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Pillar 01 · AI Engineering

AI Engineering

From generic AI tools to production-grade systems that fit your workflows — designed, shipped, and hardened by engineers who run these systems 24/7 on their own infrastructure.

LOCAL / SIGNAL / CONTROL

Tailored Multi-Agent Systems

We embed with your team, map your real workflows and data, then design and ship custom single or multi-agent systems built around the models that perform best for your use case — not the ones with the best marketing.

What you get
  • Agent architecture designed around your workflows (orchestration, memory, tool use)
  • Model selection benchmarked on your actual tasks — open-weight first when it wins
  • Local-first deployment: your data and keys never leave your infrastructure
  • Guardrails, evaluation harness, and human-in-the-loop controls for high-stakes actions
  • Documentation and handover so your team owns the system
How it works
  1. 1
    Scoping call — we map workflows, data sensitivity, and success criteria
  2. 2
    Architecture + prototype — a working vertical slice on your real data
  3. 3
    Ship + harden — production deployment, evals, OpSec review, handover
For: Teams drowning in manual workflows; founders who need leverage without headcount; organizations that cannot send data to third-party clouds.
Book a scoping call

Inference & Model Engineering

Running AI reliably in production requires more than prompting. We support teams with fine-tuning, inference optimization, provider selection, Hugging Face organization, and MLOps infrastructure.

What you get
  • Model selection & benchmarking across cost, latency, and quality
  • Quantization and serving setup sized to your hardware
  • Fine-tuning pipelines with data engineering and evaluation built in
  • Inference cost audits and on-prem or hybrid deployment plans
How it works
  1. 1
    Audit — current stack, costs, latency, and quality baselines
  2. 2
    Plan — target architecture with measured trade-offs
  3. 3
    Execute — migration, tuning, and monitoring in production
For: Teams hitting cost, latency, or privacy walls; ML teams moving from API-only to owned inference.
Discuss your model needs

AI Engineer Retainer

Direct access to a Delta V AI Engineer (supported by dedicated ZHC subagents) for ongoing optimization, security, and capability expansion.

What you get
  • Reserved monthly engineering hours with same-week turnaround
  • Continuous model and tooling watch as the frontier moves
  • Security reviews of agent permissions, prompts, and data flows
  • Quarterly architecture review with a written roadmap
How it works
  1. 1
    Onboarding — deep-dive into your existing systems and priorities
  2. 2
    Cadence — monthly hours, async requests, shared backlog
  3. 3
    Compound — each month builds on documented system knowledge
For: Teams running AI systems in production without a dedicated AI engineer.
Upskill instead — ForgeView retainer options
Ecosystem & Stack
DeepSeekMoonshotZ.aiMiniMaxClaudeOpenAIGoogle AIHugging FaceLangChainPyTorchComfyUIObsidianOllamaLM StudioOpenCodeHermesOpenRouterCocktail PeanutBuzzMercuryPiherdrNVIDIARaspberry PiBF6DeepSeekMoonshotZ.aiMiniMaxClaudeOpenAIGoogle AIHugging FaceLangChainPyTorchComfyUIObsidianOllamaLM StudioOpenCodeHermesOpenRouterCocktail PeanutBuzzMercuryPiherdrNVIDIARaspberry PiBF6