Anuvia · Practices · 03
Applied AI built as engineering, not as a demo.
Strategy sprints, RAG and agent builds on AWS Bedrock / LangGraph / pgvector, SageMaker-based MLOps, and verticalized products. Every system ships with an eval harness against a held-out set, OpenTelemetry-based observability across the agent graph, documented rollback procedure and a cost ceiling per tenant. PoVs that fail eval gates do not graduate.
The 3 pillars
01
Production gates, not vibes
Discovery → PoV → Build → Operate. Each gate has a written checklist: eval harness vs. held-out set with thresholds, rollback procedure tested, OpenTelemetry spans verified across the agent graph, cost cap enforced at the gateway. A PoV that misses eval thresholds returns a no-go report — not a softer threshold.
02
Cost logged per inference, justified in the ADR
Inference cost is logged at request level and aggregated per tenant, feature and model. AWS Bedrock, SageMaker JumpStart, Vertex AI, or self-hosted vLLM — each choice is justified in the Architecture Decision Record with TCO over 12 months and a fallback plan if the model is deprecated.
03
Dogfooded on the stack we sell
Anuvia's outbound, qualification, scheduling, follow-up and admin run on the agent stack we ship as Anuvia AI Ops. When we describe how LangGraph behaves under retry storms or how a guardrail handles edge cases, that comes from operating it on our own pipeline — not from a vendor slide.
Full catalog
Strategy
Use case inventory, estimated ROI, 12-month roadmap. For teams that know they need AI but don't know where to start.
Engineering
Build of 1 agent or AI pipeline in production. Spec → implementation → integration → handover.
Engineering
Multi-agent stack or full AI platform. Vector DB, RAG, agents, orchestration, evaluation, monitoring.
GenAI
Internal knowledge assistant with per-document access control, citations in every response, hallucination eval harness.
MLOps
Feature pipelines, model registries, CI/CD for ML, drift monitoring, governance. Foundation for reliable AI at scale.
Strategic
Fractional AI Engineer for the team. Strategic guidance + ad-hoc engineering. 6-month minimum.
Product
Multi-tenant SaaS. Agent stack that runs your ops (sales, customer success, content). The same stack we run at Anuvia.
Vertical Product
Vertical product for pharma/biotech. In design partner stage — 3-5 spots for co-founding clients.
Vertical Product
Vertical product for healthcare. In design partner stage — 3-5 spots for co-founding clients.
Who it's for
Company profile
Decision maker
Typical pain
Start with the AI Readiness Sprint — we map where AI actually moves ROI in your operation. No hype, no demo.