Problem

Infera addresses the integration gap inside life-science laboratories: labs operate sophisticated instruments from multiple vendors, but the surrounding software is fragmented. A typical lab may run roughly six instruments from three or more vendors, write a one-off script for each experiment, stitch outputs together manually, and track inventory in an unreliable spreadsheet. Scientists consequently spend valuable research time acting as the “human glue” between machines that should already communicate.

The economic pain is wasted skilled labor, slower experiment throughput, avoidable protocol and data-handling errors, and loss of institutional knowledge when the person who built a workflow leaves. Laboratory automation’s core value is freeing researchers’ time for higher-value work, and Infera’s killer use case is a multi-instrument workflow—particularly in a core facility, diagnostic lab, or production lab—where a scientist needs to move an experiment across different instruments and vendors without repeatedly rebuilding the workflow by hand.

Product / Service

Infera is an AI-native laboratory operating system and natural-language compiler. A researcher describes an experiment in plain English; Infera asks clarifying questions, surfaces edge cases, checks the laboratory’s inventory and instrument state, and converts the intent into a validated, instrument-ready run. For programmable equipment it generates vendor-specific scripts, executes the run, retrieves the resulting data, and performs analysis. For manual steps such as pipetting, gels, or hand fermentation, it provides the contextual layer that guides the researcher through reagents, sequencing, failure modes, and prior practice.

The delivery model is a hardware-agnostic software layer that works with instruments the lab already owns, or supports work performed by hand at the bench. It consolidates protocol logic, vendor scripts, data, inventory, and institutional knowledge; can upload data directly from instrument PCs; alerts users when validation fails; and tracks consumables and reordering. Every protocol and validated run becomes part of an auditable knowledge base, producing standardized SOPs and a traceable record from the original protocol through what happened on the bench.

Market

Infera competes in laboratory automation, lab orchestration, and the emerging category of AI-native laboratory operating systems. Its closest overlapping software alternatives include Synthace, which helps researchers design, run, and analyze automated experiments; Biosero’s Green Button Go, which orchestrates lab workflows; and Scispot’s AI-enabled Lab OS. Emerald Cloud Lab and Strateos are adjacent alternatives because they provide remotely controlled or cloud-lab execution, whereas Infera’s pitch is to coordinate the equipment already installed in a customer’s laboratory. Its differentiation is the natural-language interface and the attempt to sit across vendors rather than forcing labs to rebuild protocols inside one proprietary platform.

Infera is very early rather than a scaled commercial vendor. It was founded in 2026 by Chloe Sow and Troy Zhang, has two employees, and is a Spring 2026 Y Combinator company. The company says it is running pilots with Boston-area academic labs and core facilities and is seeking relationships with academic, diagnostic, biotech, and instrument-manufacturer users; its website identifies core facilities, diagnostic labs, and production labs as current target customers. Public materials do not establish meaningful revenue or named-customer traction: the available profile leaves current revenue blank, while PitchBook reports $500,000 raised.