Title Slide
Postman for fine-tuning
The Problem
The Problem
Enterprise AI adoption is stalled by privacy & complexity
Enterprise AI is Broken
Complex infrastructure, data privacy concerns, and prohibitive costs block 70% of enterprises from adopting AI.
Data Privacy Risk
Sending sensitive IP to OpenAI/Anthropic is a non-starter. Data leakage is the #1 concern.
GPU Scarcity & Cost
Cloud GPUs are expensive and hard to reserve. Training costs are skyrocketing.
Complexity Cliff
Fine-tuning requires deep ML expertise that most teams lack.
Our Solution
The Solution
Prompt-to-production workflow for local AI
One Command
pip install langtrain-aiSetup in under 5 minutes. No dependency hell. Pre-configured environments.
100% Private
Data never leaves your GPU
Cost Efficient
Use commodity hardware
How It Works
How It Works
From data to deployed model in 3 simple steps
Prepare Dataset
Import CSV/JSON or connect DB. Visual editor for cleaning and formatting.
Fine-Tune Locally
One-click training with SFT/LoRA. Real-time metrics and auto-tuning.
Export & Deploy
Export GGUF/ONNX to use anywhere. Push to HuggingFace or S3.
Technical Moat
Technical Moat
Deep optimization that competitors can't easily replicate
Optimized Local Training
Custom CUDA kernels for 2x faster LoRA on consumer GPUs
Zero Data Leakage
No network calls during training. Verified by security audits
Memory Efficient
Fine-tune 7B models on 8GB VRAM with gradient checkpointing
Universal Model Support
Llama, Mistral, Phi, Gemma, Qwen – all major architectures
Smart Data Pipeline
Auto tokenization, augmentation, and quality scoring
Continuous Improvement
Weekly updates with latest research (DoRA, FSDP2)
Product
Langtrain Studio
Desktop app for macOS, Windows & Linux
Traction
Traction
Strong organic growth driven by community
Market Opportunity
Market Opportunity
The Shift to Local & Private AI
$50B
AI/ML Platform Market
$15B
Fine-tuning & MLOps
$500M
Privacy-first (Year 3)
35%
CAGR through 2028
Explosive growth in edge AI computing
Business Model
Business Model
Open core with enterprise-grade features
Open Source
Langtrain Pro
Enterprise
Unit Economics
Unit Economics
Strong margins with path to profitability
$50
Community-led growth keeps acquisition low
$480
24-month avg subscription lifetime
9.6x
Well above 3x benchmark
70%
Gross Margin
Primarily software-based, minimal infrastructure costs
Revenue Projections
Revenue Projections
Conservative growth based on current trajectory
2024
$120K
500 users
2025
$800K
3K users
2026
$3M
12K users
2027
$10M
40K users
Target ARR by Series A
$1.5M
Current Runway
18 Months
Growth Curve
Go-To-Market
Go-To-Market Strategy
Community-led growth with enterprise expansion
Build Community
Convert to Pro
Enterprise Sales
Roadmap
Product Roadmap
Building the AI Workbench — from local-first to enterprise-ready
Foundation
- Studio v1.0 Launch
- macOS + Windows
- SFT & LoRA Training
Scale
- Cloud Sync
- Model Registry
- Team Workspaces
Ecosystem
- Plugin Marketplace
- API Access
- Multi-GPU
Platform
- Enterprise SSO
- Managed Cloud
- Partner SDK
Why We Win
Why We Win
The only developer-first, local-first fine-tuning platform
The Landscape
What we understand
Teams don't want another cloud service. They want reproducible, fast, cost-efficient fine-tuning on their own GPUs.
Team
The Team
Technical execution meets business strategy
Pritesh Raj
Founder & CEO
Built Langtrain end-to-end. Former applied research at DRDO. Kaggle Expert. Experience fine-tuning LLMs on large domain datasets.
Anjali
Co-Founder
8+ years in enterprise software. Lead Business Analyst at ArcelorMittal. Leads product strategy, pricing, and customer discovery.
The Ask
The Ask
Raising Seed to accelerate growth
Pre-Seed Round
40%
Product
Studio features & infra
35%
Growth
Community & marketing
25%
Operations
Team & legal
Contact
Let's Talk
Building the future of AI fine-tuning
langtrain.xyz
pritesh@langtrain.xyz
Postman for fine-tuning
The Problem
Enterprise AI adoption is stalled by privacy & complexity
Enterprise AI is Broken
Complex infrastructure, data privacy concerns, and prohibitive costs block 70% of enterprises from adopting AI.
Data Privacy Risk
Sending sensitive IP to OpenAI/Anthropic is a non-starter. Data leakage is the #1 concern.
GPU Scarcity & Cost
Cloud GPUs are expensive and hard to reserve. Training costs are skyrocketing.
Complexity Cliff
Fine-tuning requires deep ML expertise that most teams lack.
The Solution
Prompt-to-production workflow for local AI
One Command
pip install langtrain-aiSetup in under 5 minutes. No dependency hell. Pre-configured environments.
100% Private
Data never leaves your GPU
Cost Efficient
Use commodity hardware
How It Works
From data to deployed model in 3 simple steps
Prepare Dataset
Import CSV/JSON or connect DB. Visual editor for cleaning and formatting.
Fine-Tune Locally
One-click training with SFT/LoRA. Real-time metrics and auto-tuning.
Export & Deploy
Export GGUF/ONNX to use anywhere. Push to HuggingFace or S3.
Technical Moat
Deep optimization that competitors can't easily replicate
Optimized Local Training
Custom CUDA kernels for 2x faster LoRA on consumer GPUs
Zero Data Leakage
No network calls during training. Verified by security audits
Memory Efficient
Fine-tune 7B models on 8GB VRAM with gradient checkpointing
Universal Model Support
Llama, Mistral, Phi, Gemma, Qwen – all major architectures
Smart Data Pipeline
Auto tokenization, augmentation, and quality scoring
Continuous Improvement
Weekly updates with latest research (DoRA, FSDP2)
Langtrain Studio
Desktop app for macOS, Windows & Linux
Traction
Strong organic growth driven by community
Market Opportunity
The Shift to Local & Private AI
$50B
AI/ML Platform Market
$15B
Fine-tuning & MLOps
$500M
Privacy-first (Year 3)
35%
CAGR through 2028
Explosive growth in edge AI computing
Business Model
Open core with enterprise-grade features
Open Source
Langtrain Pro
Enterprise
Unit Economics
Strong margins with path to profitability
$50
Community-led growth keeps acquisition low
$480
24-month avg subscription lifetime
9.6x
Well above 3x benchmark
70%
Gross Margin
Primarily software-based, minimal infrastructure costs
Revenue Projections
Conservative growth based on current trajectory
2024
$120K
500 users
2025
$800K
3K users
2026
$3M
12K users
2027
$10M
40K users
Target ARR by Series A
$1.5M
Current Runway
18 Months
Growth Curve
Go-To-Market Strategy
Community-led growth with enterprise expansion
Build Community
Convert to Pro
Enterprise Sales
Product Roadmap
Building the AI Workbench — from local-first to enterprise-ready
Foundation
- Studio v1.0 Launch
- macOS + Windows
- SFT & LoRA Training
Scale
- Cloud Sync
- Model Registry
- Team Workspaces
Ecosystem
- Plugin Marketplace
- API Access
- Multi-GPU
Platform
- Enterprise SSO
- Managed Cloud
- Partner SDK
Why We Win
The only developer-first, local-first fine-tuning platform
The Landscape
What we understand
Teams don't want another cloud service. They want reproducible, fast, cost-efficient fine-tuning on their own GPUs.
The Team
Technical execution meets business strategy
Pritesh Raj
Founder & CEO
Built Langtrain end-to-end. Former applied research at DRDO. Kaggle Expert. Experience fine-tuning LLMs on large domain datasets.
Anjali
Co-Founder
8+ years in enterprise software. Lead Business Analyst at ArcelorMittal. Leads product strategy, pricing, and customer discovery.
The Ask
Raising Seed to accelerate growth
Pre-Seed Round
40%
Product
Studio features & infra
35%
Growth
Community & marketing
25%
Operations
Team & legal
Let's Talk
Building the future of AI fine-tuning
langtrain.xyz
pritesh@langtrain.xyz