Investor Deck

Planerix

AI Execution Platform for Data-Driven Companies

Turn business data into insights, decisions, and automated execution.

Founder: Kyrylo Prolieiev

Planerix connects analytics, AI agents, and operational workflows to help companies continuously improve performance.

Core narrative

From passive analytics to measurable execution

Planerix visual
Connect fragmented data
Generate AI recommendations
Drive operational execution
Track impact

2. Mission

Transform business data into operational decisions and continuous execution

Most companies collect large volumes of data from CRM, marketing, finance, and operations. Very few can convert this data into clear actions that improve performance.

Planerix bridges this gap by connecting analytics, AI agents, and execution in one platform.

Vision

The next generation of companies will be operated through AI-assisted systems

Instead of manually reviewing dashboards and coordinating across disconnected tools, organizations will rely on systems that continuously analyze performance, detect problems, and recommend actions.

Planerix is building the execution layer for AI-driven companies.

3. Problem

Businesses operate with fragmented tools

Modern companies use many software products that store valuable data but rarely interact with each other.

CRM systemsMarketing platformsAnalytics dashboardsFinance toolsProject management systems
Data remains fragmented
Insights are difficult to produce
Decisions are slow
Teams lack operational clarity

Core operational question still unanswered: What should we do next to improve performance?

The AI adoption gap

The issue is not lack of data

Many organizations invest in analytics and AI technologies but struggle to generate measurable business value. The main blocker is the missing connection between insight generation and operational execution.

Data and reports exist
Execution systems are disconnected
Planerix closes this gap

4. Solution

Planerix is the execution layer for business data

Instead of static dashboards, companies get an operational system that continuously analyzes performance and drives actions.

Step 1

Integrate business systems

Connect CRM, ads, analytics, finance, and operational software.

Step 2

Normalize and analyze

Create one trusted operational layer from fragmented source data.

Step 3

Detect insights with AI

Use AI agents to identify patterns, bottlenecks, and opportunities.

Step 4

Generate actions

Convert findings into recommendations teams can execute immediately.

Step 5

Track impact

Measure delivered actions against business outcomes and KPI deltas.

5. Product

A unified operational data platform

Planerix integrates data from multiple business sources and processes it through a structured AI-ready architecture.

CRM systemsAdvertising platformsWeb analytics toolsFinancial systemsInternal databases and APIs

Architecture flow

Layer 1

Data ingestion

Layer 2

Data warehouse

Layer 3

Semantic analytics layer

Layer 4

AI agents

Delivered value in product

Planerix architecture visual
Analytics dashboards
AI recommendations
Automated workflows
Execution tracking
Static reporting tells teams what happened. Planerix tells teams what to do next, then tracks whether it worked.
Open one-pager PDF

6. Value Proposition

Planerix transforms passive analytics into execution

Operational clarity

One system connecting marketing, sales, finance, and operations.

AI-driven insights

Agents continuously analyze performance and detect problems early.

Faster decision making

Teams receive actionable recommendations instead of static reports.

Execution tracking

Strategic goals connect directly with actions and measurable outcomes.

7. Why Now

Technology conditions now support AI-driven execution platforms

Explosion of business data

Companies now generate large operational datasets across SaaS stacks.

Maturity of AI technologies

Modern models can interpret complex data and produce practical actions.

Fragmented SaaS ecosystems

Organizations use many disconnected tools and need one unifying layer.

Demand for automation

Leaders expect systems that automate analytics and decision support.

8. Market

Large and expanding software category overlap

Planerix operates at the intersection of Business Intelligence, workflow automation, AI productivity, and business process automation.

Business Intelligence
Workflow automation
AI productivity platforms
Business process automation

Initial target segments

Multi-location businessesFranchise networksEducation organizationsE-commerce companiesDigital-first service businesses

SaaS market trajectory (USD Bn)

Toward nearly USD 800B by decade end

2024

2026

2028

2030

2024

USD 320B

2026

USD 470B

2028

USD 620B

2030

USD 790B

Illustrative trend aligned with publicly discussed SaaS expansion toward the end of the decade.

9. Competition

Current tools solve parts of the problem

Most companies stitch multiple systems together. These tools rarely connect analytics with operational execution in one governed loop.

Analytics tools

Tableau

Looker

Power BI

Workflow tools

Monday

ClickUp

Asana

Automation platforms

Zapier

Make

n8n

Planerix advantage

One platform, four critical layers

Data integration
Analytics
AI decision support
Execution workflows

10. Business Model

Hybrid SaaS plus usage-based pricing

Subscription revenue provides predictability, while usage-based components align revenue with customer activity and value.

Free

$0

Up to 4 users

Manual-first AI assistant

No external integrations

1 GB storage

Self-serve onboarding

Pro

EUR 249 / month

Up to 10 users

Operational agent bundle

5 org + 3 personal/user

10 GB storage

Dedicated paid rollout

Enterprise

Custom pricing

Custom infrastructure

Custom AI policy

Custom connector scope

Custom storage model

Dedicated infra + SLA

11. Usage-Based Revenue

Credits align pricing with delivered value

Additional usage is priced through credits and expansion bundles. The model keeps entry lightweight for free workspaces, while Pro and Enterprise scale with real operational usage.

AI operations
Data processing
Storage expansion
Automation workloads

Pricing structure

Base subscription

Predictable recurring platform revenue

Usage credits

AI token packs and automation expansion tied to usage

Expansion levers

Users, storage, integrations, and dedicated infra

12. Unit Economics

Margins improve as infrastructure scales

AI-driven software starts with lower gross margins than traditional SaaS, then improves through optimization and platform efficiency.

Gross margin targets

Year 1

Year 2

Year 3

Year 1

63%

Year 2

70%

Year 3

75%

Customer acquisition economics

CAC target

EUR 1,200 - 2,500

Average revenue per account

Approx. EUR 420 / month

CAC payback

8-12 months

LTV/CAC target

4-6x (healthy benchmark: 3:1)

LTV/CAC positioning

Target: 4-6x, above the common healthy SaaS benchmark of 3:1.

13. Go-To-Market Strategy

Phased rollout from pilots to platform scale

Initial focus is high-value early adopters with measurable operational pain.

Phase 1 - Pilot customers

Founder-led sales

Industry networks

Direct outreach

Phase 2 - Vertical expansion

Industry-specific solutions

Consulting partnerships

Integration partners

Phase 3 - Platform growth

Ecosystem integrations

AI agent marketplace

International expansion

Early target segments

Education networks, franchise businesses, and data-driven SMEs.

14. Traction

Early production pilot is active

Data integration pipelines
Marketing analytics
Attribution analysis
AI agent framework

Milestones

Next delivery targets

Additional pilot customers
Expanded AI agent catalog
Production SaaS platform

15. Financial Projections

Conservative 3-year growth scenario

The model assumes steady adoption through founder-led sales, direct outreach, and industry partnerships.

Year 1

20

customers

Approx. EUR 8k MRR

Approx. EUR 100k annual revenue

Year 2

60

customers

Approx. EUR 25k MRR

Approx. EUR 300k annual revenue

Year 3

150

customers

Approx. EUR 65k MRR

Approx. EUR 780k annual revenue

Customer growth

Year 1

Year 2

Year 3

MRR growth (EUR k)

Year 1

Year 2

Year 3

16. Team

Kyrylo Prolieiev - Founder

Data architect and AI systems developer with experience building analytics infrastructures and operational execution platforms.

Data architectureAI systemsBusiness analyticsProcess automation

Execution founder profile

Deep technical focus on data and automation systems
Product vision centered on measurable business impact
Lean go-to-market with pilot-first execution strategy

17. The Ask

Planerix is raising a pre-seed round

Capital will accelerate product development and market entry, with focus on engineering throughput and customer acquisition.

Platform engineering
AI agent development
Customer acquisition
Product scaling

Investment objective

Establish Planerix as the execution layer for AI-driven organizations

Faster roadmap delivery
Broader pilot footprint
Stronger commercial repeatability

18. Final Slide

Planerix

AI Execution Platform for Data-Driven Companies

Founder: Kyrylo Prolieiev

Investor contact

Planerix - AI Execution Platform for Data-Driven Companies

Open the product, request founder materials, or schedule a focused investor walkthrough.