D-04 · Private AI
Inference that does not leave the premises.
When the workload is regulated, the weights matter less than the route they travel. Subnet345 designs and operates private inference fabrics: self-hosted, industry-standard, and observability-driven, for organizations that cannot send their inputs to someone else's GPU.
Public inference APIs resolved the demo problem. They did not resolve the enterprise problem. Regulated organizations cannot route prompts, completions, or fine-tuning datasets through a tenant they do not govern.
Every prompt is a data-exfiltration event disguised as a product call. Every fine-tune is a training-set disclosure. Every retrieval-augmented pipeline is a cleartext index of your corpus, persisting somewhere you have not audited. The public-API pattern was engineered for the open web. It was never engineered for the regulated enterprise.
Open-weight model quality has closed the capability gap for most production tasks. What remains is an infrastructure problem: running inference at enterprise scale on hardware you control, under a network posture you own, with observability that satisfies your auditors.
Subnet345's private-AI practice is built on that problem. We design, deploy, and operate inference fabrics whose entire lifecycle remains inside your boundary: prompt, completion, adapter, and log.
§ Capability
What a private-AI engagement delivers.
Cap I
Private inference fabric
Multi-node GPU fabric serving open-weight models behind industry-standard endpoints. Open-source inference runtime with quantized and adapter-based serving, hot-loaded adapter strategies, and workload-aware placement across datacenter and prosumer GPU tiers.
- · Self-hosted, private-network transport
- · Sovereign-region delivery on request
- · Inference routing and rate discipline
Cap II
Fine-tuning and distillation workbench
Supervised fine-tuning pipelines, deterministic and model-assisted dataset correction, parameter-efficient adapter adaptation, and distillation flows that compress production behavior into lower-cost serving tiers.
- · Ingestion of agent decision logs
- · Dataset curation and SFT export
- · Adapter iteration gated by evaluation
Cap III
Agent orchestration
Decision-loop architectures for autonomous agents: tiered memory models, coherence signaling, behavioral validation harnesses, and test-backed agent frameworks. Production patterns drawn from internal platforms running at scale.
- · Tiered memory and state management
- · Decision-loop orchestration
- · Behavioral test coverage
Cap IV
Observability-driven operations
Inference is a distributed system and requires the same operational posture as one. Distributed tracing, metrics collection, dashboards, and request-level introspection. All wired into SLOs agreed at scoping, not discovered after launch.
- · Latency and throughput SLOs
- · Adapter and fabric health telemetry
- · Per-tenant cost and attribution
§ Capability surface
Operator-grade technology posture.
Each line below is an operator-level competency, not a vendor handshake. The posture stays the same when the underlying tooling cycles.
Inference runtime
Hardware
Fine-tuning
Agent and retrieval
Data services
Observability
Host and transport
§ Engagement
How a private-AI engagement unfolds.
Same method cadence as every Subnet345 engagement, applied to the specific physics of inference infrastructure.
01 / Start
What judgment does this inference serve? Which users, which latency budget, which regulatory boundary? Before architecture, the commercial objective.
02 / Immerse
Current AI posture, data-residency constraints, threat model, audit history. Performed inside the environment, not from a deck.
03 / Map
Runtime, adapter strategy, transport, observability plan, SLOs, exit conditions. Every architectural decision written before the statement of work is signed.
04 / Prove
Bounded deployment under production-grade load, with a disproof attempt gated against every phase. We commit to scale only after the pilot survives honest attempts to break it.
05 / Launch
Production fabric deployment with seniors at the keyboard. Telemetry wired to SLOs. Runbooks live from day one.
06 / Evolve
Documentation, role-based training, measured competency gates, adapter-iteration discipline. You operate the fabric after we leave.
See the full method on the principles page →
§ Proof
What stands behind the work.
Practitioner lineage
Private-AI practice led by a principal whose career spans enterprise security product engineering, hyperscaler datacenter consulting, global transformation consulting serving enterprise, military, and government programs, and independent AI platform development. Named U.S. patent holder.
Internal reference platform
A private, unreleased AI platform serves as the reference architecture for client engagements: multi-service backend, tiered-memory agent systems, decision-loop orchestration, and a behavioral-validation suite exercising hundreds of test cases under production-style load.
Open-source contributions
Founding practitioners are contributors to open-source security research tooling and an open-source intelligence platform. Production code, reviewed by external maintainers, spanning infrastructure, data, and AI-adjacent services.
Posture
U.S.-based operations. Sovereign-region delivery on request for self-hosted and private-inference workloads. Enterprise-regulated compliance baseline: SOC 2, HIPAA, GDPR. Industry-standard architecture frameworks applied to design.
Private AI sits on infrastructure. Infrastructure sits under a security posture. We run both.
Engage the private-AI practice