MaxisAI Holarchy: Governed Context Layer for Every AI Decision.
MaxisAI Holarchy, Maxis AI’s Verticalized Context Layer, sits between every AI agent and every model. It grounds each call in the standards your industry runs on and the rules your regulator enforces, masks protected data before the model sees it, holds every output to your quality bar, and seals the whole path into an inspection-ready record. One configuration change. No agent rewrite.
A confident answer is not a provable one.
In a regulated industry, the model can sound right and still leave you exposed. Three gaps are the reason.
The model doesn't know your study.
It answers from its training data, not from this protocol, this SOP version, or this regulator.
“It looked right” is not evidence.
An inspection asks how the output was generated, who approved it, on what basis, and when. A chat log does not answer that.
Ungoverned agents are expensive.
They re-answer questions your reviewers already settled, resend context nobody reads, and spend against no budget.
Five sources, assembled per call, in parallel.
Before any model responds, the Context Layer bundles what your work actually runs on into one versioned, promotable object. Every decision traces back to the clause behind it.
Industry standards.
The codified standards of your industry, versioned, with the regulator's own text behind each rule.
Your own corpus.
Protocols, SOPs, mapping specs, and prior submissions. Private to your tenant, never pooled.
Knowledge graph.
Requirements, concepts, and their edges, so every rule traces back to the clause behind it.
Past decisions.
What your reviewers approved, corrected, and rejected before, carried into the next answer.
Live study state.
Site, subject, and system events from the enterprise stack. Current, not last quarter's extract.
One Context Bundle.
Knowledge scope, rules, quality policy, and review policy, moved dev to staging to production like code.
Every request, every agent, governed automatically.
The same seven steps run on every call, before and after the model, with no change to your agents.
- 1
Identify.
Resolves the study, the agent, the quality policy, and the regulatory regime in force.
- 2
Reuse.
A question your reviewers already settled is answered from that decision, at zero model cost.
- 3
Ground.
Standards, your SOPs, the graph, past decisions, and live study state become one bundle.
- 4
Mask and call.
Protected data is redacted before the call, then routed to a model you have approved.
- 5
Judge.
Output is scored against your quality bar and the regulator's rules before anyone sees it.
- 6
Escalate.
What falls short routes to a qualified reviewer, on a clock you set per study.
- 7
Seal and learn.
Signed trace, immutable record, cost attribution. The correction becomes tomorrow’s answer.
Governance is not added afterwards. It is where the product begins.
Other approaches wrap an AI in a thin layer of prompts. MaxisAI Holarchy builds industry knowledge and compliance into the fabric itself, so the standards of your industry, the regulator's own text, and your private corpus sit behind every rule.
Industry knowledge, embedded.
Domain knowledge, therapeutic knowledge, processes, standards, policies, and compliance are built in, not bolted on.
Audit-ready by construction.
Each record is append-only and hash-chained, tagged to the regulator, standard, and geography in force at the moment of the decision. The explanation is the record.
The same agent, with and without the Context Layer.
In internal benchmarking, one agent was run twice, once ungoverned and once through the Context Layer, and both were traced and scored identically. Only the grounding differed.
Ungoverned baseline.
Every run failed a blocking check, with roughly five issues per run.
64 blocking failures avoided.
across the benchmark window with the Context Layer in place.
96 issues avoided.
fewer errors reaching a reviewer or an inspection.
Cost and tokens down.
materially lower spend and token use per run.
Quality and confidence up.
above baseline, and the advantage widens run over run as the institutional memory compounds.
Early internal benchmarking. Final figures confirmed by product before publish.
Yesterday's correction is today's answer.
When a reviewer corrects an output, the correction is captured as a decision, attributed to a named approver, and carried into the next answer, so the same class of error stops recurring. Quality rises and the cost curve bends over time. Nothing is fine-tuned, and nothing is opaque.
Three ways to run it. Your data, your rules.
Shared.
Maxis AI hosts the fabric and the data. The fastest path to a governed proof of value.
External.
Maxis AI runs the services. Your databases hold the data, and it never leaves your estate.
Private.
Everything inside your datacenter. Standards arrive as signed packages, only usage counts leave.
One governed foundation, across the industry.
CROs and site networks.
Give your agents consistent, governed context across sponsors and studies.
Large pharma and biotech.
Apply it across therapeutic areas and functions, under one regime of governance.
Smaller teams.
Work above your headcount without losing control.
Technology vendors.
Connect your own agents and applications to it, extending governed context beyond any single tool.
One governed layer, many domains.
MaxisAI Holarchy is the Verticalized Context Layer inside the Agentic AI platform, the governed institutional memory for regulated work. Within it, work is organized into domain-specific intelligences, called MaxisAI Holons, one for each area of clinical execution. Each Holon holds its own knowledge, standards, and decision boundaries, and acts on its own work but never unilaterally, answering to a single regime of governance, evidence, and human oversight.
- Monitoring
- Safety
- Data Management
- Site Operations
- Supply
- Statistics
All you need to know.
MaxisAI Holarchy, Maxis AI’s Verticalized Context Layer, is a governance fabric that sits between your AI agents and the models they call. It grounds each request in your standards and your regulator’s rules, masks protected data, holds every output to your quality bar, routes what falls short to a human reviewer, and seals the whole path into an inspection-ready record.
No. Existing agents come under governance by pointing at a new endpoint. One configuration change, no rewrite, no SDK migration.
The domain-specific parts within the layer, one for each area of clinical execution, such as monitoring, safety, data management, and statistics. Each owns its knowledge and decision boundaries and works under a single regime of governance.
It ships governance for FDA, EMA, MHRA, PMDA, CDSCO, TGA, and more, across clinical and life sciences and other regulated industries. Register a regulator it does not yet ship, and it governs from that day.
Three options: shared, where Maxis AI hosts the fabric and the data; external, where Maxis AI runs the services and your databases hold the data; and private, entirely inside your own datacenter.
What is the Context Layer? MaxisAI Holarchy, Maxis AI’s Verticalized Context Layer, is a governance fabric that sits between your AI agents and the models they call. It grounds each request in your standards and your regulator’s rules, masks protected data, holds every output to your quality bar, routes what falls short to a human reviewer, and seals the whole path into an inspection-ready record.
Do we have to rewrite our agents? No. Existing agents come under governance by pointing at a new endpoint. One configuration change, no rewrite, no SDK migration.
What are MaxisAI Holons? The domain-specific parts within the layer, one for each area of clinical execution, such as monitoring, safety, data management, and statistics. Each owns its knowledge and decision boundaries and works under a single regime of governance.
Which regulators and industries does it cover? It ships governance for FDA, EMA, MHRA, PMDA, CDSCO, TGA, and more, across clinical and life sciences and other regulated industries. Register a regulator it does not yet ship, and it governs from that day.
Where does our data live? Three options: shared, where Maxis AI hosts the fabric and the data; external, where Maxis AI runs the services and your databases hold the data; and private, entirely inside your own datacenter.
Make what your AI does provable.
See MaxisAI Holarchy grounded in your standards, your regulator’s rules, your own study state, and your connected clinical systems.