Everything you need to know about Research Lambda — evidence-grounded clinical AI built for auditability, determinism, and regulated environments.
Research Lambda is a SaaS platform designed for sponsors and CROs that combines protocol intelligence, site matching, feasibility automation, a community marketplace, and automated study startup into a single integrated workflow. It transforms how clinical trials are designed, sourced, and activated from start to finish.
Our evidence-grounded extraction pipeline analyzes clinical trial protocols by ingesting PDF documents and automatically extracting 100+ structured variables including inclusion/exclusion criteria, site requirements, patient population metrics, and study parameters. The system requires no manual data entry and enriches extractions with data from ClinicalTrials.gov and PubMed.
Our AI matches protocol criteria against persistent site profiles that capture investigator-site relationships and capabilities. Sites are automatically ranked by patient population, clinical experience, and capacity. This approach eliminates manual site selection, reduces bias, and achieves better outcomes than traditional manual matching processes.
The platform auto-generates feasibility surveys tailored to each protocol and pre-populates responses directly from site profiles using intent recognition. This eliminates the need for manual survey creation and extensive back-and-forth communication with sites, reducing feasibility assessment effort by 80% or more compared to traditional methods.
Yes. Research Lambda deploys in your cloud environment and integrates seamlessly with existing CTMS (Clinical Trial Management System) tools and downstream operational systems. This approach protects your data, maintains compliance, and ensures the platform works within your existing technology stack.
The Research Lambda marketplace is a community where clinical research sites showcase their capabilities, patient populations, and experience. Sites compete for study placements based on fit and capacity. In our beta phase, we have engaged over 200 high-quality clinical research sites across the network.
In our live pilot deployment, we have processed 200GB+ of unstructured clinical data, ingested 2,567 documents, structured 3,900+ records, extracted 100+ variables per protocol, achieved presence across 38 US states and 3 international markets, and serve 4 active paying clients. Results include 15-20% faster study startup and significant cost reductions.
Our platform serves multiple customer segments including CROs (Contract Research Organizations), pharmaceutical sponsors, wellness brands exploring clinical validation, and home-care networks looking to participate in clinical trials. Any organization involved in clinical trial recruitment and site selection can benefit from our platform.
Research Lambda uses an enterprise SaaS model with three components: a platform license fee, professional services fees for implementation and customization, and per-study usage fees based on protocol complexity and study scope. Typical enterprise clients invest between $500K and $2M+ annually depending on scale and utilization.
Research Lambda supports rapid deployment timelines. In our live pilot, the system was operational and producing actionable dashboards within hours of initial setup. Most enterprise implementations are production-ready within weeks rather than months, significantly faster than traditional clinical systems.
Research Lambda is the only platform that delivers all five end-to-end capabilities: Protocol Intelligence, Site Matching, Feasibility Automation, Community Marketplace, and Study Startup integration. Competitors offer partial solutions addressing 1-2 areas. We provide a comprehensive, unified approach that eliminates manual handoffs and integration gaps.
Research Lambda uses evidence-grounded graph reasoning rather than open-ended text generation. Every output is anchored to retrieved source documents — the system cannot assert a claim that isn't traceable to an ingested record. A separate validation stage checks for contradictions across sources before any result is surfaced. This architecture structurally prevents the kind of confabulation common in general-purpose LLMs. For a full walkthrough of the pipeline, see How It Works.
Yes — auditability is a first-class design requirement, not an afterthought. Every synthesized output includes a citation trail mapping each claim to its source document, field, and extraction timestamp. Clinical operations teams and regulatory reviewers can inspect the full reasoning chain without needing to trust the model on faith. This is critical for GCP-compliant environments where decisions must be defensible. Learn more on the Architecture page.
Yes. For a given protocol and evidence corpus, Research Lambda produces the same structured output every time — regardless of who runs the query or when. Unlike probabilistic chat-style AI tools, our pipeline is designed for reproducibility: fixed retrieval logic, deterministic graph traversal, and evidence compaction that collapses redundant information rather than sampling from it. Two teams running the same feasibility assessment will reach the same answer. Details are covered in the reasoning architecture overview.