The hardware
An inference engine built on high-performance NVIDIA GPUs, tuned for real-time workloads.
- Containerised model deployment
- Plug-and-play expansion
- Low-latency inference
- Edge storage and compute
All-in-one AI infrastructure
The future of on-premise intelligent systems
A turnkey AI system for organisations that want artificial intelligence inside their own walls — software, AI training consultancy and NVIDIA inference hardware delivered as a single package.
powered by NVIDIA · HPE collaboration · MCP-nativeExecutive summary
A complete AI system, delivered inside your own walls
Lentur AI is a turnkey AI system for organisations that want artificial intelligence on-premise — combining software, AI training consultancy and NVIDIA inference hardware into a single package.
Every deployment is a personalised solution, co-developed with your team, fully installed and tuned for your environment — scalable, secure, and yours to keep. Our own consumer product, Mosea, runs on the same stack and proves it in production every day.
On-premiseNVIDIA GPUsOpen-weight modelsMCP-nativeCo-developed
How the system fits togetherHardwareGPU inference nodes in your data centre.
SoftwareServing, monitoring, retraining, MCP.
PeopleEngineers beside your team for 3 months.
Why Lentur AI
AI keeps stalling for three reasons — we remove all three
Where enterprises get stuck
- Fragmented AI workflowsData, training and deployment handled by separate vendors, with nobody owning the result.
- Expertise gapNo in-house AI domain expertise, and no local infrastructure to run models on.
- Security and compliance riskCloud-only AI moves sensitive data outside the organisation — often outside the rules.
How Lentur AI answers
- 1All-in-one integrationHardware, AI training and deployment in one package.
- 2On-premise deploymentFull control over data, latency and access.
- 3Scalable inferenceAdd nodes as demand grows — no re-architecture.
- 4Continuous improvementA retraining pipeline that adapts as your data drifts.
The concept
Hardware, software and people — as one system
The software
A custom stack that keeps the model useful long after go-live.
- Model serving and monitoring
- Data drift detection
- Performance analytics
- Secure retraining pipeline
- Agentic orchestration and MCP
The human element
A dedicated team that makes the solution measurable and interpretable.
- AI engineer / trainer — builds and tunes models
- Domain expert — contextualises for your field
- Your stakeholder — aligns outcomes with business goals
“Think of it as your own private AI cloud — shrink-wrapped and installed in your building.”
How it works
One system, installed inside your network
Five stages, from connecting your first data source to a model that keeps improving after go-live.
Your systems become sources
ERP, CRM, ticketing, data warehouses, documents and sensor streams are connected to the node. Nothing is copied to a vendor cloud — the connection points inward.
The GPU platform
NVIDIABuilt on NVIDIA — sized to the workload
NVIDIA H200
141 GB HBM3eLarge-model training and high-throughput inference for data-centre deployments.
RTX 6000 Pro
96 GB · BlackwellThe efficient workhorse for on-premise inference and fine-tuning in a single server.
NVIDIA GB300
Grace Blackwell UltraRack-scale capacity for frontier reasoning models and multi-tenant enterprise AI.
DGX Spark
Desk-side AI systemA compact starting point for pilots, edge sites and departmental deployments.
Start with one node and add capacity as demand grows — the software stack stays identical from Spark to rack.
Core capabilities
Four kinds of intelligence, one platform
Seeing and hearing what your operation produces
Vision, audio, sensors and signals — object detection, scene understanding, acoustic anomalies and IoT streams, processed on nodes that sit beside the equipment rather than in a distant cloud.
Manufacturing · surveillance · healthcare imaging · asset inspectionTalk to us about thisperception · edge inference
Object detectionReal-time streams from cameras and line sensors.
Scene understandingContext across frames, not isolated snapshots.
Acoustic anomaliesSound signatures that precede a fault.
IoT signal fusionMany sensors reduced to one interpretable state.
The AI lifecycle
From concept to operation in three months
We structure the full lifecycle of intelligence inside one co-creation framework — then the cycle repeats.
- 01
Discovery
Define metrics, objectives and key data sources.
→ Clear success criteria - 02
Data collection
Gather and structure data together with your team.
→ Clean, representative dataset - 03
Model training
Co-develop and tune the model on your infrastructure.
→ Initial working model - 04
Deployment
Ship to production on your own inference nodes.
→ Operational AI - 05
Feedback loop
Monitor drift and retrain as the business changes.
→ Sustainable performance
Phases 01–04 — 3 monthsPhase 05 — continuous maintenanceEvery cycle raises your organisation’s AI maturity.
Where it applies
Four patterns we build for
1 / 4
Deployment pattern
TELECOM
Challenge — Churn models cannot keep up with changing behaviour.
Impact — Multimodal behaviour models with a retraining loop, serving millions of users at low latency.
Talk to us about this
Deployment pattern
HEALTHCARE
Challenge — Clinical data is siloed and sensitive; cloud inference is not compliant.
Impact — On-premise nodes with federated learning across sites, so models improve without moving patient data.
Talk to us about this
Deployment pattern
FINANCE & INSURANCE
Challenge — Risk and pricing models need agile iteration under strict data rules.
Impact — An in-house training loop, retrained on new policy data, shortening the underwriting cycle.
Talk to us about this
Deployment pattern
MANUFACTURING
Challenge — Fault detection needs intelligence at the edge, not in a distant cloud.
Impact — Nodes beside the production line: millisecond anomaly detection and predictive maintenance.
Talk to us about thisProof in productionLive
The same engine, already running
Mosea is our own AI content product, built on the Lentur stack and open to the public at mosea.app. It is how we prove the engine before we install it in your data centre.
Top 1
Ranked first among startups by the Artificial Intelligence community.
Recognition from practitioners, not a paid award — voted by a community that builds with these tools every day.
Visit mosea.app
Brand DNA
The engine learns a brand's own visual and verbal style, then holds it across every asset.
Agentic pipeline
Multi-step generation with people supervising the output — a first draft in under 200 seconds.
MCP-native
Usable directly inside Claude, ChatGPT and Gemini through the Mosea MCP router.
Same stack
Serving, monitoring, drift detection and retraining — identical to an on-premise install.
Real work
Five brands, five looks — all made by the engine
Different brands, different looks — each one follows that brand’s own style, because Brand DNA drives it.
Video, made the same way
Cinematic motion generated from a brief — rendered with Seedance 2.0 under our exclusive ByteDance partnership, then finished in the Mosea pipeline.
Collaboration
Working with HPE, NVIDIA and ByteDance
- Joint enterprise engagements — HPE brings the certified infrastructure, NVIDIA the compute, Lentur the AI system on top.
- One accountable delivery team — Hardware, deployment and model work land together, not vendor by vendor.
- OEM with HPE — in progress — Our software shipping pre-installed inside HPE AI systems, sold as one package.
Engagement model
What an engagement includes
| Component | What you get | Commercials |
|---|---|---|
| Discovery & scoping | Metrics, objectives, data sources and a written success definition. | Based on inquiries |
| 3-month co-development | Dedicated AI engineers and domain experts building the model with your team. | Based on inquiries |
| On-premise deployment | Inference nodes installed and tuned in your environment, sized to the workload. | Based on inquiries |
| Annual technical support | Monitoring, drift detection, retraining, updates and an engineer on call. | Based on inquiries |
| Scale-out | Additional nodes, model distillation and load balancing as demand grows. | Based on inquiries |
Every engagement is scoped to the use case, the data and the infrastructure already in place — so we quote after the first conversation, not before it.
Resources
Everything you need before the first conversation
The AI lifecycle
Discovery through the feedback loop — what happens in each of the five phases, and what you hold at the end of each one.
Engagement model
What a Lentur engagement includes, from scoping and co-development through deployment, support and scale-out.
Mosea — the engine in production
The same stack we install on-premise, open to the public. Try it, then talk to us about your own deployment.
Open MoseaThe MCP router
Connect it once and your internal systems become governed tools inside Claude, ChatGPT or Gemini.
Sizing the GPU platform
H200, RTX 6000 Pro, GB300 and DGX Spark — where each one fits, and how the stack stays identical across them.
How it works, end to end
Walk the five stages, from connecting your first source to a model that keeps improving after go-live.
LENTURAI
Open for strategic partnership
We are looking for infrastructure partners, system integrators and enterprises ready to bring AI on-premise — and to build the first deployment together.
OEM & hardware partnersSystem integratorsIndustry principalsEnterprise pilots
Steven Wijaya · CEO · Haris Putratama · CTO