INVESTOR PRESENTATION
CLIP™
Every title your contracts let you sell
Reads 80–200-page licensing contracts and shows studio sales teams what they can sell, where and when.
70+ studio contracts processed · ≥94% accuracy · pilot underway
80–200 pages
per contract
2–3 weeks
to interpret each one
6+ places
rules are scattered
~15% revenue
of deal value leaks
“A title sat unsold in a territory for 9+ months because a holdback clause was buried deep in the contract.”
Generic chat AI doesn’t fix it: 68% baseline accuracy, with gaps and hallucinations.
Contracts
Approved rules
Eligible titles
Revenue
Contract review: 2–3 weeks → <2 hours. Watch the 90-second demo →
Module 1 · Extract
Turns 80–200-page contracts into approved rules: qualifiers, windows, caps, rates.
Module 2 · Eligibility & forecast
Scores 12,000+ titles in ~60s: eligible now, conditional, forecast, make-eligible.
12,000+
Titles scored
In ~60 seconds, every active contract
~$120K
Per seat, per year
vs $15M+ unlocked per studio (modeled)
≥94%
Field-level accuracy
vs 68% baseline (human-verified)
50+
studio buyers and SMEs interviewed
70+
contracts processed
≥94%
field accuracy vs 68% chat AI
<2 hrs
contract review, down from 2–3 weeks
VELOCITY
Design partner
Discussing pilot deployment at leading studio to process their real contracts
Pipeline
3 studios in talks
Next
First paid seats in 3 months (February 2026) at ~$120K per seat per year
Insight: Legacy approach makes scenario modelling difficult, unable to quickly capture all eligible titles for sale
What incumbents do
CLM tools find clauses, then stop
Rights systems need rules keyed by hand
Chat AI: one document, 68% accuracy
What we learned in studios
Sellers need rules per title
Value is in what makes a title sellable
Do not optimize title sale across territories, caps, qualifiers, etc. to full revenue potential
Why now: streaming, FAST and territory splits multiply rules per deal
LLMs now read 80–200-page contracts reliably with human approval
Enables simulating multiple scenarios to maximize deal across territories, distribution channels, commercial incentives and timeframes.
RR
Raj Rangaswamy
Co-founder & CEO
Growth & Customer Experience
9 years building and leading AI solution sales at top SIs
Former CIO, Tech M&A (PE)
Two exits
Sold to M&E and digital natives
Scaled AI sales and GTM businesses from zero
PS
Praveen S
Co-Founder & CTO
Lead Solution Architect – M&E & AI
8 yrs M&E AI lead at 2 of the Top 5 Studios
Solution architecture – rights distribution expert
Lead software engineer at startups and large enterprises
Advisors
Market Insights
Market Insights and Product Validation
M&E SME executive
M&E studio EVP
Technology CXO
Startup founder
$B software sales SVP
200+
studios & distributors
×
1–20
seats per studio
×
~$120K
per seat, per year
=
$24M–$480M
Initial wedge (studio seats), a year
$130B+
Licensing revenue in contracts to read
Expansion
Distributors, buyers, music rights and other media contracts on the same engine.
Who PAYS?
200+ studios and distributors, 1–20 seats each, ~$120K per seat.
Global
Territories and languages
50–200
Contracts per studio
Quarterly
Reviews per live deal
Validated
Spoke with 60+ prospects and SMEs
*Source: Global TV and Video Distribution & Licensing Market Data Report; $122.38B parent market baseline, $170B by 2031 (Link).
CHANNEL
Top 50+
Big 4, GSIs, Consulting firms
M&E Practice Leaders
Value add to existing MSAs
THE BUYER
200+
Studios
Distribution Sales
Potential: 1–20 users/studio
CHANNEL
Top 20+
ERP & DAM software firms
CXOs
Bolt-on to boost IP & LTV
Near term
Contract-level extraction & eligibility insights for studio sales teams
Long term
Seller + Buyer driven insights, SaaS integrated with DAM, Music rights and other contracts
1
Licensing rule ontology
2
Catalog-grounded eligibility
3
Rule library that grows with every contract
4
Team from two Top 5 US studios
| Category | Examples | What they do well | Gap CLIP™ fills |
| Rights & royalty systems | Rightsline, FilmTrack, Vistex | System of record for rights and royalties | Rules keyed in by hand; CLIP™ extracts and feeds them |
| Contract lifecycle (CLM) AI | Icertis, Ironclad | Contract storage, workflow and clause search | Horizontal; no M&E licensing ontology or title-level eligibility |
| GSIs & Big 4 consulting | Accenture, Infosys, TCS, Deloitte, PwC, EY, KPMG | Large transformations, custom builds, rights audits | Project-based; also our channel via existing MSAs |
| Generic chat AI | ChatGPT, Copilot, Gemini | Fast summaries of single documents | 68% baseline accuracy; gaps, hallucinations, no audit trail |
| In-house builds | Studio IT and data teams | Fit to internal systems | Slow and costly to maintain; no cross-studio rule library |
1
DEPLOY to a studio
Go live with design partner studios
Q1 · WIN
Design-partner pilot live
2
POC on their data
Real contracts and catalog from source systems
Q2 · PROVE
≥94% on their contracts
First paid seats
3
INTEGRATE and configure
Configure rules and workflows to their process
Q3 · CONVERT
3 studios · 6 seats · ~$0.7M ARR
4
EXPAND users and studios
Package the proven playbook for more studios and distributors
Q4 · SCALE
5 studios · 10 seats · ~$1.2M ARR
THE VISION: CLIP™ becomes the licensing operations center for media, used by every studio, distributor and buyer to extract, forecast and execute licensing strategy.
THE ASK
$750K
Target: 5 paid studios → ~$1.2M ARR
Use of funds
12 months
Sales & Marketing 48% · $360,000
Studio sales, pilots and GSI partner channel
Product & Integrations 40% · $300,000
Engineering hires, roadmap and customer integrations
Operations 12% · $90,000
Cloud, security, legal and admin
Total $750,000 · 100% · 12 months of runway
Thank you
Raj Rangaswamy
+1 213 260 0012
LinkedIn: Raj Rangaswamy
Los Angeles, USA
video-clip.growthpanda.co
clipcore.growthpanda.co
ANNEX
Supporting detail
Module 1: Extraction · Module 2: Eligibility & forecasting · Architecture and trust · Revenue forecast
Four rule segments
Qualifiers: which titles?
Windows: when licensed?
Caps: how many, how often?
Rate cards: what cost?
Why the approach works
Licensing rule ontology across all 4 segments
Specialist LLM per segment
Multi-modal: tables, redlines
Grounded in catalog context
Agentic RAG orchestration
≥94%*
Extraction accuracy
< 2 hours
End to end, with human checkpoints
Versioned rule decks
Every confirmation saved
Audit trail
Every edit logged, source lookup
Confidence scoring
Flags fields under 70%
*Source: ≥94% field-level accuracy verified across 70+ recently processed contracts.
Real-time eligibility
Rules score every catalog title against every active contract.
12,000+ titles in ~60 seconds
Manual overrides and approval
Humans approve every extracted rule before it runs.
Confidence badges guide focus
Eligible now
Window open
Conditional
Awaiting trigger
Forecast
Future window
Make-eligible
Playbook to unlock
12-month forecast
Rolling horizon
Scenario modeling
What-if projections
Export insights
SAP, Tableau, CRM
1
Plan
Plans retrieval
2
Tool use
Scoped calls
3
Ground
Catalog checks
4
Verify
Golden rules
5
Approve
Human locks
6
Observe
Traced, costed
Groundedness and accuracy
Values grounded in catalog
Every rule cites its clause, and is traceable
≥94% field-level accuracy (70+ contracts)
Tools, cost and observability
Scoped, logged tool calls
Cost tracked per contract
Every run traced, replayable
Human in the loop: AI is cheap to call but hard to make reliably correct.
Open integration layer: Forecasts export to SAP, Tableau and studio systems.
| Milestone | Studios | Paid seats | MRR | ARR | Gross margin |
| 6 months | 5 | 10 | $100K | $1.2M | 30% ($360K) |
| 12 months | 10 | 50 | $500K | $6M | 50% ($3.0M) |
| 18 months | 40 | 200 | $2M | $24M | 60% ($14.4M) |
| 24 months | 100 | 500 | $5M | $60M | 80% ($48.0M) |
~$120K
per seat, per year
$60M ARR
24 mo · 500 seats · 80% GM