Expanse

Product & Competitive Intelligence

Finds wasted GPU and HPC capacity before jobs burn money.

Company Overview

Expanse is a compute intelligence layer that predicts GPU and HPC job needs, cuts cluster waste, and diagnoses failed runs. Serving platform teams in AI infra, quant finance, drug discovery, and research labs; named customers are not public.

Latest Intel

Zeitgeist tracks private signals to determine where the company is heading strategically.

May 14, 2026

Technical Approach Signal

Confidence:

Medium

New Intel: Expanse is turning cluster telemetry into pre-run resource prediction and failure diagnosis. That opens the GPU waste budget and pressures Run:ai, CAST AI, and homegrown platform tooling.

View All The Latest Signals

What They're Building

The company's public product roadmap & what they're committed to building.

Passive Telemetry

Cluster agents capture job requests, runtime metrics, logs, and outcomes so resource advice is based on actual workloads, not vibes.

Resource Analysis

The analyse command predicts memory, runtime, GPU use, failure risk, and better job configs before a run starts.

Failure Diagnosis

The diagnose command turns failed jobs into root-cause notes with code and config changes a platform team can act on.

Waste Scanner

The open scanner checks SLURM and Kubernetes clusters for CPU, memory, and GPU waste, then produces a shareable report.

Scheduler Integrations

Expanse supports SLURM, Kubernetes, Databricks, YARN, cloud batch systems, Nomad, Ray, Flyte, and related workload stacks.

Founder and Key Execs

Ismaeel Bashir

Co-Founder & CEO (training workloads at quant funds and national supercomputing centers, EPCC resource prediction work)

Nikodem Bieniek

Co-Founder & CTO (hedge fund infrastructure and GPU cluster speech ML experience)

Yafet Melake

Co-Founder & COO (quant infrastructure tooling and graph neural network work for SLURM queue prediction)

Eren Mendi

Co-Founder & CPO (quant finance prototyping and ML systems work on decentralized foundation model training)

Founder Force Multiplier

The team has the rare combo this market needs: HPC scars, quant infra taste, and ML systems depth. That should help them sell to buyers who distrust generic AI wrappers and care about scheduler reality.

Funding History

2025 | Founded
2026 | Joined Y Combinator Spring 2026 batch

Competitors

NVIDIA Run:ai:

GPU orchestration platform with NVIDIA distribution; competes on cluster utilization, but is closer to scheduler control than neutral workload intelligence.

CAST AI:

Cloud and Kubernetes cost optimizer; stronger in cloud spend automation, less focused on HPC job prediction and failure diagnosis.

HPE Determined AI:

ML training platform with resource management; broader experiment workflow, weaker fit as a scheduler-agnostic intelligence layer.</p>

Expanse

's Moat:

The path is proprietary workload telemetry and workflow switching costs once predictions are trusted inside SLURM and Kubernetes queues.

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