Enterprise AI, delivered end to end.
Strategy, architecture, hardware, and platform — one accountable team, from the first workshop to the tenth year of operation. Vendor-neutral, security-first, evidence in every claim.
From strategy to a system in production.
We work alongside your teams — not around them — from the first executive workshop through governance, deployment, and long-term optimisation.
AI strategy & roadmap
A named twelve to thirty-six month plan tied to specific business units, capital envelopes, and measurable outcomes.
AI readiness assessment
Data, tooling, skills, governance, and infrastructure scored against a documented rubric before the first workload.
Enterprise AI architecture
Reference architectures for training, retrieval, inference, and MLOps — mapped to your networks, identities, and compliance zones.
LLM & generative AI consulting
Model selection, fine-tuning, RAG design, evaluation harnesses, and guardrails — with published trade-offs, not vendor pitches.
AI governance & compliance
Policy, model inventory, and audit workflows aligned to ISO/IEC 42001, the NIST AI RMF, and the DPDP Act, 2023.
AI security & responsible AI
Threat models for prompt injection, data exfiltration, and model abuse — with controls mapped to IEC 62443 and OWASP LLM Top 10.
AI use case discovery
Structured interviews and process mining that surface candidates ranked by value, feasibility, and time to production.
AI ROI assessment
Cost of inference, cost of latency, cost of migration — quantified against status quo before the build begins.
Digital transformation
AI-enabled workflows that connect ERP, MES, SCADA, and knowledge systems without breaking existing systems of record.
AI Center of Excellence
Operating model, hiring plan, standards, and shared services so AI compounds across the organisation rather than fragmenting.
Compute that lives where the work does.
Public cloud is a choice for training. It is rarely the right choice for regulated data, real-time inference, or ten-year unit economics. We design and deliver the infrastructure that changes those trade-offs.
GPU cluster design
Node topology, InfiniBand or RoCEv2 fabric, storage tiers, and scheduling — sized for the workload, not the datasheet.
Private AI cloud
Multi-tenant compute inside your perimeter, with quotas, chargeback, and self-service — API-compatible with common tooling.
Micro data centers
Sealed, prefabricated cabinets from 20 kW to 150 kW IT class — outdoor-capable, crane-in to first inference in a day.
AI servers
H100, H200, L40S, MI300X, and Grace-Hopper class systems specified against measured workloads and total cost.
High-performance storage
NVMe-oF, parallel file systems, and object storage sized for training throughput and inference tail latency.
Kubernetes deployment
Hardened clusters with GPU operators, network policies, secrets management, and admission controls — GitOps by default.
Edge AI infrastructure
Rugged inference nodes at the machine — filtered intake, wide-temperature class, out-of-band managed, air-gap capable.
Hybrid cloud AI
Placement rules that route training to where compute is cheapest and inference to where latency and sovereignty demand.
Network & security
Segmentation, east-west inspection, and zero-trust identities across the training fabric and the inference edge.
Monitoring & observability
Metrics, logs, traces, and GPU-level telemetry unified in one pane, with SLOs on latency, availability, and utilisation.
GPU computing clusters
Training and fine-tuning fabrics from a single node to multi-rack systems.
AI research labs
Reproducible environments for universities, hospitals, and R&D groups.
Enterprise AI platforms
End-to-end stacks for chat, retrieval, agents, and workflow automation.
Edge AI systems
Rugged inference at the machine, with fleet management from day one.
On-prem AI clouds
Private, multi-tenant compute inside your perimeter.
Hybrid AI infrastructure
Workload placement across on-prem, edge, and public cloud by policy.
Compact. Sealed. Built for AI.
A micro data center is a factory-built, sealed cabinet — power, cooling, compute, network, and management delivered as one commissioned unit. NAVAXON CORE ships in the C20, C60, and C150 IT classes; the site sees a crane, not a construction project.
Faster inference
Single-digit millisecond decisions beside the machine.
Data sovereignty
Regulated data stays inside the site, the state, or the country.
Lower cloud cost
Backhaul and egress removed from the operating model.
Edge deployment
Outdoor-capable, IP55 sealed, wide-temperature class.
High availability
N+1 power, redundant cooling, dual-path network.
Disaster recovery
Site-to-site replication and air-gap restore workflows.
Planning
Workload sizing, floor plans, and total-cost modelling.
Architecture
Power, cooling, network, and compute reference designs.
Hardware selection
Vendor-neutral BoM with measured, not marketed, numbers.
Rack design
Airflow, weight, cabling, and serviceability by drawing.
Power & cooling
UPS, PDU, thermal loops, and derating curves published.
GPU infrastructure
Node, fabric, and storage sized to the training profile.
Storage
NVMe, parallel file systems, and object tiers.
Networking
Segmented fabrics, out-of-band, and zero-trust identities.
Deployment
Factory build, burn-in, crane-in, and commissioning.
Monitoring
Power, thermal, security, and SLA in a single pane.
Maintenance
Remote operations, predictive alerts, and site visits by exception.
Manufacturing
Mining
Healthcare
Education
Government
BFSI
Assessment
Site survey, workload profiling, and constraints on paper.
Design
Architecture, single-line diagrams, and thermal model.
Procurement
Vendor-neutral bill of materials with published lead times.
Installation
Foundations, power, fiber, and crane-in.
Deployment
Commissioning, workload cutover, and acceptance tests.
Support
Remote operations under GRID and PULSE for the decade after.
Your organisation's private AI operating system.
One platform for chat, retrieval, agents, and automation — deployed on your infrastructure, integrated with your identity provider, and evaluated against your own benchmarks.
Private LLM
Open-weight models served on your hardware, versioned and evaluated against your own benchmarks.
Enterprise chat
A single conversational surface with SSO, retention controls, and per-workspace policy.
Document intelligence
Ingestion, OCR, layout parsing, and structured extraction from contracts, drawings, and reports.
Knowledge base search
Hybrid keyword and vector retrieval across sanctioned sources, with citations on every answer.
RAG engine
Chunking, embedding, reranking, and answer synthesis — instrumented end to end for accuracy and drift.
AI agents
Task-scoped agents with typed tools, human checkpoints, and full audit trails.
Workflow automation
Deterministic orchestration around model calls — retries, fallbacks, and idempotent side effects.
Domain assistants
HR, legal, finance, sales, and support assistants grounded in your policies and systems of record.
Meeting intelligence
Transcription, action extraction, and summarisation with configurable retention and redaction.
API gateway
Rate limits, quotas, and per-tenant keys in front of every model and tool.
Model management
Registry, evaluation, canary rollout, and rollback — for open-weight and hosted models alike.
Role-based access
Group, project, and record-level authorisation, integrated with your identity provider.
Cloud
Managed by NAVAXON on a sanctioned hyperscaler tenant.
Private cloud
Single-tenant, deployed in your VPC or sovereign region.
On-premises
Runs entirely on your hardware — air-gap capable.
Hybrid
Placement by policy across on-prem, edge, and cloud.
| Dimension | Public AI | Enterprise AI Platform |
|---|---|---|
| Privacy | Prompts and data traverse a shared service. | Data stays inside your perimeter. |
| Customisation | Limited to prompt and light fine-tuning. | Full fine-tuning, retrieval, and tool integration. |
| Data ownership | Governed by the provider's terms. | You own the data, the embeddings, and the logs. |
| Compliance | Provider-defined controls. | Mapped to ISO/IEC 42001, NIST AI RMF, and DPDP. |
| Security | Shared responsibility with a public tenant. | Single-tenant with your identity and network controls. |
| Model choice | The provider's models. | Open-weight and hosted models side by side. |
| Cost control | Per-token pricing at scale. | Capex and steady-state opex on your hardware. |
Intelligent infrastructure for Industry 4.0.
On-premise and edge AI systems that close control loops, keep data on site, and scale from the machine to the plant.
Manufacturing
Machine-level edge inference for quality, maintenance, and process control loops.
Mining
Rugged edge nodes for autonomous haulage, safety monitoring, and pit-to-port telemetry.
Energy & utilities
Rugged edge and micro data centers for grid balancing and remote site monitoring.
Healthcare
On-premise inference for imaging triage and clinical records inside the hospital network.
Banking & insurance
Private LLM stacks for underwriting, fraud, and customer operations under DPDP controls.
Government
Sovereign AI infrastructure for case triage, citizen services, and national data residency.
Telecommunications
Regional AI infrastructure aligned to core and RAN topology for OSS/BSS analytics.
Logistics
Edge inference at gates and yards for dispatch, damage detection, and slot allocation.
Education
Shared research clusters and governed campus AI cloud with quotas and chargeback.
Vendor-neutral. Open where it matters.
We choose components against measured workloads, not slide decks. The stack below is what we most often deploy — the right stack for your system is the one we agree on paper before anything is built.
- React
- Next.js
- TypeScript
- Python
- FastAPI
- Node.js
- Go
- LangChain
- LlamaIndex
- vLLM
- Hugging Face
- NVIDIA NIM
- OpenAI
- Anthropic
- Gemini
- Ollama
- Open-weight
- PostgreSQL
- Redis
- MongoDB
- pgvector
- Qdrant
- Milvus
- Docker
- Kubernetes
- Terraform
- Helm
- Prometheus
- Grafana
- NVIDIA GPU
- AWS
- Azure
- Google Cloud
Six commitments, one accountable team.
Enterprise-grade AI expertise
Engineers who have delivered production AI in regulated industry, not demos.
Vendor-neutral consulting
No resale margins on the recommendation. The BoM is chosen against your workload.
Private AI deployments
On-premises and air-gap capable by design, not by exception.
Infrastructure and software, one roof
The rack, the platform, and the models come from the same accountable team.
Security-first architecture
Zones, conduits, and zero-trust identities mapped to IEC 62443 and DPDP.
End-to-end implementation
From the first workshop to the tenth-year support contract — one number to call.
Seven phases. One accountable team.
Discover
Interviews, process mining, and constraints captured on paper.
Assess
Readiness scored across data, tooling, skills, and infrastructure.
Architect
Reference architecture and BoM agreed before the build starts.
Build
Factory build, burn-in, and integration under measured acceptance tests.
Deploy
Commissioning, cutover, and go-live with named runbooks.
Optimise
Latency, cost, and accuracy improved against published baselines.
Support
Remote operations, predictive maintenance, and quarterly reviews.
Build your enterprise AI future.
Whether the next step is a strategy workshop, a GPU cluster, a private LLM, or a full enterprise AI platform — one team, one contract, one number to call.