Overshoot GTM brain

Overshoot builds custom inference deployments that reduce GPU usage and latency for AI teams running models on their own hardware.

category

AI inference optimization

market profile

  • text: Production AI teams must balance model quality, GPU cost, throughput and latency for their actual workload.
  • text: General-purpose inference engines leave opportunities for workload-specific profiling, tuning and custom kernels.

personas

  • title: Head of inference infrastructure
    pains:
    • Off-the-shelf engine settings underutilize expensive GPUs
    • Latency service levels constrain batching and throughput
  • title: ML platform or GPU performance engineer
    pains:
    • Model and hardware combinations lack optimized kernel paths
    • Profiling CPU and GPU bottlenecks takes specialized engineering time
  • title: CTO of an AI product company
    pains:
    • Rising inference spend erodes product margins
    • Cannot accept lower quality or slower responses just to cut cost

icp

  • text: AI product companies and model-serving teams running substantial production workloads on their own GPUs.
  • text: Engineering organizations serving open models with vLLM or SGLang and measurable latency and throughput targets.

message pillars

  • text: Tune to the customer's workload and hardware.
  • text: Reduce GPU consumption while respecting latency requirements.
  • text: Tie payment to an agreed performance result.

objections

  • objection: Optimization may degrade output quality or latency.
    response: Agree on quality limits and latency targets before benchmarking the customized deployment.
  • objection: We can tune vLLM ourselves.
    response: Compare a workload-specific profile and kernel-level opportunity against the team's current optimized baseline.
  • objection: Paying for an unproven engagement adds risk.
    response: Clarify the agreed performance target and outcome-dependent payment terms before work begins.

competitors

  • name: Luminal
    website: https://www.ycombinator.com/companies/luminal/jobs/J87U2SS-cloud-inference-engineer
    why: Offers an AI compiler and serving stack designed to accelerate inference on hardware.
  • name: Inferact
    website: https://developers.googleblog.com/announcing-new-vllm-and-sglang-inference-engines-on-tpu/
    why: Works on high-performance inference engine infrastructure and optimization.

influencers

  • name: Zakaria El hjouji
    url: https://nitter.rdrama.net/zakariaornot
    why: CEO publicly discusses forward-deployed inference optimization and benchmarks.

keywords

  • LLM inference optimization
  • reduce GPU inference cost
  • vLLM performance tuning
  • SGLang throughput optimization
  • custom CUDA inference kernels
  • GPU latency optimization
  • inference cost per token

voice

  • Technical and benchmark driven
  • Direct
  • Performance focused

signals

  • text: A company runs expensive open-model inference on its own GPU fleet and has strict latency targets.
  • text: An AI team is hiring GPU kernel or inference performance engineers to improve serving economics.

stack

  • vLLM
  • SGLang
  • FlashAttention
  • NVIDIA B200
  • RTX PRO 6000

pricing

model: unknown

motion

sales_led

channels

  • technical blog
  • X
  • LinkedIn
  • YC directory
  • founder outreach

confidence

medium

Founders

  • name: Zakaria El hjouji
    title: Founder/CEO
    role: CEO
    bio: Co-founder and CEO of Overshoot. Prior to this, Zakaria spent 7 years building pricing algorithms at Uber and writing GPU kernels at Meta AI. Won 3 prominent AI hackathons. Built and sold a software product while in grad school at MIT. He graduated top of his class at the London School of Economics. Likes to nerd out on anything inference and VLM / video understanding.
  • name: Younes El hjouji
    title: Founder/CTO
    role: CTO
    bio: Ex-Intel Computer Vision AI Frameworks Engineer. CTO of Overshoot. Building the platform for realtime vision powered by LLMs.

Back to YC companies