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.

Engagements

Choose your entry point.

01

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.

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
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. 01
    Scoping call
    We map workflows, data sensitivity, and success criteria.
  2. 02
    Architecture + prototype
    A working vertical slice on your real data.
  3. 03
    Ship + harden
    Production deployment, evals, OpSec review, handover.
02

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.

For: Teams hitting cost, latency, or privacy walls; ML teams moving from API-only to owned inference.

Discuss your model needs
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. 01
    Audit
    Current stack, costs, latency, and quality baselines.
  2. 02
    Plan
    Target architecture with measured trade-offs.
  3. 03
    Execute
    Migration, tuning, and monitoring in production.
03

AI Engineer Retainer

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

For: Teams running AI systems in production without a dedicated AI engineer.

View retainer options Upskill instead: Forge
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. 01
    Onboarding
    Deep-dive into your existing systems and priorities.
  2. 02
    Cadence
    Monthly hours, async requests, shared backlog.
  3. 03
    Compound
    Each month builds on documented system knowledge.
From the library

Field-tested, written down.

All posts
Deep DiveJuly 23, 20268 min

OpenWorker Architecture: Andrew Ng's Open-Source AI Coworker

Andrew Ng just released OpenWorker: an open-source desktop AI agent that delivers finished work, not just chat.

Deep DiveJuly 8, 20263 min

Tencent Hy3: 295B MoE That Punches at Trillion-Scale

A 295B MoE model with 21B active parameters, Apache 2.0 licensing, 256K context, and agent-first design.

Deep DiveJuly 6, 20264 min

FAST-AR: NVIDIA Cracks the Video Diffusion Bottleneck

NVIDIA Research framework that eliminates the KV-cache bottleneck in autoregressive video diffusion.

Work With Us

Ready to own your AI stack?

A free 30-minute scoping call. We map your workflows, data sensitivity, and success criteria before any commitment.

Book a call Upskill with Forge