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Instromeda

Instromeda provides self‑driving surface plasmon resonance (SPR) instruments that embed protocol design, real‑time optimization, and data analysis directly in the hardware, allowing labs to obtain gold‑standard binding measurements without SPR expertise. The modular Neo platform can be used as a standalone device or networked into high‑throughput arrays and is controllable via REST API, Python SDK, SiLA2, Slack or natural‑language commands, integrating seamlessly into existing automation workflows.

Updated 2 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Many research laboratories lack access to high-quality surface plasmon resonance (SPR) instrumentation because traditional systems are expensive, require specialized expertise to design protocols, and involve complex data interpretation, limiting the ability to perform reliable binding experiments at scale.

Solution

Instromeda delivers self‑driving analytical devices that embed domain expertise directly in the hardware. Users specify the desired measurement through a REST API, Python SDK, or natural‑language interface, and the instrument automatically designs the SPR protocol, optimizes experimental parameters in real time, runs the assay, and returns fully analyzed results. The modular Neo platform can operate as a single unit for low‑throughput labs or be networked into arrays for high‑throughput screening, providing consistent gold‑standard data at the cost of entry‑level label‑free instruments. Built‑in self‑repair and long service life further reduce operational overhead, making expert‑level SPR accessible to a broader range of scientific teams.

Target Audience

Primary customers are academic, biotech, and pharmaceutical laboratories that require reliable SPR or label‑free binding measurements but lack dedicated SPR specialists or large capital budgets.

Features

  • Onboard protocol design and real‑time optimization that eliminate the need for manual SPR expertise
  • Programmable endpoints accessible via REST API, Python SDK, SiLA2, MCP server, Slack, or natural‑language commands
  • Modular hardware that can function as a standalone unit or scale into networked arrays for high‑throughput workflows
  • Integrated data analysis pipeline delivering ready‑to‑use binding kinetics and affinity metrics
  • Self‑repair mechanisms and durable manufacturing to extend device lifespan and lower total cost of ownership
  • Compatibility with existing laboratory automation stacks, enabling seamless integration into decision‑making workflows
This profile is AI-generated and may contain inaccuracies.