Enterprise AI Engineering, Ready for Production
Your infrastructure. Your models. Your engineering standards.
HanahTechnology helps software and product engineering organizations deploy private and cloud-based AI infrastructure, integrate intelligent development agents, and establish secure, repeatable AI-powered engineering workflows.
From NVIDIA and AMD GPU systems to Amazon Bedrock, agent orchestration, automated code review, testing, engineering governance, we provide end-to-end implementation, enablement and operational support.
Service modules
AI infrastructure
Workstation/server and GPU sizing; NVIDIA/AMD compatibility; containers; network isolation; access controls; monitoring.
Model deployment
Local inference (e.g., Ollama/vLLM); Amazon Bedrock integration; approved model registry; benchmark and routing policy.
Agent orchestration
Framework assessment (Hermes Agent, LangGraph, OpenAI Agents SDK, etc.); MCP tools; workflow state; permissions; recovery.
Engineering methodology
AGENTS.md / CLAUDE.md conventions; architecture and standards documents; reusable skills, prompts, checklists.
Review & validation
Independent code review; tests; static/security scanning; CI integration; evaluation harness; human approval gates.
Enterprise rollout
Pilot, developer enablement, adoption metrics, operational documentation, ongoing support and governance.
Platform Architecture
HanahTechnology integrates interchangeable models, runtimes and deployment options into a customer-controlled engineering workflow. Hardware, model and framework choices remain replaceable, so the platform can adopt new models, runtimes and hosting options without rebuilding the engineering workflow.
Engineering teams work through a single AI engineering workbench. Behind it, orchestration, review and governance control what agents can do, what they can change, and what must be approved before anything is deployed. That control layer can run on private infrastructure, in the cloud, or as a hybrid, and it connects to the organization’s existing engineering systems.
Compute & serving
The platform runs on GPU capacity wherever it makes sense for the workload: private or local NVIDIA RTX/PRO and AMD (ROCm) GPUs and local servers, or cloud GPU capacity such as Amazon EC2 G5/G6 instances, AWS-hosted or private LLM hosting, and managed infrastructure.
Models
Models are chosen per workload: local models served with Ollama or vLLM, including private GPT, Llama and other models, or managed models through Amazon Bedrock — Claude, GPT, Llama and Titan — and Gemini where needed.
Agent runtime
Development agents run locally or in managed runtimes: Claude Code agents, Hermes Agent and LangGraph on the private side; LangGraph, the OpenAI Agents SDK and managed Hermes Agent runtimes in the cloud.
Integrations
Agents connect to the tools engineers already use: repository (Git) integrations, local MCP servers and CLI tools; or AWS services and API integrations, SaaS integrations and managed hooks.
Control plane
Delivery and operations are automated with Docker/Kubernetes and local CI/CD pipelines, or with AWS, GitHub Actions, GitLab CI and managed job orchestration.
Tooling is chosen per engagement, not assumed. The choice is structured from workload, privacy, uptime, licensing, developer experience and total cost of ownership — not every customer needs a local GPU or Hermes Agent.
Commercial packages
Readiness & architecture assessment — 1–2 weeks — $3,000–$7,000
Assessment, target architecture, roadmap and pilot definition.
AI Engineering Starter — 3–5 weeks — $10,000–$20,000
Single environment, repo standards, agents, training.
AI Engineering Platform — 6–10 weeks — $25,000–$60,000
Multi-model setup, orchestration, integrations, review/evaluation.
Enterprise rollout — 3–6+ months — $75,000+
Scaled governance, integration, migration and training.
Managed AI operations — Monthly — $2,000–$8,000/month
Monitoring, maintenance, evaluation and improvements.
Fees are indicative starting points. GPU purchases, licenses and major application development are excluded unless explicitly included in the statement of work.