Architecture-Led. Sovereign by Design. Controlled AI Infrastructure.

We build sovereign AI infrastructure, distributed intelligence systems, and foundation-model platforms through controlled engineering, domain review, and private delivery governance.

Sovereign AI

Private AI infrastructure
Secure runtime systems
AI governance framework
Data sovereignty by design

Distributed Systems

Scalable intelligence systems
Multi-agent orchestration
High reliability architecture
Distributed compute and runtime

Distributed Delivery Model

Distributed specialist network
Capability expansion model
Project-based activation
Domain expert engagement

Research Capability

AI systems research
Systems engineering
Runtime and infrastructure R&D
Prototype review and technical assessment

Enterprise Delivery

Architecture and design
Build and implementation
Security and compliance
Operations and optimization

Secure by Design

Security, privacy, and compliance built into every layer.

Multi-Region Delivery

Specialist capability across approved technical regions.

Scalable by Model

Engage the right experts only when the program needs them.

Focus on Impact

Engineering work is tied to measurable institutional outcomes.

GPU Runtime Engineering

CUDA kernel profiling, model-serving, and inference runtime planning.

Sovereign AI/LLM

Private Service Foundation-model and sovereign infrastructure program Private delivery by scope approval only

Sovereign AI for nations, institutions, and organizations requiring dedicated foundation-model capability

Foundation model capability maturity depends on integrated ownership across compute infrastructure, distributed systems engineering, training systems, model architecture capability, runtime optimization, evaluation frameworks, deployment systems, governance capability, security systems, operational resilience, and long-term infrastructure ownership.

Compute Distributed Systems Data Systems Model Systems Training Systems Runtime Systems Evaluation & Benchmark Deployment Security Reliability Governance Research Organization Blueprint Project Handover Runbook Domain Experts CUDA Engineering

Compute Infrastructure

NVIDIA GB200 Grace Blackwell
NVIDIA DGX GB200 NVL72
NVIDIA B200 Tensor Core
NVIDIA H200 Tensor Core
NVIDIA HGX B200
NVIDIA HGX H200
NVIDIA GH200 Grace Hopper
NVIDIA H100 Tensor Core
NVIDIA A100 Tensor Core
NVIDIA L40S

Distributed Training

Tensor Parallelism
Pipeline Parallelism
Data Parallelism
Expert Parallelism
Sequence Parallelism
ZeRO Optimization
Distributed Gradient Systems
Checkpoint Sharding

Distributed Runtime

GPU Scheduling Systems
CUDA Runtime Profiling
Distributed Runtime Systems
Runtime Orchestration
Runtime Telemetry
Distributed Inference
Speculative Decoding
Dynamic Batching
KV Cache Optimization

Cluster Systems

Cluster Scheduling Systems
Multi-node Orchestration
Multi-region Infrastructure
Distributed Cache Systems
Cluster Failover Systems
Distributed Storage Systems

Interconnect and Storage

NVLink
NVSwitch
InfiniBand
Parallel Filesystem
Distributed Object Storage
Checkpoint Storage Systems
High Throughput Storage

Evaluation & Benchmark Expansion

MMLU-Pro, GPQA Diamond, BBH, ARC-Challenge, GSM8K, and MATH evaluation tracks
HumanEval, MBPP, SWE-bench Verified, LiveCodeBench, and repository-level coding tests
LongBench, RULER, needle-in-a-haystack, multi-hop retrieval, and context-retention tests
ToolBench-style tool execution, structured-output validity, and transaction-safety benchmarks
Domain scientist review, PhD-level advisory review, and professor-level benchmark review
HELM-style regression matrix with baseline comparison, release gates, and score drift review

Training Assurance

Loss-curve governance, gradient-norm telemetry, overflow checks, and convergence anomaly review
Dataset contamination audit, benchmark holdout protection, tokenizer coverage audit, and corpus mixture review
NCCL fabric health checks, all-reduce profiling, checkpoint reproducibility, and restart rehearsal
Optimizer-state integrity, activation checkpointing policy, sharded checkpoint validation, and artifact hashing

Runtime & Inference Engineering

Time-to-first-token, p95/p99 latency, tokens per second, and saturation-curve profiling
KV-cache fragmentation analysis, continuous batching, speculative decoding acceptance rate, and queue discipline
CUDA kernel profiling, custom operator review, memory coalescing review, and CUDA engineer handoff notes
Quantization regression tests, model routing canaries, rollback rehearsals, and capacity-failure simulation
Kernel profiling, memory-bandwidth pressure review, tensor-parallel serving layout, and inference cost envelope

Security & Governance Testing

Prompt injection suite, jailbreak robustness, adversarial instruction testing, and unsafe-tool-call review
PII leakage checks, membership-inference risk review, memorization probes, and data-extraction tests
Model card, system card, dataset card, release evidence, red-team log, and rights-transfer register
Access governance, audit trails, environment isolation, incident response, and post-release monitoring protocol

Tier I

Domain Foundation Model

This level funds a domain foundation model program with lawful corpus acquisition, data lineage, controlled training runs, safety evaluation, private inference readiness, and transfer of agreed model artifacts.

Capability Development Timeline: 9-15 months
Distributed Engineering Organization: 180+ engineers, domain-specific scientists, PhD-level domain advisors, professor-level domain reviewers, and operators
Replacement Complexity: 10+ years
Defensibility: Controlled infrastructure, governance, and delivery model

Private Service Asset Transfer Buyer retains 100% ownership of assigned deliverables Model weights ownership transfer Dataset ownership agreement Private training environment

Parameter Architecture

3B Parameters
7B Parameters
13B Parameters

Model Architecture

Dense Transformer
Retrieval Architecture
Embedding Systems
Tokenizer Systems
Domain Alignment Systems
Domain instruction tuning
Tensor-parallel inference layout
KV-cache management policy

Token Scale

500B Training Tokens
1T Training Tokens
Mixture weighting and deduplication policy
Held-out evaluation corpus

Dataset Systems

Domain Corpus Engineering
Dataset Validation
Dataset Governance
Synthetic Data Generation
Data Lineage Systems
Source-rights ledger
Contamination and PII review

Benchmarks

MMLU
GSM8K
ARC
HumanEval
Domain task benchmark
Threat Intelligence Benchmark
Detection Rule Benchmark
Release scorecard

Deployment

Private Deployment
Hybrid Deployment
Edge Deployment
Model-serving topology
Rollback and release procedure
Containerized serving mesh
Inference gateway and request routing

Reliability

Autoscaling
Runtime Monitoring
Fault Tolerance
Checkpoint restore validation
Inference latency envelope

Security

AI Governance
Infrastructure Security
Prompt Injection Resilience
Access-control policy
Model card and system card

Release Package

Weights and tokenizer transfer plan
Evaluation evidence register
Deployment runbooks
Risk acceptance log
Ownership and license schedule

Engineering Organization

Foundation Research Engineer
Applied AI Research Engineer
Alignment Engineer
Evaluation Engineer
Distributed Systems Engineer
Runtime Systems Engineer
CUDA Runtime Engineer
HPC Engineer
GPU Systems Engineer
Data Engineer
Data Pipeline Engineer
Synthetic Data Engineer
Domain Scientist
PhD-Level Domain Advisor
Professor-Level Domain Reviewer
Platform Engineer
MLOps Engineer
Reliability Engineer
AI Security Engineer
Governance Engineer

Program Components

Foundation research plan, scaling assumptions, and architecture decision record
Corpus acquisition, licensing review, source-rights ledger, and exclusion register
Private training environment, experiment tracking, checkpoint policy, and reproducibility log
Synthetic data pipeline with quality filters and provenance controls
Evaluation suite, safety tests, red-team report, and release scorecard
Inference architecture, runtime monitoring, rollback procedure, and operations runbook
Domain scientist review, PhD-level advisory, and professor-level domain review
Assigned artifact transfer for weights, tokenizer, checkpoints, evaluation evidence, and deployment documentation
Multi-year support plan with maintenance, review cadence, and governance handover

Infrastructure Scale

Dedicated GPU capacity plan with training, evaluation, and inference allocation
Multi-petabyte corpus, checkpoint, and artifact storage design
Private or hybrid compute environment with isolated access boundary
Dedicated domain research and evaluation workstream
Private deployment with edge or hybrid operating option
Controlled inference, telemetry, and incident response environment

Assigned Ownership Position

Buyer retains 100% ownership of assigned deliverables
Model weights, tokenizer, checkpoint, and evaluation artifact transfer schedule
Dataset ownership and permitted-use agreement with source authority map
Private training access control and artifact custody record
Architecture blueprint, model card, system card, and operations runbook
Source-material, synthetic-data, and third-party license register

Cost Basis: Rp2T

The budget is tied to governed model creation: lawful corpus access, controlled training, measurable release gates, red-team review, private deployment readiness, and ownership-grade artifact transfer.

Program Budget

USD 125M (Rp2T)

Request Proposal Technical Consultation

Tier II

Native Foundation Model

This level funds a native model program with larger training and evaluation obligations, air-gapped or sovereign deployment options, stronger runtime security, and a dedicated transfer package for assigned model assets.

Capability Development Timeline: 15-21 months
Distributed Engineering Organization: 400+ engineers, domain-specific scientists, PhD-level domain advisors, professor-level domain reviewers, and operators
Replacement Complexity: 12+ years
Defensibility: Controlled infrastructure, governance, and delivery model

Private Service Asset Transfer Buyer retains 100% ownership of assigned deliverables Model weights ownership transfer Dataset ownership agreement Private training environment

Parameter Architecture

13B Parameters
32B Parameters
Architecture ablation plan
Tokenizer and context-window evaluation

Architecture

Dense Transformer
Sparse Attention
Retrieval Systems
Long Context Systems
Tool-use interface design
Domain adaptation path
FSDP / ZeRO-3 training plan
Distributed checkpoint orchestration

Token Scale

1T-5T Tokens
Multilingual mixture plan
Deduplication and contamination controls
Holdout benchmark protection

Dataset Capability

Structured Intelligence Dataset
Unstructured Intelligence Dataset
Multilingual Dataset
Synthetic Dataset Pipeline
Dataset rights register
Data quality scorecards

Benchmark Systems

MMLU
GPQA
BBH
HellaSwag
HumanEval
MBPP
Tool Execution Benchmark
Safety and refusal evaluation

Deployment

Sovereign Deployment
Air-gapped Infrastructure
Multi-region Deployment
Inference control plane
Artifact promotion workflow

Reliability

Distributed Failover
Health Monitoring
Checkpoint Recovery
Load testing and capacity model
Release rollback drill

Security

Runtime Security
Model Isolation
AI Governance
Air-gap operating procedure
Audit trail and access review
mTLS boundary enforcement
Audit logging and access telemetry

Transfer Package

Weights, tokenizer, checkpoints
Training configuration and run ledger
Evaluation and red-team evidence
Deployment manifests
License and ownership schedule

Additional Engineering

CUDA Engineer
CUDA Kernel Engineer
CUDA Performance Engineer
Kernel Engineer
Runtime Optimization Engineer
Domain Scientist
PhD-Level Research Advisor
Professor-Level Evaluation Reviewer

Program Components

Native foundation-model architecture plan with scaling law, context, and retrieval decisions
Corpus acquisition, multilingual source review, licensing register, and contamination controls
Distributed training operations with checkpoint cadence, experiment ledger, and recovery validation
Synthetic data generation, human review queue, and quality acceptance rules
Evaluation, safety, red-team, tool-use, and long-context release gates
Air-gapped or sovereign runtime architecture with deployment manifests and operations runbook
Domain scientist review, PhD-level advisory, and professor-level evaluation review
Assigned transfer of model artifacts, training evidence, evaluation records, and governance documentation
Multi-year support plan with update review, security reassessment, and runtime monitoring cadence

Infrastructure Scale

Dedicated GPU capacity plan for training, tuning, evaluation, and serving
High-throughput object, checkpoint, and evaluation-artifact storage
Air-gapped or sovereign deployment option with controlled promotion path
Dedicated evaluation, safety, and security workstream
Multi-region recovery design where required by the operating model
Private runtime, inference control plane, telemetry, and incident procedure

Assigned Ownership Position

Buyer retains 100% ownership of assigned deliverables
Model weights, tokenizer, checkpoint, and registry transfer
Dataset ownership agreement with source authority map and exclusions
Private training environment and isolated artifact store custody record
Architecture blueprint, model card, system card, and runtime runbook
License exclusions and third-party dependency limits documented before handover

Cost Basis: Rp5T

The cost is driven by native model engineering, larger-scale training operations, long-context and multilingual dataset work, air-gapped delivery constraints, security testing, and transferable model governance evidence.

Program Budget

USD 312M (Rp5T)

Request Proposal Technical Consultation

Tier III

Foundation Systems Company

This level funds a foundation systems company capability: model family planning, large distributed training, evaluation infrastructure, runtime platforms, security operations, and an organization capable of repeating the release process.

Capability Development Timeline: 21-30 months
Distributed Engineering Organization: 900+ engineers, domain-specific scientists, PhD-level domain advisors, professor-level domain reviewers, and operators
Replacement Complexity: 15+ years
Defensibility: Controlled infrastructure, governance, and delivery model

Private Service Asset Transfer Buyer retains 100% ownership of assigned deliverables Model weights ownership transfer Dataset ownership agreement Private training environment

Parameter Architecture

32B Parameters
70B Parameters
Model family sizing plan
Pretraining and post-training roadmap

Architecture Systems

Mixture of Experts (MoE)
Sparse Systems
Agent Systems
Multi-agent Systems
Long Context Systems
Cognitive Orchestration
Model routing and tool governance
MoE expert routing
Serving mesh and canary lanes

Token Scale

5T-10T Tokens
Large corpus mixture governance
Domain and multilingual balance plan
Benchmark contamination protection

Benchmark Systems

GPQA
MMLU
BIG-Bench
BBH
SWE-Bench
HumanEval
MBPP
Workflow Automation Benchmark
Multi-agent Benchmark
MITRE ATT&CK Evaluation
Release board evidence pack

Reliability

Runtime Observability
Distributed Recovery
Distributed Checkpoint
Multi-region failover rehearsal
Serving SLO and error budget
NCCL fabric telemetry
Latency SLO and error-budget controls

Security

AI Red Team
Prompt Injection Defense
Adversarial Robustness
Tool-call permission model
Model supply-chain evidence

Company System

Research backlog and experiment governance
Model registry and release train
Evaluation laboratory workflow
Runtime platform operating model
Internal capability transfer plan

Engineering Expansion

AI Systems Architect
Distributed Storage Engineer
Runtime Platform Engineer
CUDA Performance Engineer
Domain Scientist
PhD-Level Systems Researcher
Professor-Level Safety Reviewer
AI Governance Specialist

Program Components

Foundation systems operating model for research, training, evaluation, release, and runtime operations
Corpus acquisition, licensing, mixture governance, contamination audit, and source-rights evidence
Large distributed training plan with checkpoint fabric, recovery drills, and experiment lineage
Multi-agent, workflow, coding, safety, and domain evaluation programs
Red-team operations, adversarial testing, model supply-chain review, and release approval gates
Runtime platform design with model registry, routing, telemetry, rollback, and incident process
Domain scientist review, PhD-level systems research, and professor-level safety review
Assigned transfer of model family artifacts, evaluation assets, runtime documentation, and governance records
Multi-year support model for release repetition, security review, and operational maturity

Infrastructure Scale

Dedicated GPU capacity plan for multiple training, tuning, and evaluation tracks
Exabyte-scale corpus, checkpoint, model registry, and evaluation storage planning
Multi-region compute and recovery architecture with failure-domain mapping
Dedicated research, evaluation, runtime, and security organization
Private sovereign deployment with controlled release train
Distributed runtime and inference fleet planning with observability and rollback

Assigned Ownership Position

Buyer retains 100% ownership of assigned deliverables
Model family weights, tokenizer, checkpoint, and registry transfer
Dataset ownership agreement, lineage evidence, and source-rights ledger
Private training environment with audit boundary and artifact custody controls
Blueprints, runbooks, model cards, system cards, and release evidence assigned under contract
Research artifacts, evaluation assets, and runtime operating procedures transferred by schedule

Cost Basis: Rp9-10T

This budget supports a repeatable foundation systems organization: large training runs, multi-agent and workflow benchmarks, red-team operations, distributed runtime engineering, model family release governance, and assigned asset transfer.

Program Budget

USD 562M - 625M (Rp9T - 10T)

Request Proposal Technical Consultation

Tier IV

Sovereign Foundation Infrastructure

This level funds sovereign foundation infrastructure: persistent compute planning, secure data estates, model-family governance, multi-region recovery, dedicated security operations, and long-term ownership of assigned foundation-model assets.

Capability Development Timeline: 30-42 months
Distributed Engineering Organization: 1,800+ engineering staff, domain-specific scientists, PhD-level domain advisors, professor-level domain reviewers, security staff, and infrastructure staff
Replacement Complexity: 20+ years
Defensibility: Controlled infrastructure, governance, and delivery model

Private Service Asset Transfer Buyer retains 100% ownership of assigned deliverables Model weights ownership transfer Dataset ownership agreement Private training environment

Parameter Architecture

70B Parameters
120B+ Parameters
Model family roadmap
Pretraining, alignment, and serving profiles

Architecture

Foundation Model Family Systems
Sovereign AI Systems
Native Runtime Systems
Evaluation laboratory architecture
Policy and tool-control layer
Policy-aware control plane
Zero-trust runtime boundary

Token Scale

10T+ Training Tokens
National or institutional corpus estate
Rights, retention, and exclusion controls
Continuous data refresh process

Research Systems

Scaling Law Research
Optimization Research
Evaluation Research
Architecture Engineering
CUDA Performance Engineering
Domain Scientist Review
PhD-Level Research Advisory
Professor-Level Evaluation Review
Model family release governance

Benchmark Systems

MMLU
GPQA
HumanEval
SWE-Bench
Reliability Benchmark
Long Context Benchmark
Safety, policy, and domain benchmark board

Reliability

Infrastructure Redundancy
Multi-region Recovery
Runtime Resilience
Disaster recovery runbook
Capacity and failover rehearsal

Security

Infrastructure Hardening
AI Governance Systems
Access Governance
Secret and key-management model
Security operations evidence
Secret rotation and KMS envelope
Compliance-grade audit evidence

Sovereign Operations

Data estate governance
Artifact custody and chain of control
Policy release board
Operational training program
Handover and transition schedule

Program Components

Sovereign foundation infrastructure plan for compute, data estate, model family, and runtime operations
Corpus acquisition, licensing, retention, exclusion, and source authority governance
Distributed training, checkpoint fabric, evaluation lab, and release management system
Safety, red-team, policy, reliability, and domain benchmark programs
Security operations model covering access governance, secrets, audit, incident response, and artifact custody
Native runtime, inference fleet, multi-region recovery, and operational monitoring runbooks
Domain scientist review, PhD-level research advisory, and professor-level evaluation review
Assigned transfer of model family assets, governance evidence, deployment manifests, and operations documentation
Multi-year support model for capacity planning, security reassessment, and release governance

Infrastructure Scale

Dedicated GPU capacity plan across training, evaluation, inference, and reserve capacity
Exabyte-scale storage, checkpoint fabric, model registry, and evidence archive
Multi-region sovereign compute with recovery-zone architecture
Dedicated security, reliability, evaluation, and infrastructure operations
Private sovereign deployment with controlled release and recovery zones
Native runtime, controlled inference, telemetry, and incident management infrastructure

Assigned Ownership Position

Buyer retains 100% ownership of assigned deliverables
Model family, runtime configuration, tokenizer, registry, and checkpoint transfer
Dataset ownership agreement with regulated source handling and exclusion register
Private training environment under client-approved access and custody policy
Architecture blueprint, system cards, release evidence, security records, and runbooks
Sovereign handover schedule for artifacts, documentation, and operator transition

Cost Basis: Rp20T

The price reflects sovereign infrastructure, not a single model run: persistent compute planning, data estates, model family governance, long-term reliability, hardened access control, multi-region recovery, and ownership-grade documentation.

Program Budget

USD 1.25B (Rp20T)

Request Proposal Technical Consultation

Tier V

Large Foundation Model Company

This level funds a large foundation-model company program with deep research work, large-scale training and inference planning, dedicated evaluation science, multi-region reliability operations, and contracted transfer of assigned model assets.

Capability Development Timeline: 42-60+ months
Distributed Engineering Organization: 3,000+ foundation-model staff, domain-specific scientists, PhD-level domain advisors, professor-level domain reviewers, infrastructure staff, evaluation staff, security staff, and operations staff
Replacement Complexity: 25+ years
Defensibility: Controlled infrastructure, governance, and delivery model

Private Service Asset Transfer Buyer retains 100% ownership of assigned deliverables Model weights ownership transfer Dataset ownership agreement Private training environment

Parameter Architecture

120B+ Parameters
Large Dense Architecture
Large MoE Systems
Model family and specialist-model roadmap
Long-horizon scaling plan

Architecture Systems

Native Foundation Ecosystem
Agent Framework
Distributed Runtime
Large-scale Distributed Inference
Compiler and kernel optimization path
Policy-aware orchestration layer
Tensor-parallel serving mesh
Kernel fusion and FlashAttention path

Token Scale

Large-scale Training Infrastructure
Multi-experiment training program
Continuous corpus refresh governance
Evaluation holdout protection

Benchmarks

MMLU
GPQA
BBH
BIG-Bench
SWE-Bench
HumanEval
Reliability Benchmark
Long Context Benchmark
Multi-agent Evaluation
Adversarial Benchmark
Runtime Security Benchmark
Domain and policy evaluation board

Reliability

Multi-Region Failover Systems
Runtime Resilience
Distributed Recovery
Fleet SLO and error-budget governance
Capacity reserve and incident drills

IP Layer

Native Runtime Ownership
Native Training Ownership
Native Evaluation Ownership
Distributed Systems Ownership
Orchestration Ownership
Artifact custody and transfer ledger

Security & Governance

Model supply-chain governance
Access, key, and secret control
Red-team and policy review board
Incident response and release freeze process
Regulated source handling
Supply-chain attestation
mTLS service boundary and key rotation

Engineering Expansion

Foundation Research Engineer
Kernel Optimization Engineer
CUDA Engineer
CUDA Kernel Engineer
CUDA Performance Engineer
Compiler Engineer
HPC Specialist
Runtime Systems Engineer
Distributed Systems Engineer
AI Scientist
Domain Scientist
PhD-Level Research Advisor
Professor-Level Domain Reviewer
Infrastructure Architect

Program Components

Large foundation-model company operating model for research, data, training, evaluation, runtime, and governance
Corpus acquisition, licensing, data estate governance, source authority, and exclusion controls
Large distributed training program with experiment portfolio, checkpoint fabric, and reproducibility evidence
Evaluation science program covering benchmarks, policy review, safety, coding, tool use, and domain capability
Kernel, compiler, runtime, and distributed inference optimization workstreams
Security operations, red-team board, model supply-chain review, and incident response process
Domain scientist review, PhD-level research advisory, and professor-level domain review
Assigned transfer of model family assets, runtime systems, evaluation assets, release evidence, and governance records
Multi-year operating support for releases, capacity planning, security reassessment, and operator transition

Infrastructure Scale

Dedicated GPU capacity plan for training, ablation, tuning, evaluation, inference, and reserve capacity
Exabyte to multi-exabyte corpus, checkpoint, artifact, and evidence storage architecture
Multi-region research, inference, recovery, and controlled-release estate
Dedicated foundation-model research, runtime, evaluation, safety, and security organization
Private sovereign deployment capability with operational transition path
Multi-region reliability, safety, incident, and capacity governance operations

Assigned Ownership Position

Buyer retains 100% ownership of assigned deliverables
Model weights, tokenizer, checkpoint, registry, and runtime configuration transfer
Dataset ownership agreement, corpus-rights ledger, and regulated source handling record
Private training environment with client-governed access and artifact custody controls
Architecture blueprints, system cards, model cards, release evidence, security records, and runbooks
Contracted transfer of agreed research, evaluation, runtime, and governance assets

Cost Basis: Rp49-50T

The amount corresponds to a large foundation-model organization: long-horizon research, sustained experiment capacity, specialized kernels and compilers, evaluation science, multi-region runtime operations, security governance, and assigned asset transfer.

Program Budget

USD 3.06B - 3.12B (Rp49T - 50T)

Request Proposal Technical Consultation

Capability Maturity Dimensions

Foundation maturity is determined by parameter scale, token scale, benchmark performance, dataset capability, deployment capability, reliability engineering, security engineering, runtime systems, distributed systems capability, infrastructure ownership, research capability, architecture systems, evaluation systems, governance capability, operational capability, and long-term strategic defensibility.