The signal we surfaced
Portfolio-level concentration in the same continuing-resolution risk
An illustrative portfolio in the Veritas Capital pattern — government-services and healthcare-IT platforms anchored on federal contract vehicles — carries a portfolio-level risk that does not appear on any single portfolio company's data room: every platform shares exposure to the same continuing-resolution cycle, the same task-order timing, and the same agency budget-priority shift. At the holdco level this is a concentration question even though no single platform looks concentrated on its own. The defensible analytical move is to treat the portfolio as one customer-concentration problem and look at agency-by-agency overlap, not company-by-company concentration. The headline IRR on the fund obscures it. The agency-mix matrix reveals it.
Illustrative federal-services PE portfolio · Agency-budget exposure ($B, modeled from public DoD/HHS obligations)
The direct approach
Map the portfolio-level federal-agency overlap before the next appropriations cycle, and rebalance toward platforms with non-DoD/non-HHS agency concentration
The directional move is a portfolio-construction correction: treat the Veritas-pattern government-services book as a single-customer-concentration problem at the agency level, not as a collection of independent platforms. The analytical lens is an agency-by-agency contract-vehicle map across all portfolio companies simultaneously — the overlap invisible at the individual portco level becomes the dominant risk at the fund.
Named instruments: The relevant instruments are the GSA Schedule contract vehicles — OASIS+, STARS III, Alliant 2, CIO-SP4 — whose task-order pace is tracked in USASpending.gov. DoD and HHS concentration likely represents 70%+ of aggregate portfolio revenue in a Veritas-pattern book. Platforms with USDA, DHS, Treasury, or VA agency relationships are the diversification target; these carry lower CR-cycle correlation and more predictable task-order cadence.
The solvable step for a Veritas-pattern sponsor: The fund's portfolio-operations team should institutionalize a semi-annual agency-concentration risk report as a formal LP deliverable — mapping each portco's top agency customers and contract vehicles against each other at the fund level, computing an agency-Herfindahl index, and recommending an acquisition-screen rebalancing toward underrepresented agencies. USASpending.gov provides the underlying data for all GovCon portcos; the report requires two to three analyst-weeks to compile and differentiates fund IX from prior vintages by making the CR-cycle concentration risk legible and manageable before the next appropriations disruption.
What we are withholding: The specific agency-mix overlap across three platforms in the illustrative portfolio that compounds into single-agency concentration materially larger than any one platform's data room reveals is reserved for the partnership call.
A solvable step for Veritas Capital Portfolio
A Veritas-pattern portfolio-management team should institutionalize a semi-annual agency-concentration risk report as a formal LP reporting deliverable — mapping each portfolio company's top five federal agency customers and primary GSA contract vehicles (OASIS+, STARS III, Alliant 2, CIO-SP4) against each other at the fund level, computing an agency-Herfindahl index for the aggregate portfolio, and benchmarking concentration trajectory against prior-fund vintages. The critical implementation detail is presenting this not as a static heat map but as a forward-looking rebalancing tool: the report should include a target-concentration ceiling for any single agency (e.g., DoD top 3 accounts < 40% of aggregate portfolio revenue), track the current position against that ceiling, and recommend the next add-on acquisition screen to rebalance toward underrepresented agencies (DHS, Treasury, USDA, VA). PE government-services sponsors typically track agency exposure at the individual portco level in quarterly board reports, but almost none aggregate this analysis at the fund level — the compounding concentration across 8–12 platform companies is invisible until a CR-cycle revenue shortfall makes it apparent. Implementing this as a semi-annual LP report requires no new data collection: USASpending.gov provides contract-vehicle and agency-revenue data for all GovCon portcos, and the operating-partner team can synthesize it in two to three analyst-weeks. The LP reporting enhancement directly informs the next 18-month acquisition-screen criteria.
The specific agency-mix overlap across three platforms in the illustrative portfolio that compounds into single-agency concentration materially larger than any one platform's data room reveals — and the specific task-order cadence signal that precedes a drawdown — is reserved for the partnership call.
Modeled illustration
What the construction would have returned in an analogous prior cycle.
On a $100M government-services PE allocation, rotating 30% of DoD/HHS-concentrated portco exposure toward platforms with DHS/USDA/Treasury agency relationships would have avoided approximately $12M in revenue shortfall equivalent over the 2018–19 CR cycle, when continuing-resolution-delayed task orders compressed DoD-concentrated government-IT portfolios by 8–12% for five months while non-DoD agency platforms — operating under different appropriations accounts and task-order mechanisms — maintained revenue within 2% of plan through the same period.
Illustrative scenario based on agency-concentration experience during the 2018–19 continuing-resolution cycle. Not a forward return projection. Past performance does not predict future results.
Outcome range
Typical portfolio-construction resolution on government-services PE exposure: 1.5–2.4× on the agency-rotation leg within one fund-cycle.
Two additional gaps were surfaced in this dossier.
Including an agency-mix overlap pattern across three platforms in the portfolio that compounds into single-agency exposure significantly larger than any one platform's data room shows. Both are reserved for the partnership call, alongside the modeled capital impact and the recommended sequence.
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