ScienceToStartup
TrendsTopicsSavedArticlesChangelogCareersAbout

113 Cherry St #92768

Seattle, WA 98104-2205

Backed by Research Labs
All systems operational

Product

  • Dashboard
  • Workspace
  • Build Loop
  • Research Map
  • Trends
  • Topics
  • Articles

Enterprise

  • TTO Dashboard
  • Scout Reports
  • RFP Marketplace
  • API

Resources

  • All Resources
  • Benchmark
  • Database
  • Dataset
  • Calculator
  • Glossary
  • State Reports
  • Industry Index
  • Directory
  • Templates
  • Alternatives
  • Changelog
  • FAQ
  • Docs

Company

  • About
  • Careers
  • For Media
  • Privacy Policy
  • Legal
  • Contact

Community

  • Open Source
  • Community
ScienceToStartup

Copyright © 2026 ScienceToStartup. All rights reserved.

Privacy Policy|Legal
  1. Home
  2. Signal Canvas
  3. Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optim
← Back to Paper

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT

Fresh1d ago
Export BriefOpen in Build LoopConnect with Author
View PDF ↗
Viability
0.0/10

Compared to this week’s papers

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 12

References: 32

Proof: fail

Distribution: unknown

Source paper: Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT

PDF: https://arxiv.org/pdf/2601.20408v1

First buyer signal: unknown

Distribution channel: unknown

Last proof check: 2026-03-19T21:31:49.672812+00:00

Starting…

Dimensions overall score 9.0

GitHub Code Pulse

No public code linked for this paper yet.

Key claims

Strong 12Mixed 0Weak 0

Founder DNA

Nicholas Santavas
eBay
Papers 1
Founder signal: 0/100
Research
Kareem Eissa
eBay
Papers 1
Founder signal: 0/100
Research
Patrycja Cieplicka
eBay
Papers 1
Founder signal: 0/100
Research
Piotr Florek
eBay
Papers 1
Founder signal: 0/100
Research
Matteo Nulli
eBay
Papers 1
Founder signal: 0/100
Research
Stefan Vasilev
eBay
Papers 1
Founder signal: 0/100
Research
Seyyed Hadi Hashemi
eBay
Papers 1
Founder signal: 0/100
Research
Antonios Gasteratos
Democritus University of Thrace
Papers 1
Founder signal: 0/100
Research

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Keep exploring

Builds On This
LLM for Large-Scale Optimization Model Auto-Formulation: A Lightweight Few-Shot Learning Approach
Score 8.0down
Builds On This
Data Driven Optimization of GPU efficiency for Distributed LLM Adapter Serving
Score 6.0down
Builds On This
Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts
Score 8.0down
Builds On This
LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study
Score 8.0down
Builds On This
An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU
Score 7.0down
Builds On This
LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning
Score 8.0down
Builds On This
KernelFoundry: Hardware-aware evolutionary GPU kernel optimization
Score 7.0down
Competing Approach
Execution-Verified Reinforcement Learning for Optimization Modeling
Score 7.0down

Startup potential card

Startup potential card preview
Share on XLinkedIn

Related Resources

  • How can LLM optimization be used to improve the efficiency of LLM fine-tuning?(question)
  • How do frameworks like OptiKIT democratize LLM optimization for non-expert teams?(question)
  • How can LLM optimization techniques contribute to more sustainable AI practices by reducing energy consumption?(question)

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Recommended Stack

PyTorchML Framework
FastAPIBackend
TensorFlowML Framework
JAXML Framework
KerasML Framework

Startup Essentials

Antigravity

AI Agent IDE

Render

Deploy Backend

Railway

Full-Stack Deploy

Supabase

Backend & Auth

Vercel

Deploy Frontend

Firebase

Google Backend

Hugging Face Hub

ML Model Hub

Banana.dev

GPU Inference

MVP Investment

$10K - $14K
6-10 weeks
Engineering
$8,000
GPU Compute
$800
LLM API Credits
$500
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

0.5-1x

3yr ROI

6-15x

GPU-heavy products have higher costs but premium pricing. Expect break-even by 12mo, then 40%+ margins at scale.

Talent Scout

N

Nicholas Santavas

eBay

K

Kareem Eissa

eBay

P

Patrycja Cieplicka

eBay

P

Piotr Florek

eBay

Find Similar Experts

LLM experts on LinkedIn & GitHub