Sulaco.AI™
ENTERPRISE-GRADE AI DELIVERY

Anyone Can Build a Demo.
We Build What Ships.

AI tools have lowered the barrier to entry for prototypes. They haven't lowered the bar for what enterprise deployment actually requires — and the gap between the two is where most AI projects fail.

"AI tools are like power saws. They don't make you a master carpenter — they just let skilled carpenters work faster."

THE GAP

Prototype vs. Production

The things that make an AI impressive in a demo are not the things that make it safe, reliable, and scalable in your business. Here's what changes when the stakes are real.

Prototype / Demo

API key hardcoded in the repo
Manual deploy via git push
Works on one machine
Single environment
No error handling on AI calls
"I tested it myself"
No audit trail
Downtime = restart the process

Enterprise Production

Secrets management with automatic rotation
CI/CD pipeline with automated test gates
Multi-region, load-tested, pen-tested
Dev → staging → production parity
Retry logic, fallbacks, and circuit breakers
Automated test suite required to merge
Compliance-ready logs for every action
Auto-recovery with zero-downtime deployments

A vendor who built their system in three weeks with an AI chatbot cannot give you the right column.

WHAT ENTERPRISE AI ACTUALLY REQUIRES

Six Domains That Separate Engineers from Experimenters

Each of these takes years to learn and months to implement correctly. Missing any one of them is a production incident waiting to happen.

Cloud Infrastructure

Common gap: Single-region, single-instance, no scaling policy — works until it doesn't.

Multi-AZ deployment with automatic failover
Auto-scaling groups with defined capacity thresholds
Disaster recovery with documented RTO and RPO
Cost optimization — reserved instances, spot strategies, rightsizing
Network segmentation and VPC architecture

Technologies

AWS · GCP · Azure · EKS · ECS · Terraform

CI/CD Pipelines

Common gap: Manual deploys — or worse, a git push straight to production.

Automated test gates: unit, integration, and end-to-end
Blue-green and canary deployments with traffic shifting
Automated rollback triggered by error rate thresholds
Environment promotion gates with required approvals
Deployment audit trail for every change, every environment

Technologies

GitHub Actions · ArgoCD · AWS CodePipeline · Spinnaker

Infrastructure as Code

Common gap: Manually clicking through the AWS console — state lives only in someone's head.

All infrastructure version-controlled and peer-reviewed
Drift detection — alerts when live state diverges from declared state
Automated compliance checks before every apply
Immutable infrastructure — no snowflake servers
Secrets and config managed separately from code

Technologies

Terraform · AWS CDK · Pulumi · Checkov · Terragrunt

AI Agent Architecture

Common gap: One API call to a language model with no error handling, no output validation, no cost controls.

Multi-agent orchestration with defined failure modes
Human-in-the-loop checkpoints for high-stakes decisions
Agent output validation before downstream consumption
Token budget controls and rate limiting per workflow
Full audit log of every agent decision and tool call

Technologies

LangGraph · Anthropic API · Custom orchestration · OpenTelemetry

Security

Common gap: API keys in environment variables, no access controls, no audit trail.

Zero-trust architecture — no implicit trust between services
IAM least-privilege: every service can only touch what it needs
Secrets rotation with Vault or AWS Secrets Manager
AI input sanitization and output filtering to prevent injection
Penetration testing and compliance scoping (SOC2, HIPAA, GDPR)

Technologies

AWS IAM · HashiCorp Vault · AWS WAF · Snyk · Pen testing

Observability

Common gap: "I'll check if it's working by looking at it." — No alerting, no tracing, no SLAs.

Distributed tracing across every agent call and service boundary
Custom dashboards tracking business KPIs, not just uptime
SLO tracking with error budgets and burn rate alerts
On-call runbooks so any engineer can respond at 2am
Post-incident review culture — every outage makes the system stronger

Technologies

Datadog · OpenTelemetry · PagerDuty · Grafana · CloudWatch

DUE DILIGENCE

Questions to Ask Any AI Vendor

Bring these into your next vendor meeting. A vendor who built their system with an AI chatbot in a few weeks cannot answer them. A production engineer can answer every single one.

Deployment & Reliability

What's your rollback strategy if a deployment fails at 2am on a Sunday?

How do you handle a regional cloud outage — what's your RTO and RPO?

Can you walk me through your CI/CD pipeline from commit to production?

How do you test AI outputs before they reach production users?

Security

How do you manage secrets across dev, staging, and production?

What does your IAM policy for production access look like?

Have your systems been pen tested? Can you share the scope and last report date?

How do you prevent prompt injection attacks in your AI agents?

Monitoring & Observability

What does your monitoring alert on — and who gets paged?

How do you track AI agent decision quality and output drift over time?

Show me your incident response runbook for a production AI failure.

What happened during your last production incident and how long did recovery take?

Sulaco™ answers all of these. Not because we prepared for an interview — because we've been building production systems long before AI chatbots made it easy to demo one.

PRODUCTION PROOF

Not Demos. Not Mockups. Live Systems.

These are running in production today — monitored, deployed through pipelines, and used by real businesses with real operational requirements.

LIVE IN PRODUCTION

VoyagePlanner

voyageplanner.sulaco.ai

Multi-agent cruise itinerary platform processing 2,400+ global port records per request — in production for cruise operators.

Multi-agentCI/CDCloud-nativeMonitored
LIVE IN PRODUCTION

Functional Movement Index

fmi.sulaco.ai

AI-powered biomechanical analysis platform for physical therapists — real-time computer vision with clinical-grade reliability requirements.

Computer visionReal-timeHIPAA-awareMonitored
WHY EXPERIENCE MATTERS

The Difference Isn't the Tools. It's the Judgment.

We use the same AI tools as everyone else — Claude, GPT-4, Gemini. The difference is knowing when to use them, where they fail, how to validate their output, and how to build the surrounding system that makes them trustworthy at scale.

That judgment comes from years of building production systems, responding to incidents at 2am, and owning the consequences of architectural decisions — not weeks of experimenting with chatbots.

Years of cloud architecture experience

Not months of prompting

Security-first by default

Not bolted on after the breach

Observability designed in from day one

Not added when things break

CI/CD before the first commit

Not "we'll automate it later"

Ready to Talk to Engineers, Not Experimenters?

Book a Discovery Call with Dr. Will Perez — bring your hardest technical requirements. We'll show you exactly how we'd approach them.