Generative AI Consulting

Generative AI consulting focuses specifically on large language models, vector retrieval, agentic systems, and the orchestration layer (the "harness") that wraps them in production. Washington DC clients typically need help moving past pilots into governed deployments — including guardrails, evaluation harnesses, and integration with existing data systems. Generic AI consulting often stops at strategy decks; we ship working systems.

Not currently. We deliver to Washington DC on a remote-first basis from our nearest offices in Sydney, Melbourne, and Auckland — with time-zone overlap and synchronous delivery cadence. On-site visits are arranged on request.

Discovery engagements typically run 2–4 weeks and start in the low five figures. Implementation engagements (proof of concept through to production handover) typically run 8–16 weeks; cost depends on data volume, integration scope, and whether self-hosted models are required. We provide fixed-fee proposals after a no-obligation 30-minute scoping call.

A focused PoC for a single use case (e.g. document intelligence over a defined corpus, or an internal copilot for a specific workflow) typically takes 4–6 weeks from kickoff to a working demo. The bottleneck is rarely the model — it's usually data access, evaluation criteria, and stakeholder alignment.

We are model-agnostic. In production we deploy Anthropic Claude (Opus, Sonnet, Haiku), OpenAI GPT, Google Gemini, and self-hosted open-weights models (Llama, Mistral, Qwen, DeepSeek) when data residency or cost requires them. Selection is driven by the task — coding agents, structured extraction, long-context analysis, and creative generation all favour different models.

US client data residency is configured per state requirements (CCPA, NYDFS, HIPAA where applicable) and cloud regions are chosen to align — typically US-East / US-West / US-Gov where required. Sectoral controls (HIPAA, SOX, FedRAMP) are wired in at architecture time.