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
One-off add-on: AI Agent OpSec Review — prompt-injection surface, tool permissions, memory and data-flow audit, scored findings
Documentation and handover so your team owns the system

How it works

1
Scoping call — we map workflows, data sensitivity, and success criteria
2
Architecture + prototype — a working vertical slice on your real data
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 customize models on your own data and to your enterprise requirements — fine-tuning, inference optimization, provider selection, and MLOps — and we hand you the weights.

What you get

Models customized on your data — fine-tuned on your documents, tickets, code, or transcripts — weights delivered to you
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
Inference cost audit: token-level cost breakdown of your current stack vs. API, open-weight, and hybrid options
Sovereign deployment path: local-first architecture, hardware sizing, and guided execution — you own the infrastructure end to end
Post-training ops: eval regression checks and quarterly re-tunes as your data grows

How it works

1
Audit — current stack, costs, latency, and quality baselines
2
Plan — target architecture with measured trade-offs
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, scored against our own ops review checklist
Quarterly architecture review with a written roadmap

How it works