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We shipped AI and it broke

Integrated managed orchestration for enterprises that already tried the easy path

Enterprises that shipped AI and watched it stall have all hit the same structural ceiling: foundation-model APIs and SaaS-embedded AI accumulate as vendor equity, not as customer equity. The integrated managed orchestration layer closes that gap — multi-model routing, persistent context, integrated evaluation, and governance running as one stack on your data, producing custom-built models you own. This page lays out the seven-discipline framework and the failure modes the architecture prevents.

30-day onboardingSOC 2 certifiedZero AI engineers required
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30 daysFrom kickoff to live AI
98%+Verified output accuracy
0AI engineers you need to hire
SOC 2Certified + HIPAA ready

Industries & Applications

Where the orchestration layer earns its keep

Click any category to explore what we've built — and what we can build for you.

Where the seven disciplines compound

Multi-model routing across the AML stack

Problem

Banks running FICO + Verafin + in-house models route per query class — large-amount transactions to one model, structuring patterns to another, novel patterns to a third — with governance integrated and the audit trail intact for OCC examinations.

Multi-model + governance + audit

Customers running orchestrated AI in production

Ripple Wellness

AI powered wellness CRM and content platform for holistic practitioners.
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For teams who learned the hard part wasn't the build
wasn't the build

Real numbers from real enterprise deployments — not benchmarks.

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AI projects fail to reach production

RAND RR-A2680-1 (2024). The structural reason isn't model capability — it's the integration discipline.

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GenAI pilots fail measurable P&L

MIT Project NANDA (2025). Captured corrections that don't change the model is the silent failure mode.

0

AI projects deliver promised return

Gartner (April 2026). The orchestration layer is what compounds; foundation-model API calls decay.

Built with Empromptu

Seven disciplines, one stack

M

Multi-model routing

Route per query class; failover by confidence threshold. Foundation-model-agnostic by design.

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P

Persistent context

Maintain context across silo'd systems without rebuilding the systems.

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I

Integrated evaluation

In-line accuracy checks per query class; not a post-hoc dashboard.

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G

Governance integration

NAIC, OCC, HRSA-grade audit trails by default. Policy enforced in-line.

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M

Managed monitoring

Post-deployment-decay surfaced and reversible. Drift caught at the source.

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C

Custom-model export

Models exportable to your infrastructure. Yours, not licensed.

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Seven disciplines as one stack

Routing, context, evaluation, governance, monitoring, policy, audit — operated together, not assembled from point tools. The integration discipline that stalls 80%+ of enterprise AI builds is the orchestration layer's deliverable.

Multi-model routing

Query-class routing across foundation models + custom models with confidence failover; model agnosticism + cloud agnosticism by design.

Persistent context

Maintain context across legacy + modern systems your AI app needs to read, without rebuilding any of them.

Integrated evaluation

In-line accuracy + relevance scoring per query class; SME-label feedback closes the loop on the edge cases the model wasn't trained on.

Governance integration

NAIC, OCC, HRSA-grade audit trails by default; policy enforcement is a first-class capability, not a bolt-on review layer.

Managed monitoring

Post-deployment decay surfaced + reversed via the correction-and-learning loop; the silent-drift failure mode becomes observable.

Policy enforcement

Policy enforced in-line with model invocations, not as a separate review pass. Same orchestration layer + same audit trail.

Audit + export

Every routing decision audit-trailed; the custom models trained by your AI apps export cleanly to your infrastructure.

Built for the discipline-vs-capability gap that stalls enterprise AI

The structural reason enterprise AI initiatives stall isn't model capability — it's the integration discipline. Seven capabilities running as one stack: multi-model routing, persistent context, integrated evaluation, governance integration, managed operation, model agnosticism, cloud agnosticism. Custom-built models trained by your AI apps, exportable, audit-trailed, governed by the same layer that produces them.

Ship enterprise AI in 30 days.
No AI team required.

Most teams: 6–12 months, 3–5 AI engineers. With Empromptu:
DiscoveryDay 03
Build AI featureDay 310Working AI
Testing & evalsDay 815
Guardrails & complianceDay 1225
Enterprise deploymentDay 2030Enterprise AI
RefinementDay 2435
Zero AI engineers hired. Zero vendor lock-in.
Not a prototype — enterprise AI
SOC 2 + HIPAA from day one
Your team stays focused on your product

Architecture review with a Founder

25-min technical call. Come with the deployment you've stalled on and the orchestration question you most want to debug. We'll walk the seven-discipline framework against your specific architecture.

30-day onboardingDedicated engineer availableSOC 2 certified

From kickoff to production AI in 30 days. No AI team required.