates? Which verification methods produce the most accurate contacts?

Use the data to refine your targeting. Over time, you will develop a proprietary intelligence advantage that no database can replicate.

The 2026 Landscape: What Has Changed#

The LP intelligence market has shifted significantly since 2025. Three developments are reshaping the landscape:

The BlackRock-Preqin Acquisition#

BlackRock's acquisition of Preqin closed in early 2026, creating a vertically integrated data giant. Preqin's survey-based LP database now sits alongside BlackRock's Aladdin platform, connecting allocator data to portfolio management tools.

The acquisition has two implications for GPs. First, Preqin data will become more expensive as BlackRock integrates it into its broader product suite. Second, Preqin's survey-based methodology will not change — BlackRock is unlikely to invest in OSINT infrastructure when the survey model already produces adequate margins.

The result: GPs who rely on Preqin will pay more for the same data, with the same coverage gaps and staleness issues.

The Rise of OSINT-Native Platforms#

Altss is not the only OSINT-native LP intelligence platform. A handful of startups have emerged, each with a different approach to OSINT collection. Some focus on regulatory filings, others on news monitoring, others on professional signals.

The differentiation is in the integration layer. Altss combines all six OSINT layers into a single platform, with human verification and entity resolution that no other platform matches. The 9,000+ family office coverage and 30,000+ institutional LP coverage are the result of years of investment in OSINT infrastructure and analyst talent.

The Self-Reporting Experiment#

Self-reporting is the newest layer in LP intelligence. Altss began enabling allocator self-reporting in early 2026, allowing allocators to claim, update, and enrich their own profiles directly on the platform.

The early results are promising. Allocators who self-report provide richer data than OSINT alone can capture — forward-looking allocation plans, internal decision-making processes, relationship preferences. But self-reporting also introduces bias: allocators who self-report are more likely to be those who want to be found, which is the same bias that plagues survey-based databases.

Altss handles this by flagging self-reported data as such, and by maintaining OSINT-derived profiles as the baseline. Self-reported data supplements, not replaces, the OSINT foundation.

Case Studies: OSINT in Action#

Case Study 1: The Invisible Family Office#

A GP raising a growth equity fund identified a target allocator: a European family office that had never appeared in any LP database. The family office had no website, no public filings, no news mentions. It was invisible to survey-based platforms.

Altss found the family office through a combination of signals:

• Property records: The family office was registered as the owner of a commercial property in Geneva, filed with the Swiss commercial register.
• Board registrations: The family office's principal appeared on the board of a portfolio company, listed in the Luxembourg business register.
• Foundation filings: The family operated a charitable foundation in Liechtenstein, with investment data filed in the foundation's annual report.
• News monitoring: A regional Swiss business journal mentioned the family office in a story about a direct investment in a medtech company.

Altss constructed the family office profile from these signals, verified the contact information through multi-provider verification, and added the profile to the database. The GP used the profile to target outreach, referencing the medtech investment in the introduction. The family office responded within a week.

Case Study 2: The Mandate Shift#

A GP raising a secondaries fund had been targeting a US pension fund for six months with no response. The pension fund's stated mandate in legacy databases was "buyout and growth equity" — no secondaries allocation.

Altss detected a signal: the pension fund had filed an updated Form ADV with a new line item for "secondaries investments." The filing was three weeks old. No legacy database had caught the change.

Altss updated the pension fund profile, flagged the mandate shift, and notified the GP. The GP rewrote the outreach, referencing the new secondaries mandate and positioning the fund as aligned. The pension fund responded within two weeks.

Case Study 3: The Decision-Maker Change#

A GP had built a relationship with a family office CIO over two years. The relationship was progressing toward a commitment. Then the CIO stopped responding to emails.

Altss detected the reason: the CIO had left the family office for a new role at a pension fund. The change was captured through a LinkedIn profile update, confirmed by a news article in *Family Capital*.

Altss updated both allocator profiles — removing the CIO from the family office, adding the CIO to the pension fund — and notified the GP. The GP reached out to the CIO at the new allocator, referencing the existing relationship. The CIO responded, and the GP is now pursuing a commitment from the pension fund.

Conclusion: The Intelligence Advantage#

The LP intelligence market is not a commodity market. The sourcing methodology behind the data determines what you can find, how quickly you can find it, and how confident you can be in the information.

Survey-based databases are adequate for the allocators that want to be found. They fail for the allocators that matter most: the family offices that never respond to surveys, the pension funds that change mandates overnight, the sovereign wealth funds that operate below the media radar.

OSINT-based intelligence is the only way to find those allocators. It is harder to build, more expensive to maintain, and requires a combination of automated signal detection and human judgment that few platforms achieve. But it produces a fundamentally different intelligence product: verified, current, and complete.

Altss is the only platform that combines all six OSINT layers — regulatory filings, news monitoring, professional signals, contact verification, entity resolution, and human verification — into a single, continuously refreshed database of 9,000+ family offices and 30,000+ institutional investors, RIAs, and family offices. The institutional LP coverage, live since February 2026, extends the same methodology to the full allocator universe.

For GPs raising capital in 2026, the intelligence advantage is the difference between targeting the allocators everyone targets and finding the allocators everyone misses. OSINT methodology is the foundation of that advantage.

Altss tracks 9,000+ family offices and 30,000+ institutional investors, RIAs, and family offices globally, with profiles built from OSINT — not surveys — and verified by human analysts on a sub-30-day refresh cycle. The platform covers 150,000+ private-markets entities across 40+ jurisdictions. For fund managers and emerging GPs raising capital, Altss provides the intelligence to find allocators that legacy databases systematically miss. Visit altss.com to learn more.