Building the Model-Improvement Layer for Open-Weight AI Systems
We turn open-weight generalist AI models into production-ready systems that anyone can evaluate, improve, and deploy — no ML team required
🚀 an Escape Velocity Labs project
Presented by:
Problem
Fine-tuning open models is slow, inaccessible, and "doesn't work"
Despite $30–40 billion in enterprise investment in generative artificial intelligence, AI pilot failure is officially the norm — 95% of corporate AI initiatives show zero return - McKinsey State of AI Report 2025
Source: industry averages, internal benchmarking, 2025.
Solution
From costly ML projects to self-serve model improvement
Depura reduces the time/cost of adapting open models by 95–99%, and lets any team reach production-grade accuracy without ML ops expertise.
Depura reduces fine-tuning time by up to 99.5% and cost by 95%.
Teams reach production-grade accuracy in hours, not weeks — with no ML ops required.
Internal benchmarks; based on typical consultancy and in-house fine-tuning costs, 2025.
How It Works
Create Evaluations
Develop human-written evaluation templates tailored to your domain requirements, assisted by our platform.
Run Models
Execute open models (starting with OpenAI's GPT OSS 20B)
Analyze Errors
Human-in-the-loop evaluation without heavy MLOps infrastructure
Post-Train
Depura auto-applies LoRA/RL techniques to fine-tune your base model
Re-Evaluate
Depura allows you to understand the performance of fine-tuned models before you decide if they need further work or are ready for production.
Deploy
Launch production-ready models via web and API (can be hosted on Depura platform or downloaded for personal or commercial use)
Impact Metrics (vs traditional fine-tuning): Time ↓99.5% Cost ↓95% Accuracy ↑90–99%
Market Opportunity
Market Size in 2025
Global MLOps/LLMOps market
Our Wedge in 2025
Evals + Fine-tuning for Open-Source Models
Annual Growth Rate
CAGR driving market expansion until mid-2030s
We target the $0.3–0.5B post-training segment inside the $3B MLOps market.
Capturing <1 % of this market reaches $20M ARR.
Depura's focus is non-technical users requiring specialised AI in their workflows.
Footnote: TAM: MLOps $2.3–$3.0B (2025) — FBI/GVR. LLMOps: $1.2–$1.3B (2024) — MarketIntelo/DataIntelo. EVL wedge (eval + post-training) modeled as 25–40% of LLMOps budgets. Enterprise AI spend: IDC $307B (2025); GenAI $69B (2025). Adoption: McKinsey 78% use AI in ≥1 function (2025). Pent-up demand: MIT/press coverage shows most GenAI projects don't yet impact P&L.(Thesis: lack of evaluation, fine-tuning, big reason).
Competitive Landscape
Depura = evaluation-first model-improvement platform anyone can use.
Footnote: Category definition: “Model-Improvement Platforms” — complete evaluate → post-train (LoRA/RL) → re-evaluate → deploy loop, accessible to non-ML teams.
Competitive Advantage
Our edge: evaluation + accessibility → >100× faster, 20× cheaper.
Business Model
B2B, B2D Credits-Based Pricing
Customers pay in credits tied to model evaluation and post-training tasks, with optional team and deployment plans.
This creates predictable recurring revenue as teams scale usage from prototypes to production.
Credits-Based Compute
usage-based recurring revenue (20–30% compute markup + platform usage fee)
Subscription Tiers
predictable ARR
Enterprise Collaboration
private deployments (annual contracts).
Marketplace (2026)
future network effect.
Target Sectors: AI developers, professional services, and enterprise teams building domain-specific AI applications.
Usage-based recurring revenue with strong gross margins (40–60%).
Financial Projections
Unit-based plan to $20M: enterprise + mid-market + teams; margins improve with reserved compute and model optimization
Year 1
$1.5M revenue, establishing market presence, becoming profitable
Year 2
$8M revenue, growing at scale, introducing marketplace
Year 3
$20M revenue, accelerated sales motion, established within category
Footnote: Growth: +433% YoY (Y1→Y2), +150% YoY (Y2→Y3); 2-yr CAGR ≈ 265% (blended 11.4% MoM).
Implied wedge share (≈$0.3–0.5B, 2025): Y1 ~0.3–0.5% • Y2 ~1.6–2.7% • Y3 ~4.0–6.7%.
Roadmap
From intuitive evaluation loops to reinforcement-learning environments.
MVP
Prove the loop
Expansion
Broaden models
Automation
AI-assisted setup
Learning Systems
AI trains AI
Management Team
Manny E. Reimi
CEO
15+ years in product and AI/DeFi startups (GRVT, DeOrderBook, Pickle). 3x founding product specialist bridging user-centric design, product engineering, and growth.
Paul V. Reed
CTO
20+ years in high-performance, low-latency backend and AI systems engineering. Former lead developer in AI and Web3 projects (GRVT, Thomson Reuters).
Renée A. Reimi
Chief Product & Marketing Officer
Former agency principal. Marketing and operations leader with AI startup experience (Oh My Ink, C&R Wise AI). Focus on community, GTM, and partnerships.
The founding team brings deep expertise in AI infrastructure, product development, and go-to-market strategy, with proven track records in scaling technology startups.
Investment Opportunity
Fundraising Target
$250,000 USD
Target closing: March 2026
Valuation
$3.3M pre-money
≈ 7% equity for investors
Join us in building the model-improvement layer for open-weight AI systems!
Why Invest Now:
- 35–40% CAGR market, $3B+ in 2025.
- Depura reduces fine-tuning cost/time by 95–99%.
- Path to $20M ARR and leadership in niche category.
- 10× potential return on $250K investment → $2.5M stake at Series A.
- Experienced technical, product, and marketing team, already building first pilots.