YottaDynamics
AI Review
Private AI infrastructure
YottaDynamics helps teams design, build, and operate private LLMs, production RAG systems, sovereign AI agents, and Kubernetes-based AI platforms where data control, compliance, reliability, and operational ownership matter.
Infrastructure snapshot
Live
Services
A fixed-scope assessment for teams evaluating private LLMs, production RAG, sovereign agents, or AI workloads on Kubernetes. We identify the data, architecture, security, cost, and operations risks before you commit to a platform path.
Architecture and implementation support for AI systems over private documents, customer data, source code, and internal knowledge where retrieval quality, access control, evaluation, and auditability matter.
Platform architecture for private AI workloads that need model serving, GPU scheduling, observability, security boundaries, cost controls, and a Day-2 operating model your team can own.
Advisory and implementation support for internal coding agents and tool-using automation where source-code privacy, tool permissions, human approval, audit logs, and safe execution boundaries matter.
Process
We work in a defined sequence so every phase turns uncertainty into an artifact your team can use: risk memo, architecture decision, deployment plan, runbook, or production implementation.
A focused conversation about your AI goal, data boundaries, current platform, and where the production risk sits.
We map model, retrieval, agent, Kubernetes, security, observability, cost, and Day-2 operations gaps before implementation starts.
We define the private AI platform shape, data boundaries, service interfaces, and ownership model with documented tradeoffs.
We build or guide the infrastructure, services, RAG pipelines, agent controls, and deployment pieces needed for production.
We leave behind the runbooks, diagrams, decisions, and observability baselines your team needs to own the system.
Focused design review, platform guidance, and production hardening support after the first release.
Example Scenarios
A regulated team needs LLM capabilities without sending protected documents or operational metadata to uncontrolled external systems.
Architecture outcome
Private model-serving path, data boundary map, observability plan, and operational handoff model.
Read scenarioA SaaS team has a working retrieval demo but needs access-aware ingestion, evaluation, fallback behavior, and auditability before launch.
Architecture outcome
Retrieval design, evaluation loop, access-control strategy, deployment plan, and runbooks.
Read scenarioA platform team must support AI workloads with predictable capacity, policy boundaries, observability, and Day-2 operations.
Architecture outcome
Model-serving topology, GPU/capacity plan, GitOps workflow, alerting baseline, and production readiness roadmap.
Read scenarioAbout
YottaDynamics is a specialized engineering practice focused on production AI infrastructure for regulated environments.
The work is led by senior platform engineering experience across Kubernetes-based infrastructure, Linux systems, Go services, production operations, and high-availability environments where security boundaries, auditability, cost control, and Day-2 ownership matter.
Trust is built through technical depth, public artifacts, written architecture thinking, clear process, and concrete deliverables — not founder celebrity. The goal is simple: help your team run private AI systems it can secure, monitor, debug, and own.