One Agentic Investor Pitch
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AI Due Diligence
for Venture Capital
The first workspace where VC teams see what they're actually investing in.
"Every capital allocation decision in early-stage investing is made with complete, unbiased information about the people and markets behind it."
Mohamed Baddar Pre-Seed baddar@oneagentic.us
02
Faisal, overwhelmed
The Story
Meet Faisal — a VC analyst in Riyadh.
Two years at an early-stage fund. Sharp, ambitious, and the first screen for every deal that comes in.
  • His fund sees 200+ inbound startups a month
  • He spends 40–60% of his time on repetitive research
  • Partners expect decision-ready answers by the next partner meeting
  • The region's deal flow keeps growing
03
The Problem
Faisal overwhelmed
200 startups a month. One Faisal.
200
Inbound deals / month
1
Analyst on first screen
The math doesn't work

A proper first pass takes hours per deal — founder background, market sizing, claim verification. At 200 deals, that's months of work compressed into weeks. Triage by gut feel is the only option left.

And quality is uneven

Depth depends on who ran the check and how busy the week was. References are curated by founders. Network gaps decide what gets verified. The process rewards who you know, not how carefully you look.

04
The Cost
So Faisal ghosts founders. Everyone loses.
Conversations he started go quiet — not from carelessness, but from arithmetic. Here is what changes with an AI analyst at his side:
Faisal today
  • ~20 deals get a real look
  • Red flags found only if time allows
  • Quiet gems slip past the funnel
  • Founders left waiting
Faisal at 10×
  • Every inbound deal screened
  • Red flags surfaced automatically, with sources
  • Promising outliers ranked into view
  • Every founder gets an answer
A founder waiting on a reply that never comes
The silent end of a conversation a founder thought was going well.
05
Faisal, smiling
+
NEO
The Solution
Meet NEO — Faisal's AI diligence analyst.
One Agentic is an AI workspace where VC teams run structured due diligence — configured around how each fund actually invests.
Thesis Capture
Fund-Specific Evaluation

Each fund encodes its investment thesis — stage, markets, team signals, exclusions. Every deal is evaluated against that lens, not a generic checklist.

Automated Enrichment
Deep, Multi-Source Research

NEO enriches every deal automatically: founder background, company context, market signals, public records — synthesized into a structured, sourced output.

Signal Verification
Surface What Doesn't Align

When independent sources tell different stories about the same fact, the product flags it explicitly, with sources. NEO flags; the investor decides.

Not a decision engine. Faisal directs every step — NEO does the hours of work in minutes.
06
Faisal, smiling
+
NEO
Product Mechanics
Inbox to decision-ready in 20 minutes.
1
Deal stage
Deal In
2
NEONEO
Enrichment
3
Signal flagsSignal flags
Signals
4
VC reviewVC review
Review
5
LoopLoop
Decision
1
Deal enters through any path
2
NEO enriches automatically
3
Signal inconsistencies surface
4
The VC reviews and directs
5
Loop continues until decision-ready
07
Timing
Why now? Four forces just converged.
AI
AI can now reason across messy, conflicting data
Data
Founder signals have never been more accessible
Speed
Deal cycles are compressing
Demand
Investors are done with generic AI tools
Each force alone was interesting. Together, they make this the right moment.
Capability Threshold
AI can now reason across messy, conflicting data

The ability to reason across incomplete, conflicting data — and arrive at a structured, sourced conclusion — required a threshold of model capability that simply didn't exist two years ago. It does now.

Data Availability
Founder signals have never been more accessible

Public signals, social footprints, court records, company filings — the raw material exists and is growing. No one has built the synthesis layer for VC.

Market Pressure
Deal cycles are compressing

Funds that move faster without losing depth have a real edge — and they know it. Willingness to pay for speed is measurable and growing.

Market Readiness
Investors are done with generic AI tools

A tool built for VC diligence knows what signals matter at pre-seed versus Series A, how a well-prepared deck obscures risk, and what questions a founder didn't expect. Generic tools don't. That gap is what funds are now willing to pay to close.

08
Target Customer
Built for lean, early-stage funds.
1–15
Team
50–200
Deals/mo
Fund Profile
  • Pre-Seed through Series A focus
  • Team: 1–15 people
  • 50–200+ inbound deals/month
  • 5–30 deals in active evaluation
  • No dedicated research infrastructure
  • Also serves angels, accelerators, and corporate VC
Two Primary Users
The Analyst — Faisal

1–3 years in. Spends 40–60% of time on research. Becomes a daily user because the product makes him sharper in partner meetings and faster on triage.

The Partner

Reviews deals, relies on analyst output. Uses the product to gut-check, not to grind. Needs to trust the output before relying on it in a partner meeting.

09
Market Opportunity
Every early investor is a Faisal.
VC funds, angels, accelerators, and corporate VC — sized bottom-up from the pricing model.
—
SOM
The first funds we can win through the KSA & MENA beachhead
—
SAM
Early-stage investors who fit NEO's diligence workflow today
—
TAM
Every investor worldwide who runs diligence on private companies
Beachhead: KSA and the US, simultaneously. MENA and Western Europe in the next wave.
10
Business Model
Simple, credit-based subscriptions.
Usage-based credits shared across the whole fund — priced from measured production costs, not guesses.
Micro
—
/ month
Solo GP / scout fund
Seed
—
/ month
Classic seed fund
Mid
—
/ month
Active early-stage fund
Established
—
/ month
Full early-stage team
Why credits

Credits track real consumption, are shared across the whole fund, and unused credits roll forward.

Unit Economics

Overage credits are the expansion lever. Self-serve first; Enterprise tier follows with SSO, API, and dedicated CSM.

11
Competitive Landscape
We evaluate. They source or track.
Every competitor stops at the boundary we start from. None are built around the full diligence workflow.
ProductEvaluates DealsBuilt for VCVerifies SignalsAccessible Pricing
One Agentic✓✓✓✓
Harmonic◐✓✗✗
Affinity✗✓✗✗
PitchBook◐◐✗✗
Clay (enrichment)◐✗✗◐
ChatGPT / Claude / Perplexity◐✗✗✓
✓ full  ·  ◐ partial  ·  ✗ not offered
12
Our Differentiators
Better agents on better data, compounding.
Signal Verification
We flag what doesn't align. You decide.
Encoded VC Intelligence
Your fund's lens on every deal.
Signal verification Signal verification
Signal Verification
We detect what doesn't align: the claim that contradicts the filing, the exit story that changes between articles, the signal deliberately buried. Detection compounds — every deal run sharpens it.
Encoded VC Intelligence
We encode what signals matter at pre-seed vs Series A, what patterns indicate resilience vs polish, what a well-prepared deck was designed to avoid. General AI can research a founder. It can't do it through the lens of a seed-stage investor with a thesis.
Partner-Grade Trust
An analyst can tolerate noise. A partner cannot. We build to the higher bar: consistent output with a clear audit trail for every flagged signal — the kind a partner puts in front of a Monday meeting and stands behind.
Simplicity as Moat
VCs won't configure complex tooling. Every setup step is a reason to stop. Our bar: a partner with no technical background reaches a trusted output without asking for help. In a market of complicated tools, that's genuinely hard to copy.
The defensible combination: Every deal generates proprietary signals only we hold — workflow features alone can be copied; this loop can't.
13
Traction & Product State
Working product. Ready for design partners.
The core diligence workflow runs end-to-end. Now stress-testing it on real deals before we open the doors.
✓ Built & Working
  • Core diligence workflow runs end-to-end
  • Automated enrichment from public sources
  • Pricing grounded in 100 measured production sessions
  • AI synthesis outpaces analyst research
⚡ Launch Gates
  • Run 20 real deals with VC users
  • 3 paying pilot customers on live deals
  • Onboarding to first output under 20 min
  • Partner-grade output quality
✓
Full diligence workflow, live today
20
Real deals to run with VC users before launch
3
Paying pilots required before we scale
14
The Team
The people behind NEO.
Mohamed Baddar
Mohamed Baddar
CEO
CEO & Co-Founder
23 Years exp
Drives product vision, investor strategy, and the commercial direction of the company.
Deep AI practitioner who has spent his career at the intersection of intelligence systems and real-world product decisions. Leads strategy, investor strategy, and product vision for NEO.
Mohamed El-Deeb
Mohamed El-Deeb
Founding AI Engineer
Founding AI Engineer & Co-Founder
10 Years exp
Designed the AI layer behind NEO's enrichment and signal-verification pipelines.
AI engineer with deep experience in AI product management. Designed the AI layer behind NEO's enrichment and signal-verification pipelines.
Amr El-Zidy
Amr El-Zidy
Sales & Operations
Sales & Operations Co-Founder
16 Years exp
Owns the customer pipeline, fund partnerships, and go-to-market execution.
Drives revenue and go-to-market execution. Owns the customer pipeline, fund partnerships, and the operational systems that keep the business running at pace.
15
The Ask
Help every Faisal answer every founder.
$1M
Pre-Seed · SAFE
$1M
Pre-Seed · SAFE
Capital Deployment
60%
Engineering
20%
Data & Licensing
20%
GTM
What MVP Lab Unlocks
Launch in the right ecosystem
KSA's venture ecosystem is the ideal beachhead — concentrated deal flow, lean fund teams, and direct access to the exact funds, accelerators, and CVC units we're built for.
What We Need From Partners
  • Capital to reach launch and first revenue
  • Warm introductions to fund partners & accelerators
  • Connections to VC partners
  • Access to GP feedback on live deal output
Mohamed Baddar CEO & Co-Founder, One Agentic baddar@oneagentic.us
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