LSM is designed for defined analytical decisions, not broad, open-ended generalization. It learns the structure of your instrument data and the confidence boundaries around your scientists’ decisions. Your scientists benchmark it against their reviewed work, resolve inconsistencies, and approve each model version before production. Data outside its validated boundaries goes to an expert rather than being forced into an answer.

Implementation starts with one workflow and the reviewed examples, SOPs, acceptance criteria, and systems that support it. The model is trained and benchmarked against your experts, then integrated into your environment. Validation, security review, and change control are completed before production.

EI is vendor-agnostic and connects to the data sources and systems your lab already uses, from controlled file exchange to direct integration with instruments, vendor software, LIMS, and enterprise systems. It can run in an approved cloud, private cloud, on-premises, or hybrid environment. Data residency, access, and processing are configured around your requirements, and customer data and models are never mixed across organizations.

EI provides the technical controls and documentation needed to support regulated and accredited laboratories, including audit trails, electronic records and signatures, access controls, model versioning, validation documentation, and controlled change. Compliance and accreditation apply to the laboratory’s validated use, procedures, and quality system, not to software alone.

LSM is EI’s foundational model, built for laboratory instrument data and defined analytical decisions. It is deterministic, learns from your scientists’ reviewed work, and routes anything outside its confidence boundaries to an expert. Large language models generate language probabilistically and do not make EI’s analytical decisions. Raw data remains unchanged, and generative tools used to explain information do not create or alter the analytical result.

Routine review moves from manual queues to governed automation and review by exception. Scientists become the creators and governors of the models that scale their judgment, while leadership gains a consistent standard across workflows, labs, and external partners.

EI Flow models can be deployed and validated wherever the method runs, allowing the same scientific standard to operate across internal labs, sponsors, CROs, and CDMOs. Scientists can review exceptions and refine models remotely, while access, data residency, model versions, and audit trails remain governed. This supports consistent review and method transfer in either direction.

We establish a baseline for manual review, rework, failed runs, delayed decisions, and expertise that cannot scale. The initial deployment measures changes in cycle time, throughput, consistency, and avoided work. Those results provide the business case for bringing EI to additional methods and labs.

Pricing reflects the workflows being automated, their complexity and processing volume, integration and deployment requirements, and the scale of the rollout. The commercial approach is built around a defined starting scope and the value expected from it, then adapted as deployment expands.

EI Flow creates governed decisions at the point of analysis, and EI Signal connects them across the organization. MCP makes that intelligence available to authorized people, models, and systems, from in silico design to manufacturing and quality. When those actions produce new laboratory results, EI reads that governed evidence through the same controlled path, extending understanding while preserving the scientific record.