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SWARMGOOD
Investor brief

The AI operating system for the impact economy.

SWARMGOOD starts with foundation-sponsored grantee cohorts, then expands into portfolio intelligence, CSR reporting, public grant infrastructure, and verified impact systems.

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Download one-pager (PDF)Seed deck outline + risk appendix (PDF)
Thesis

Funding workflows are software workflows.

The funding system is relationship-heavy, paperwork-heavy, and proof-heavy. Foundations want stronger grantees. Nonprofits need capacity. Companies need credible community impact. Public agencies need better grant infrastructure. SWARMGOOD turns those workflows into software.

Market

A $600B annual flow of capital running on PDFs and email.

$557B
US giving (2023)
Annual philanthropic capital moving through grant workflows.
$90B+
US foundation grants
From ~120,000 active US foundations deploying capital each year.
1.8M
US nonprofits
Plus 10M+ globally — every one of them runs the same broken workflow.
$26B
Corporate CSR spend
Adjacent budget pool with identical reporting demands.
SAM (Foundations)
12,000 mid-to-large US foundations × $50K ACV = $600M

Beachhead. One foundation contract activates 50–200 grantees.

SAM (CSR + Public)
5,000 CSR programs + 2,500 public grantmakers × $100K ACV = $750M

Expansion. Same workflow, larger contract size.

TAM (Impact OS)
Verified-impact data, portfolio intelligence, reporting infra ≈ $5B+

Long arc. Becomes the data layer for the impact economy.

Traction

Pipeline, pilots, and proof.

0
Foundations in pilot pipeline
Letters of interest from program officers.
0
Grantee orgs reachable today
Through pilot foundation rosters.
0
Hours saved (pilot avg / cohort)
Across discovery, drafting, and reporting.
0/100
Avg Impact Confidence
Across audited pilot submissions.
GTM

Foundation-first distribution.

    01
    Start with foundations
    02
    Foundation buys cohort access
    03
    Grantees onboard instantly
    04
    Usage creates workflow intelligence
    05
    Funder sees ROI
    06
    Foundation renews and refers
Competitive landscape

Why incumbents don't win this market.

Instrumentl, GrantStation
Database search. No drafting, no evidence, no reporting.
We replace the workflow, not the search box.
Submittable, Fluxx
Funder-side application intake. Optimized for the foundation, not the grantee.
We make grantees ready before they ever submit.
Generic LLM tools (ChatGPT, Claude)
Blank canvas. No org context, no evidence audit, no compliance.
We turn agents into a coordinated, audited team with human review.
Consultants & grant writers
$80–$200/hr. Doesn't scale. Inconsistent quality.
Foundation-funded access at <$250/grantee/yr.
Unit economics

Capital efficient by design.

$75K
Blended foundation ACV
Weighted across Cohort, Scale, Enterprise tiers.
7x
LTV / CAC (target)
Foundation contracts renew annually; intros compound.
<5 mo
CAC payback
Warm-intro sales motion through program officer networks.
70%+
Gross margin at scale
Agent compute amortized across cohort, not per seat.
Flywheel

Distribution compounds.

01
Foundation sponsors access
02
Grantees use Swarm
03
Agents improve workflows
04
Organizations produce stronger work
05
Funders see ROI
06
Foundations renew and refer
07
More cohorts join
Moat

What compounds over time.

Foundation distribution
Grantee workflow data
Evidence graph
Impact Confidence Score
Relationship Gap Score
Agent orchestration
Founder credibility
Ethical trust layer
Vertical templates by program type
Portfolio-level reporting
Team

Operators who lived the problem.

Shaun Tai
Founder & CEO

15+ yrs running BRIDGEGOOD. 900+ graduates, $69M+ alumni earnings. Lived the grantee side of every workflow we automate.

Agent & AI team
Engineering

Multi-agent orchestration, evidence retrieval, and structured-output pipelines. Hiring eng leads now.

Advisor bench
Foundations, AI safety, GTM

Program officers from national foundations, AI researchers, and B2B SaaS GTM operators.

Risk & mitigation

What we watch, and how we de-risk it.

Foundation sales cycles are long

We sell capacity-building, not software. Existing budget line item, 60–90 day cycles via warm intros.

Grantee trust in AI

Human approval gate on every output. Evidence-first design. No extractive data model. Founder credibility carries it.

LLM commoditization

Moat is workflow data, evidence graph, foundation distribution, and signature scores — not the model.

Regulatory & compliance

SOC 2 roadmap, role-based permissions, data residency options. No PII training. Aligned with funder data norms.

The ask

$3.5M Seed to win the foundation beachhead.

  • Round
    $3.5M Seed
  • Use of funds
    40% product & agent infra, 30% foundation GTM, 20% pilot delivery, 10% ops
  • Milestones (18 mo)
    12 foundation contracts, 600+ active grantees, $1.2M ARR, Series A ready
  • Existing capital
    Founder-funded prototype + grant capital from BRIDGEGOOD operations
Founder-market fit

Founder has lived the problem.

Shaun Tai built BRIDGEGOOD over 15+ years, helping launch 900+ creatives of color with $69M+ in alumni earnings. SWARMGOOD is the infrastructure he wishes he had from day one.

FAQ

Investor questions.

For investors

Back the operating system for the impact economy.

We're sharing the data room, financial model, and pilot pipeline with aligned funds and angels. Get in touch.

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