Solution landings present the architectural shape of AI deliverables — RAG systems, agentic workflows, AI architecture audits, document intelligence, LLM fine-tuning programs, governance frameworks, and observability platforms. Each landing translates a vendor-agnostic AI capability into an engagement-ready scope with explicit deliverables, KPIs, and risk profile.

Solution-focused engagements typically follow a discovery → design → pilot → production rollout cadence. Discovery surfaces use-case fit (6–10 candidate use cases scored on ROI, risk, feasibility). Design produces a reference architecture (LLM choice, vector DB, retrieval strategy, guardrails, monitoring stack). Pilot validates with a 6–10 week scoped delivery. Production rollout completes the loop with team capability ramp.

Vendor neutrality is intentional — OpenAI, Anthropic, Google, open-source (Llama, Mistral, Qwen), and self-hosted options are weighed against your constraints. Deliverables are working reference architecture + documentation, not deck-only output. Cost model and latency targets are fixed up front; governance is treated as day-one infrastructure, not a later add-on.