In-browser centroiding & value extraction for scientific data

Executive Summary

Many research and industrial labs — an addressable market of roughly 80,000 sites equating to a $2.4B market at a $30K average contract value for data-analysis tooling and SDKs — regularly process instrument outputs (microscopy, mass spectrometry, diffraction, single-molecule imaging) that require fast, accurate centroiding and value extraction. These teams today rely on heavyweight desktop tools or cloud-side pipelines that introduce latency, recurring costs, and IP/regulatory risk, and that fragment workflows between instrument, viewer, and ELN/LIMS. A practical product would be an embeddable in-browser SDK that runs centroiding and downstream feature extraction client-side using WebAssembly and WebGPU, delivering sub-second interactive responsiveness (e.g., 100–300 ms for typical 2k×2k regions on modern laptops) with graceful fallbacks for non‑GPU environments. Bundle pre-validated algorithms (Gaussian, wavelet, maximum‑likelihood variants), streaming/worker pipelines, and out-of-the-box adapters for ELNs/LIMS so integrators can drop a component into web viewers; commercially price around existing tooling benchmarks ($10–50K ACV for integrations, with a developer tier) to align with the $30K market expectation. The timing is favorable because WebAssembly/WebGPU are finally enabling near-native browser compute while ELN and web-native lab platforms increase demand for embeddable components, and privacy/data-locality pressures push compute to the client. Competition is medium: mature C++/Python libraries and cloud GPU services already exist, so the principal challenges are proving numeric parity and reproducibility, handling cross-browser GPU variability, and building trust with validation datasets; the strongest defensible plays are validated accuracy, low-integration friction, and partnerships with a small set of ELN/LIMS vendors to secure a visible beachhead.