A2K is a purpose-built agentic AI platform delivering data harmonization as infrastructure.
A2K translates any data model, ontology, or standard into an AI-optimized schema and set of specifications.
The agentic platform uses those specifications to map and harmonize heterogeneous data sources.
It returns harmonized data ready to consume, with a full audit and provenance trail.
A2K takes the data models, ontologies, and controlled terminologies you have already invested in and turns them into an executable harmonization capability, without your teams building or maintaining an AI pipeline.
A2K applies your data model the same way across every source, study, and dataset, so adoption produces comparable, analysis-ready data instead of local variants and drift.
Deploy A2K in-boundary so source data stays inside your compliance perimeter. The engine processes and returns output, retains no data between runs, and never trains on yours.
Extensible without engine changes. New data models and data types are added as specification packs; the pipeline code is unchanged, enforced in CI.
Scalable to hundreds of datasets. A stateless, containerized engine runs pipelines in parallel; workers scale to volume, then release.
Efficient model routing. Values escalate by difficulty from a light model to a frontier model, so spend follows the hard cases.
Reproducible output. Governed by versioned specification packs and deterministic equivalence gates.
Per-cell provenance. Confidence, decision type, and a full audit trail on every value.
No client data retained. The engine processes and returns output; it holds no state between runs.
Runs in your compliance perimeter. Deploy A2K inside your own cloud account, with encryption in transit and at rest. In-boundary inference runs against a managed model endpoint in your environment, for example AWS Bedrock or an equivalent managed model service on another cloud.
Model-provider abstraction. One interface runs the models, and it never trains on your data.
Governed engineering. Spec-bound development, independent design review, and CI-gated regression.
License the harmonization runtime, the specification-engineering toolchain, and the evaluation harness that verifies engine behavior. Embed A2K as your own capability: as a module, container, or API service, under your brand, with data staying in your or your customer's environment.
A2K works with consortia, academic groups, and research organizations on harmonization against community standards and ontologies. License it for your own use or to provide to the members and institutions you serve: run it as a service, via API, or deployed into your community's environment, with the standard and the relationship with your community staying yours.
A2K uses large language models as components inside a governed pipeline. The models work against explicit, versioned specifications, outputs pass deterministic equivalence gates, and every value carries provenance and confidence. For a given specification pack and model version, results are reproducible run to run, which distinguishes A2K from free-form generative tools.
A2K is model-agnostic behind a provider-abstraction layer. Values are routed by difficulty from a light model to a frontier model, and in-boundary deployments run models through a managed endpoint in your own cloud account, for example AWS Bedrock or an equivalent managed model service on another cloud. No model in the pipeline trains on your data.
Yes. A2K deploys embedded in your platform, in-boundary inside your compliance perimeter, or as an API service. In-boundary deployments keep source data in your environment.
The engine processes data and returns output; it retains no state between runs. Deployments run with encryption in transit and at rest, and your data is never used to train models.
In containerized deployments the LLM never receives your full dataset. Harmonization prompts are variable-level: for a coded variable, the engine reduces the column to its distinct values and sends each distinct value against the target codelist, then applies that decision to every matching row deterministically. No per-subject row is assembled into a prompt. Deterministic variables, including identifiers, dates, arms, age, and country, send nothing to the LLM at all. Free-text and verbatim fields send distinct, deduplicated values under your de-identification attestation. With in-boundary inference, model calls run inside your own cloud account and nothing crosses your boundary.
Scale has two axes. For volume within a client, the pipeline runs one dataset at a time and holds no state between runs, and the specification pack is a fixed, read-only artifact shared by every worker, so two runs cannot interfere. Processing hundreds of studies is a matter of running many copies of the same pipeline in parallel: throughput grows close to linearly with worker count, and the wall clock for a large batch approaches the duration of the single longest run as workers are added. The practical ceiling is model-provider throughput rather than the architecture, and that ceiling is a provisioning decision.
For breadth across data types, the platform core is never edited to add a data type. Once a specification pack for a data type exists, that type runs through the identical pipeline with no new engine work; adding the hundredth data type is the same kind of task as adding the third. Breadth scales at the speed specifications can be authored and validated.
A2K (Analysis2Knowledge) builds harmonization infrastructure for life sciences. The platform was developed in collaboration with the Clinical Research Data Sharing Alliance (CRDSA), a multi-stakeholder consortium whose standards and tools have been downloaded more than 27,000 times across 90+ countries.
Aaron Mann, founder and Chief Executive Officer, is co-founder and CEO of CRDSA and has led data-sharing and harmonization work across the life-sciences ecosystem.