Marxen
Native AI Labs · Marxen's sovereign deployment practice

Your own AI lab.
Inside your own walls.

Native AI Labs is Marxen's flagship practice for on-premise AI. We design, build, and operationalise a complete sovereign AI stack inside your infrastructure — hardware guidance, model serving, RAG pipelines, applications, security, and operations — then transfer full ownership to your team.

§ 01The problem

Cloud AI has a hidden bill — and it is not just money.

Most enterprises adopt AI by routing sensitive data through foreign cloud APIs. The visible cost is the monthly invoice. The invisible costs are larger: data residency exposure under the DPDP framework, vendor lock-in that compounds with every workflow you build, and a black-box model you can never inspect or audit.

On-premise AI converts recurring operating expense into a one-time capital investment — with full auditability, a near-zero data-leakage surface, and effectively unlimited inference at marginal cost.

≈ 0

Data leakage

None

Vendor lock-in

Marginal

Inference cost

§ 02What we deliver

A complete stack. Six layers. One handover.

Every Native AI Labs deployment includes all six layers. Nothing bolted on later. Nothing hidden behind a vendor.

  1. L1

    Infrastructure

    On-premise GPU server specification and procurement guidance, network segmentation, secure remote access, and management tooling — sized to your exact workload.

  2. L2

    Model serving

    A high-throughput serving stack exposing standard, OpenAI-compatible API endpoints. Model selection from a curated open-weight catalogue, chosen for your language needs, task type, and GPU budget.

  3. L3

    Data & retrieval

    Vector storage, embeddings tuned for Indic languages, and document-grounded retrieval so your AI answers from your documents — not from the open internet.

  4. L4

    Application

    A secure internal interface, department-specific AI agents, and an API gateway with authentication, rate limiting, and full audit logging.

  5. L5

    Security & compliance

    Air-gapped operation as an option, role-based access control, encryption at rest and in transit, complete audit trails, and an architecture built to pass security testing.

  6. L6

    Operations & handover

    Monitoring and alerting, model-update runbooks, structured team training, and a hypercare period — followed by a clean handover. Your team owns and operates the system.

§ 03Indic-first, by requirement

India does not speak one language. Neither should your AI.

Every Native AI Labs deployment treats India's language diversity as a first-class engineering requirement. Sovereign Indian language models and speech systems — Unicode-correct across chat, document, and API outputs, with transliteration and code-switching support for genuinely bilingual enterprise workflows.

  • Tamil
  • Hindi
  • Telugu
  • Kannada
  • Malayalam
  • Bengali
  • Marathi
  • Gujarati
  • Punjabi
  • Odia
  • Assamese
  • Urdu
  • Code-switched · IN-EN
§ 04Engagement tiers

Fixed scope. Clear price. No open meter.

Every tier includes discovery and audit, infrastructure guidance, model deployment, the application layer, security hardening, team training, and hypercare.

Pilot · POC

Starter Lab

A single-server deployment for a pilot or proof of concept.

Built for smaller teams — document Q&A, internal search, summarisation.

Timeline

~8 weeks

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Org-wide● Most chosen

Enterprise Lab

A multi-server, high-availability cluster for organisation-wide use.

Full retrieval stack, voice, multi-model routing, and department-specific agents.

Timeline

~12–14 weeks

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Air-gapped

Sovereign Lab

Air-gapped, classified, or critical-infrastructure grade.

BFSI, healthcare, and government deployments. Fully offline model serving with a complete compliance pack.

Timeline

Scoped per engagement

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§ 05How an engagement runs

Five steps. From audit to handover.

  1. 01

    Discovery & audit

    AI readiness assessment, data-flow mapping, infrastructure sizing, compliance gap analysis.

  2. 02

    Infrastructure build

    Server and GPU setup, network segmentation, access controls.

  3. 03

    Model deployment

    Model serving, fine-tuning on your data, embeddings stack.

  4. 04

    Application layer

    Retrieval pipelines, internal interface, API gateway, role-based access.

  5. 05

    Hardening & handover

    Security review, team training, runbooks, and clean ownership transfer.

§ 06Start here

Start with the audit. Decide with the evidence.

A structured AI readiness audit maps your infrastructure, data workflows, and regulatory constraints — and produces a clear architecture recommendation with sizing, model selection, and a deployment plan. No commitment beyond the audit itself.