The platform that turns informal AI use into auditable AI operations — across NIST AI RMF, FDA CSA, SR 11-7, HIPAA and the EU AI Act. Use-case registry, risk evaluation, evidence trail and monitoring in one place.
High-risk AI systems must comply with the EU AI Act by August 2, 2026. The obligation reaches any organization doing business with European clients, regardless of where it is headquartered.
EU AI Act penalties for non-compliance reach €35M or 7% of global annual turnover. SR 11-7, HIPAA and FDA CSA carry their own examination, audit and inspection exposure on top.
NIST AI RMF, FDA CSA, SR 11-7, HIPAA and EU AI Act. They are not theoretical anchors — they are constraints that determine what an operator can produce in inspection, in court, in client review.
The capability is already in the building. What is missing is the layer that makes that use visible, attributable, auditable and defensible. Four problems show up in every function we walk into.
Copilot, Cursor, Claude and ChatGPT are inside the workflow whether or not IT approved them. Productivity accrues to individuals, risk accrues to the company. The CIO has no map.
Which prompt produced which output, which human reviewed it, which decision followed. Without that trail the work is not defendable before an inspector, an auditor or a regulated client.
NIST AI RMF, FDA CSA, SR 11-7, HIPAA, EU AI Act. Operating without instrumented governance carries unbounded legal exposure, even before anyone enforces it.
Prompts evolve, models update, engineers leave with their patterns, agents spawn sub-agents that wander from spec. What was governed in week one isn't governed in week thirty.
The organization either over-restricts AI and loses the gain, or runs unrestricted and carries the exposure alone. Kairós is the third option.
Kairós is not a wrapper around LLMs. It is the layer that sits across them — registering what happens, attaching ownership, and producing the evidence that makes AI-assisted work defensible.
Every AI use case is registered with purpose, scope, risk classification and ownership. The CIO finally has a map of what is happening with AI inside the function.
Every prompt, output, model version, retrieval source and human review logged with timestamp and identity. Decisions reconstructable end-to-end, weeks or years later.
Each use case carries its gates: what needs human review, dual sign-off, or can run agentically with monitoring only. Enforced in operation, not added in audit.
Inspection-ready evidence packages on demand: FDA submission support, HIPAA access logs, SR 11-7 model documentation, EU AI Act conformity files. A defendable artifact, not a dredged log.
When workflows spawn sub-agents, Kairós tracks the hierarchy and attaches ownership at each level. The piece that makes agent swarms operable in regulated sectors.
Detects when prompts evolve, models update, or behavior shifts away from spec. Surfaces drift before it becomes an incident. Continuous, not point-in-time.
The analyst stays in their IDE (Cursor) and GitHub. The companion rides alongside, as an extension, surfacing notices from two autonomous agents. Three layers, lightest first: a silent badge, a tab opened at will, and a dashboard per level.
By default, just two counters in the IDE status bar: “G 1 · K 2”. Nothing opens, nothing interrupts. Zero friction over daily work.
G for governance (mandatory) and K for knowledge (optional). K is autonomous: it spots the case and offers what's reusable without being asked. G interrupts only on hard controls; everything else is logged silently.
Personal for the analyst, group for the group lead, general for the board — the latter carrying the four metrics that test the thesis. The same Kairós substrate, aggregated by level.
Kairós does not interpret regulation; it operationalizes it. Each framework becomes specific gates, specific evidence requirements and specific audit-ready outputs.
Most IT functions are running AI on logging, policy decks and good intentions. Here is what changes when governance is instrumented instead of recommended.
Every AI use case registered with owner, scope and risk class.
Prompt, output, model, reviewer and decision reconstructable end-to-end.
Human review, dual sign-off or monitored-autonomy enforced in operation.
Inspection-ready packages per framework, produced on demand.
Ownership tracked through every sub-agent in the hierarchy.
Continuous monitoring of prompt, model and behavior change.
Provider-agnostic layer; switching models doesn't break governance.
Kairós sits between the AI tools your engineers use and the organizational layer that must govern them. It does not replace the tools, the engineers or your compliance function. It instruments what they do.
Sits across OpenAI, Anthropic, Google and open-source models. No lock-in; the governance layer stays stable when underlying tools change.
Engineers keep using Copilot, Cursor, ChatGPT and internal agents. Kairós captures use-case context, not keystrokes. Low friction, high coverage.
Critical for IT-services firms across many clients. No prompt, output or learned pattern crosses tenant boundaries. SOC 2 passes with evidence, not assurances.
Every use case has a human owner. Every decision carries identity at each level. Even when swarms spawn sub-agents, the chain stays traceable.
Detailed technical specifications and product documentation live at kairos.luquin.com.
In a PRAGMAGROUP installation, Kairós is not bolted on at the end as a compliance afterthought. It is present from Discovery and instrumented through every phase.
We map current AI use across the organization: highest-leverage workflows, governance gaps, applicable frameworks. Surfaces the use cases that become Kairós registered entries.
A six-month installation plan against Discovery priorities. Case selection, signed binary criteria, gates per case, instance scoped. Approved by your executive committee before kickoff.
Operational execution under signed criteria. Kairós installed against the Phase 2 gates; your team enters the operating practice from week one. By the end, the function operates under governance whether we stay or leave.
Multi-agent architectures — one human directing a hierarchy of agents that create sub-agents — are already in production at limited scale. The capability is here. What is missing in every deployment is the governance layer.
A sub-sub-agent drafting protocol language, flagging an adverse event or committing code to a banking client cannot be the accountable party. Without a granular trail, accountability becomes unenforceable.
HIPAA, FDA CSA, SR 11-7 and EU AI Act all expect a verifiable chain of evidence. A swarm without instrumented governance produces output the auditor cannot trace.
Sub-agents creating sub-agents over months wander from spec. A traditional company solves drift through culture and reviews; a swarm has none of that by default. It has to be engineered in.
The swarm future the market describes is real. What it leaves unsaid is that no version of it works in regulated sectors without something like Kairós underneath. That is the position we occupy.
Each vertical arrives with its use cases preconfigured, its framework mapping done and its evidence templates calibrated to that sector's regulatory reality. Same motor; the wrapper speaks the buyer's world.
For CROs, biotechs, regional health systems and pharma IT functions. Preconfigured for protocol drafting, submission review, adverse-event detection, clinical-trial documentation, GxP validation and patient-facing workflows.
For software houses, integrators and MSPs in the 50–500 range. Preconfigured for developer-productivity governance, code-review traceability, client-deliverable provenance and multi-tenant segregation.
For industrial IT functions, OT/IT boundary cases and manufacturing-adjacent IT services. Use cases under development around predictive maintenance, QC vision systems and ERP-AI integration.
Kairós does what the name says: it instruments AI governance. It does not pretend to do four things the market sometimes asks of it.
Kairós does not block prompts on content rules in real time. That is a different category. Kairós captures, attributes, governs, audits.
Provider-agnostic. It works above whichever models your engineers use. Switching models does not break the governance layer.
Logs are an output, not the product. Kairós registers use cases, attaches ownership, enforces gates, detects drift and produces evidence.
Kairós instruments compliance; it does not eliminate compliance officers. Human accountability stays human. What it removes is the gap between policy and operation.
Kairós is installed through a PRAGMAGROUP engagement. It starts with a two-week Discovery: governance gap map, signed criteria, no further commitment. Or look at the product directly first.