Cloud · Engineering · AI · Growth · Industry
Cloud, platform and applied AI consulting for engineering orgs running production workloads above $30k/month in AWS-primary environments. Mid-market and scale-up, Series B onward. Architecture Decision Records, documented rollback procedures and a cost ceiling per workload defined before the first deploy.
Background
AWS · Google
The team brings tenure inside AWS as a Solutions Architect, with prior experience at Google and MongoDB. Architecture decisions on each engagement come from operating workloads at hyperscaler scale — not from whitepapers.
Verifiable depth
15× AWS-certified
Solutions Architect Pro, Security Specialty, ML Specialty, Data Analytics, DevOps Pro, Database, Advanced Networking. Public Credly profile. AWS tracks roughly 200 SAs globally at this certification depth.
Delivery model
Solutions Architect + agent squad
A senior SA owns the architecture, ADRs and gate reviews. An internal agent squad — the same one running Anuvia's own ops — handles documentation, status reports and routine delivery. Human review is mandatory on every architecture decision.
The 5 practices
Each practice has fixed-scope offerings with public pricing and a documented ICP. Most engagements start with a diagnostic — 2 to 4 weeks, defined deliverable, no commitment to follow-on work.
01 · Cloud
FinOps audits, Well-Architected reviews, multi-account landing zones and migration planning. AWS bills above $30k/month routinely contain 25-40% recoverable spend in Reserved Instance coverage, idle EBS, and untuned data transfer paths.
FinOps · AWS Practice · GCP · Multi-Cloud Strategy
02 · Engineering
DORA-based maturity assessments, CI/CD on GitHub Actions or GitLab, EKS migrations with ArgoCD/Flux GitOps, OpenTelemetry-based observability. DORA 2023 puts Elite teams at multi-daily deploys; the median client we audit deploys weekly.
DevOps · SRE · Observability · IoT/Edge
03 · AI
AI readiness sprints, RAG and agent builds on AWS Bedrock / LangGraph / pgvector, MLOps on SageMaker. Every system ships with an eval harness against a held-out set, documented rollback, and cost cap per tenant. PoVs that fail eval gates do not graduate.
Strategy · Engineering · MLOps · Products
04 · Growth
Sales Ops audits, RevOps stack implementation on HubSpot / Salesforce, and Anuvia AI Ops — a multi-tenant agent SaaS handling outbound, qualification and follow-up. The same stack runs Anuvia's own commercial pipeline.
RevOps · MarketingOps · AI Ops SMB
05 · Industry
Vertical playbooks for manufacturing (OEE, predictive maintenance), logistics (offline-first telemetry), healthcare (LGPD-saúde, clinical documentation), Life Sciences (GxP validation packages) and FinServ (BACEN 4.658, AML).
Manufacturing · Logistics · Healthcare · Life Sciences · FinServ
Start here
A 30-minute call with a Solutions Architect. We map the pain to a practice, suggest the diagnostic with the lowest commitment-to-value ratio, and propose a concrete next step. No deck, no follow-on proposal at the end.
Book →
How Anuvia AI is built
Every architecture ships with an Architecture Decision Record, a documented rollback procedure, and a measured cost ceiling per inference. PoVs that fail eval gates do not graduate to production. Three concrete commitments hold the practice to that standard.
01
Production-ready, not demo-ready
Before any deploy: eval harness against a held-out set with documented thresholds, rollback procedure tested at least once, OpenTelemetry spans across the agent graph, cost cap per tenant enforced at the gateway. Discovery → PoV → Build → Operate has explicit gates; PoVs that miss eval thresholds get a no-go, not a workaround.
02
Cost measured per inference, not per quarter
Inference cost is logged at request level and aggregated per tenant, per feature, per model. AWS Bedrock, SageMaker, Vertex AI, or self-hosted vLLM — the choice is justified in the ADR with TCO over 12 months. Account separation, tagging strategy and IAM Service Control Policies inherited from Anuvia Cloud practice.
03
Dogfooded on the same stack we sell
Anuvia's own outbound, qualification, scheduling and follow-up run on the agent stack we ship as Anuvia AI Ops. When we describe how a tool behaves under load or how a guardrail handles edge cases, that comes from operating it on our own pipeline — not from a vendor's slide.
Engagement model
01
Discovery
2-3 weeks. Read-only access to AWS account, CRM, or repos as scope demands. Output: opportunity inventory, ADR for the top 1-3 candidates, go/no-go for PoV.
02
Proof of Value
4-6 weeks. One use case, held-out eval set, success thresholds agreed up front. Gate: if eval is below threshold, engagement does not proceed to Build.
03
Build
6-12 weeks. Production deploy with IaC, OpenTelemetry, rollback procedure, runbook. Gate: load test, security review, cost-cap validation before traffic flips.
04
Operate
Optional retainer or full handover. Eval-set regression run weekly. SLO/SLI defined. Ownership documented — usually transferred to the client's platform team.
Diagnostics
FinOps Audit Risk-Free
AWS Cost Audit in 4 weeks
Identifies 25-40% recoverable spend across Reserved Instance coverage, idle EBS/snapshots, data transfer paths and untagged compute. 3× annualized-savings guarantee documented in the contract — never refunded to date.
US$ 9-12k · 4 weeks →
AWS Well-Architected
Review across the 6 pillars
Structured review against the AWS Well-Architected Framework — Operational Excellence, Security, Reliability, Performance, Cost, Sustainability. Executive report with HRI/MRI/LRI-scored findings and a remediation roadmap sequenced by blast radius.
US$ 6-10k · 3-5 weeks →
DevOps Maturity
DORA assessment + 6-month roadmap
DORA baseline (deploy frequency, lead time, MTTR, change failure rate) measured from your repos and incident log. Gap analysis against DORA 2023 Elite/High thresholds and a roadmap sequenced by effort vs. team capacity.
US$ 7-10k · 4 weeks →
AI Readiness
Strategy Sprint
8-15 candidate use cases scored on data readiness, estimated inference cost, latency budget and applicable compliance (LGPD, GxP, SOC 2). Build-vs-buy call per case. ROI model with explicit assumptions.
US$ 5-8k · 2-3 weeks →
Sales Ops Diagnostic
Funnel audit + 90-day plan
End-to-end funnel map with measured stage conversion, real response-time SLA per channel, and an automation map scored by impact vs. effort. Stack audit across HubSpot, Salesforce, Pipedrive, RD Station.
US$ 3-5k · 2 weeks →
Industry Assessment
Vertical playbook fit
For manufacturing, logistics, healthcare, Life Sciences and FinServ orgs evaluating where AI/automation has real ROI in their vertical. Maps your environment against Anuvia's accumulated vertical playbooks; honest call on cases where ROI is weak without specific pre-conditions.
Free preliminary read · 90 seconds →
A 30-minute call with a Solutions Architect. Bring the workload, the bill, the incident, or the stack you're considering. We'll map it to a practice, propose the lowest-commitment diagnostic that returns useful signal, and leave a one-page summary in your inbox. No proposal at the end of the call.
Pick a slot with a Solutions Architect