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 — not just what's easy to find.
Mohamed Baddar
"Every capital allocation decision in early-stage investing is made with complete, unbiased information about the people and markets behind it."
Pre-Seed Round Confidential Mohamed Baddar · baddar@oneagentic.us
02
The Problem
VC Due Diligence
Is Built on Incomplete Information
Speed, network gaps, and inconsistent process mean every fund makes decisions without the full picture.
  • References are curated, not independent. Founders choose who you call. Back-channel checks only exist for investors with strong enough networks to run them — everyone else relies on what they're handed.
  • Speed and depth are in direct tension. The best deals close fast. Investors who slow down to dig carefully often lose — creating pressure to decide on less information on exactly the deals that matter most.
  • Network access determines diligence quality. A smaller fund without deep connections is at a real disadvantage, regardless of how sharp their judgment is. The process rewards who you know, not how carefully you look.
  • Feedback is slow, so patterns stay invisible. Bad investments surface in 1–2 years, but full resolution takes 7–10. A fund can have a repeating process issue for years before the pattern becomes undeniable.
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Image placeholder
e.g. process diagram / stat visual
03
The Gap
The Same Process
Misses in Both Directions
Current tools don't close either gap — they surface what's easy to find and stop there.
Investing in the wrong deal
Curated signals look compelling at speed

Vanity metrics, handpicked references, inflated prior exit narratives — a motivated founder can construct a convincing picture that holds up under a quick pass but not structured verification.

Passing on the right deal
Exceptional founders without strong signals are hard to spot

First-time founders, operators from outside the network, or builders who are quiet about their work often produce weak surface signals. Pattern-matching on visibility systematically passes over this cohort.

No existing tool is built to reduce both risks at once. Most address one, partially.
04
The Solution
One Agentic — AI Due Diligence Workspace
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 their investment thesis — stage, markets, team signals, what they won't invest in. Every deal is evaluated against this lens, not a generic checklist.

Automated Enrichment
Deep, Multi-Source Research

NEO — the product's AI agent — automatically enriches every deal: 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 — cross-source inconsistencies, implausible absences — the product surfaces it explicitly, with sources.

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Product screenshot or workflow diagram placeholder
Not a decision engine. The product flags; the investor decides.
05
Product Mechanics
The Human-in-the-Loop
Diligence Cycle
A structured loop where the VC directs and the AI executes — until there's enough to decide.
1
Deal enters through any path
Pitch deck, company URL, founder name, or intake form. The product extracts what it needs from whatever it receives.
2
NEO enriches automatically
Founder background signals, company context, market references, public signals — evaluated against the fund's thesis and workflow.
3
Product surfaces signal inconsistencies
When independent sources disagree — on founding date, prior exit, co-founder relationships — each flag is specific and sourced.
4
VC reviews and directs next steps
Move forward, pass, or direct NEO to go deeper — in plain language. NEO executes and returns with results.
5
Loop continues until decision-ready
A complete, sourced diligence picture in under 20 minutes for first pass. Hours of manual research, compressed.
06
Timing
Why This Product Is Possible
Right Now
Four converging forces make this buildable and buyable today.
Capability Threshold
LLMs crossed the quality bar

Synthesizing unstructured data from multiple sources into a trusted diligence output required a quality of reasoning that wasn't there two years ago. It is now.

Market Pressure
Deal cycles are compressing

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

Data Availability
Founder data is more accessible than ever

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

Market Readiness
Specific beats generic, right now

After two years of generic AI tools, investors are ready for something purpose-built. Domain-specific wins over impressive-but-broad.

07
Target Customer
Who We're Building For
Early-stage VC funds — the ones who need this most and are most ready to act.
Fund Profile
  • Pre-Seed through Series A focus
  • Team: 115 people
  • 50200+ inbound deals/month
  • 530 deals in active evaluation
  • No dedicated research infrastructure — operating lean, relying on analysts and Google rather than purpose-built tooling.
  • Price-sensitive to enterprise tools — Harmonic ($25K+/yr) and PitchBook ($15–30K/seat) are out of reach for most of this segment.
Two Primary Users
The Analyst / Associate

1–3 years in. Spends 40–60% of time on research tasks. Becomes a daily user if the product makes them 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 they'll rely on it in a Monday meeting.

08
Market Opportunity
A Large Market With No Dominant Workflow Tool
Sized by users and deal flow — conservative by design, with meaningful upside.
TAM — VC + Angels
8,000 VC funds + 150,000 active angel investors
Extended TAM — With Adjacent Segments
Adds PE firms, corporate VC, and family offices (~ in additional addressable spend)
Year 1 reachable: North America (~2,000–2,500 funds) + Western Europe (~1,500–2,000). All figures derived from pricing model — adjust in Pricing Variables.
09
Business Model
Tiered Seats + Shared Credit Pool
Not per-seat locked-in. A 5-person fund pays one price regardless of who runs the workflows.
Seed
TBD
/ month, billed annually
2 seats · Small fund credit pool
Max
TBD
/ month, billed annually
Unlimited seats · High-volume pool
Enterprise
Custom
Annual contract
Unlimited seats · No pool cap · SSO · API · Dedicated CSM
Revenue Structure

Self-serve tiers — no sales conversation required. Credits shared org-wide, not per-seat. Unused add-on credits roll over; included credits reset on renewal. Top-up purchases are an expansion revenue lever.

Unit Economics

Avg fund spend: ~$175/mo · ~$2,100/yr. Gross margin: ~67%. 3× markup on API cost. Workflow mix: 90% standard / 10% deep research.

10
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.
Harmonic
Best-in-class sourcing. Scout AI maps markets and researches founders. ~$25–30K/yr.
↳ Finds deals. We evaluate them.
Affinity
Dominant CRM. Deep relationship intelligence. "Diligence" = organizing what your team already knows. ~$10–13.5K/yr (5 seats).
↳ Tracks the relationship. We evaluate what's behind it.
Attio
Modern CRM, growing AI layer. No founder research, no risk synthesis. ~$1,740/yr (5 seats).
↳ Manages pipeline. We produce diligence depth.
Clay
GTM enrichment + Claygent. Tech-forward analysts build their own version. No VC-specific product.
↳ DIY. We deliver purpose-built, out-of-the-box.
AlphaSense / PitchBook
Enterprise-grade, $15–50K+/yr. Built for LP reports and large fund research — not early-stage founder evaluation.
↳ Purpose-built, 10× cheaper, right fit.
ChatGPT / Claude / Perplexity
The ambient alternative. Any analyst can do faster research. No structure, no proprietary data, no persistent deal context.
↳ Beat on structure, consistency, and trust.
We sit between Attio and Affinity on price. Shared credit pool — a team of five costs the same whether two or all five run workflows.
11
Our Differentiators
The Compounding Moat
Not workflow features — those can be replicated in days. What actually compounds.
Signal Verification
Reasoning across independent sources to find what doesn't align — not aggregation, but active cross-referencing. The more deals we run, the better this gets.
VC-Specific Intelligence
A general model can research a founder. It can't do it through the lens of a seed-stage investor running a thesis-driven fund. We encode how great investors actually think — and sharpen it with every deal.
Speed That Changes Deal Dynamics
First-pass output in under 20 minutes. Not just efficiency — it changes which deals get a look. A two-person fund can evaluate every inbound deal, not just the ones that survived the backlog.
Consistency Across the Pipeline
Every deal evaluated with the same depth, same questions, same signals — regardless of who ran it or how busy the week was. Governance quality, not just analytical quality.
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Competitive positioning matrix / moat diagram placeholder
The defensible combination: better agents on better data, both improving with usage.
12
Traction & Product State
Working Product, Pre-Revenue
Core workflow is end-to-end. We know exactly what's working and what we're not pretending about.
This slide needs your actual metrics before the deck goes external. Add: number of deals processed, design partner names/logos, customer quotes, NPS if available, key usage stats. Placeholder content below.
✓ What's Working
  • Core diligence workflow runs end-to-end. Stable and repeatable on real deal inputs.
  • Automated enrichment from public sources. No manual steps required to kick off a diligence run.
  • AI synthesis outperforms analyst research at speed. Materially better output in a fraction of the time.
  • Ready to onboard design partners. Product is stable enough for real deals with real VC users.
⚡ Before Launch
  • Run 20 real deals with VC users.
  • 3 paying pilot customers on live deals.
  • Onboarding to first output <20 min.
  • Partner-grade output quality. Consistent enough for a Monday partner meeting.
TBD
Deals processed in testing
TBD
Design partners / pilot funds
TBD
Revenue / LOIs
13
The Team
Built by People Who Understand the Problem
Photo
Mohamed Baddar
CEO & Co-Founder
Background in [domain]. Add 1–2 lines on why you are uniquely positioned to build this — relevant exits, VC network, domain expertise.
Photo
Bemwa Malak
[Title] & Co-Founder
[Background, relevant experience, why now. Replace with real content.]
Photo
Mohamed El-Deeb
[Title]
[Background, engineering / product / AI expertise. Replace with real content.]
Photo
Amr El-Zidy
[Title]
[Background, relevant expertise. Replace with real content.]
Advisors & Backers: [Add advisor names, VC firm affiliations, any notable angels or existing commitments]
14
Go-to-Market
Self-Serve Launch → Direct Enterprise Sales
The first 10 reference customers are more important than the first 100 trial signups.
Phase 1 — At Launch
Self-Serve (Seed, Series, Max)
No sales conversation required. Funds sign up, pick a tier, and start. Build early usage data and reference customers before committing to enterprise infrastructure.
Phase 2 — Post-Launch
Direct Enterprise Sales
Founder-led outreach to partners and GPs at funds matching the target profile. Structured onboarding call, white-glove pilot, then close.
Signature Move
The Retrospective Pilot
Run the product against a fund's last 5 completed deals — show what One Agentic would have surfaced vs. what actually happened. A proof point, not a demo.
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GTM funnel / timeline visual
Best targets: Harmonic users without an evaluation layer · Clay/Notion DIY builders · Attio/Affinity users without diligence depth.
15
The Ask
Raising to Launch & Prove
Fill in the raise details before sharing externally. Required: raise amount, instrument (SAFE / priced round), valuation cap or pre-money, and use-of-proceeds breakdown.
Raise Amount
[TBD]
Pre-Seed round. Instrument: [SAFE / Priced Round]. Cap / pre-money: [TBD].
Use of Proceeds
18 months runway
[Fill in breakdown, e.g.:]
~60% Engineering (2 hires)
~20% Data coverage & licensing
~20% GTM / early sales
Milestones This Round Unlocks
  • Launch self-serve product with real paying customers
  • 3+ design partners on live deals
  • 20+ real deals run & validated
  • Enterprise tier built & first enterprise contract closed
What We Need From Investors
  • Capital to reach launch and first revenue
  • Warm introductions to target fund partners
  • Connections to VC analysts — the day-to-day champions
  • Guidance on enterprise deal structure & LP dynamics
Mohamed Baddar CEO & Co-Founder, One Agentic baddar@oneagentic.us
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