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Enterprise

AI that your enterprise controls.

Unified visibility, governance, and intelligence across every AI vendor, model, and team. No lock-in. Your data stays yours. Governance persists across infrastructure changes.

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Vendor-neutral across
ANTHROPICOPENAIGOOGLEMISTRALDEEPSEEKCOSINE

The control layer

See. Govern. Own.

Three capabilities that give the organisation complete control over AI, regardless of which vendors or tools teams use.

SEE

Unified visibility

Know what the organisation is doing with AI. Which vendors, which teams, which decisions are being delegated to models. Cross-vendor, cross-team, real-time.

GOVERN

Persistent governance

Policies, audit trails, and compliance that persist across infrastructure changes. Define once, enforce everywhere. Switch vendors without losing governance.

OWN

Enterprise intelligence

Every AI interaction compounds into organisational memory. Decisions, knowledge, and patterns stay with the organisation. Swap vendors without losing institutional knowledge.

The control gap

AI activity is fragmenting faster than governance can follow

AI is spreading across employees, teams, business units, applications, vendors, and autonomous agents faster than organisations can establish consistent oversight and accountability.

78%
of employees bring their own AI tools to work
Microsoft Work Trend Index, 2024
33%
of enterprise software will include agentic AI by 2028
Gartner, 2025
15%
of work decisions will be made autonomously by AI by 2028
Gartner, 2025
40%
of enterprise apps will have AI agents by end of 2026
Gartner, 2025

Questions leadership often cannot answer

1

Which AI-assisted decisions materially impact financial outcomes, and how consistent are they across teams and models?

2

Where does AI create unmonitored operational dependency across critical workflows?

3

Where does AI create compliance exposure due to ungoverned workflows or model variance?

4

Which business-critical processes rely on AI models the enterprise cannot audit or control?

5

Where is AI amplifying enterprise risk through inconsistent governance or cross-vendor fragmentation?

The landscape

Every tool governs its layer. The gaps between them are uncontrolled.

Enterprise AI operates across vendors, models, and agents simultaneously. Each domain has capable tooling. None spans the full stack.

Manage policies within a single vendor, but cannot enforce governance consistently across multiple vendors, tools, and deployment environments.

Protect infrastructure and detect threats, but cannot govern AI behaviour, decision quality, or cross-vendor activity.

Monitor system health and performance, but cannot provide visibility into enterprise-wide AI activity, decisions, or knowledge flows.

Store and retrieve knowledge, but cannot govern how AI generates, transforms, or distributes knowledge across the organisation.

Execute AI agents within their ecosystem, but cannot govern agent activity, decisions, or outputs across vendors and tools.

Automate tasks within defined pipelines, but cannot enforce cross-vendor governance, audit trails, or organisational policy.

GovernanceSecurityObservabilityKnowledgeAgentsAutomation

Neutrality is architectural. Swarmix governs activity across competing platforms because its only incentive is governance, not ecosystem expansion.

Governance — Manage policies within a single vendor, but cannot enforce governance consistently across multiple vendors, tools, and deployment environments.
Security — Protect infrastructure and detect threats, but cannot govern AI behaviour, decision quality, or cross-vendor activity.
Observability — Monitor system health and performance, but cannot provide visibility into enterprise-wide AI activity, decisions, or knowledge flows.
Knowledge — Store and retrieve knowledge, but cannot govern how AI generates, transforms, or distributes knowledge across the organisation.
Agents — Execute AI agents within their ecosystem, but cannot govern agent activity, decisions, or outputs across vendors and tools.
Automation — Automate tasks within defined pipelines, but cannot enforce cross-vendor governance, audit trails, or organisational policy.

Security and privacy

Your data never leaves without your permission

Local-first processing, on-device PII masking, and encryption at every layer. Sensitive operations run in a separate secure process that the user interface cannot access.

PII masked before it leaves

Data Shield combines pattern matching and on-device ML (Piiranha model) to detect and mask sensitive data before it reaches any AI vendor. Detection runs locally. Original data never leaves the device.

Encrypted at every layer

Transcripts encrypted with AES-256-GCM using per-device keys. Key derivation via Argon2id. Escrow key wrapping for organisational recovery. OS keychain integration for at-rest protection.

Transform proxy

Every AI request passes through a local transform proxy. PII masking, policy compliance checks, and audit logging happen before anything reaches the vendor. Responses are unmasked on return.

Security deep dive

IT governance

Deploy to every device. Enforce from one dashboard.

Identity, policy, and deployment controls that IT teams need before approving any new platform.

SSO and SCIMUsers authenticate through the existing identity provider. Groups and memberships sync automatically via SCIM 2.0. Deprovisioning follows the IdP lifecycle.
Policy engineDeclare which AI vendors, models, tools, and plugins teams can access. Group-based targeting, phased rollouts, and one-click rollback.
MDM compatibleDistribute through Intune, Jamf, or any standard MDM. The installer authenticates, fetches an encrypted policy, and provisions the complete environment.
IT governance details

Intelligence

Everything else exists to generate this.

Security protects the data. Governance structures the decisions. Intelligence is what emerges when all of that compounds into a strategic asset. Cost, adoption, workforce, and session-level analytics across every vendor and team.

Cost intelligence

Task-level economics across every AI vendor. Chargeback per department, model routing optimisation, and ROI quantification.

Adoption and workforce

Who is productive, who is struggling, which teams need training. Seat utilisation, feature adoption, and AI fluency benchmarking.

Session deep-dive

Visualise how work flows between agents, spot repeated patterns, and drill into any session with event-level detail. On-device ML, nothing leaves the machine.

Intelligence deep dive

Compliance

Audit trails, decision provenance, and regulatory readiness

Audit trailsEvery decision, vote, knowledge change, and ticket transition recorded with full provenance. Structured for SOC 2 and ISO 27001 reporting.
EU AI Act readinessHuman oversight, immutable audit trails, decision-point logging, and persistent identity management. Built for the requirements phasing in through 2026 and 2027.
ISO 42001 coverageAI governance, risk management, data management, monitoring, audit, and continual improvement. Six clause areas with supporting infrastructure.
26/27
EU AI Act phased enforcement
7%
of global revenue, maximum penalty

Source: EU AI Act, Gartner 2026

The market reality

AI is already everywhere in your organisation. The question is whether you control it.

78%
of employees use AI tools outside IT control
Microsoft Work Trend Index 2024
$670K
additional cost per breach involving shadow AI
IBM Cost of a Data Breach 2025
94%
of enterprises fear vendor lock-in
Flexera State of the Cloud 2025
75%
of world economies will regulate AI by 2030
Gartner 2026

See how Swarmix works for your organisation.

Deploy in weeks, not months. Works with the existing AI stack. No vendor lock-in.

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See how Swarmix works

A walkthrough of unified visibility, governance, and intelligence across every AI vendor and team.

  • Deploy in weeks
  • Works with any existing AI stack
  • SOC 2 compliant, GDPR ready