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Pillar 01 · AI Engineering

AI Engineering

AI tools built for your production systems. Designed and shipped by engineers who run these systems 24/7.

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.

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 wherever it is workable — we favour deployments where 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
Live example · Hydra

Your company's new AI colleague, ready to work.

Mail, calendar, tasks, treasury, invoices — all in one workspace anyone can use in plain English. Processes invoices on arrival, ranks them by urgency, and keeps your team focused on the important deals.

Open the live demo
  • Inbox to invoice
  • Read-only by default
  • Runs on a schedule
Built on mocked company data — your deployment runs on your real systems.
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 customize models on your own data and to your enterprise requirements — fine-tuning, inference optimization, provider selection, Hugging Face organization, and MLOps infrastructure.

What you get
  • Models customized on your data — fine-tuned on your documents, tickets, code, or transcripts
  • Adaptation to enterprise requirements: tone, terminology, compliance boundaries, refusal rules
  • 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 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