Unbreakable Production Systems for Ambitious SaaS
CTO-led engineering to stabilize, secure, and scale your platform — even under rapid growth and real-world traffic.
We don’t build MVPs. We fix what breaks when your product becomes critical.
Founder-led. You speak directly with CTO Jeremy Jacquemus — this isn’t delegated.
From AI MVP to Production-Grade Systems
Many teams now start with AI-generated code, no-code tools, or LLM-powered prototypes.
That speed is useful — until production reality hits. Then it fails in predictable ways.
What we see repeatedly:
- No security boundaries
- No observability or alerting
- Unpredictable latency and costs
- Code that works once, not reliably
- Systems impossible to maintain under load
What Neyto does:
We take AI-generated or experimental systems and turn them into secure, scalable, production-ready architectures — with proper testing, monitoring, deployment pipelines, and operational discipline.
AI is a tool. Production reliability is the product.
Measured Outcomes
Anonymized results delivered across recent engagements.
99.99%
Uptime
Stability restored under real production traffic.
~2s → ~120ms
Latency under load
Performance improvements that users and SLAs feel.
~45%
Cloud cost reduction
Waste removed and spend made predictable.
Hours → minutes
Deployments
Safer releases with hardened pipelines.
+40%
Team velocity
Less firefighting, more shipping.
Production Incident Proof
Production Rescue — B2B SaaS (Post-Seed)
Prevented a production outage under real traffic pressure, then hardened the system so it couldn’t happen again.
During a traffic increase from ~3k to ~25k concurrent users, the platform started failing due to database contention and unsafe deployments.
What we changed
- Redesigned the write path to remove contention hot spots
- Introduced proper connection pooling and safer DB access patterns
- Deployed a zero-downtime CI/CD pipeline with rollback discipline
Result
- 0 downtime during peak traffic
- Latency reduced from ~1.8s → ~140ms
- Incident risk eliminated before public launch
Real Case Studies
Anonymized. Metric-driven. Production outcomes.
SaaS Platform Stability Rescue
Context
B2B SaaS platform, post-seed, rapid user growth.
Problem
- API latency >2s under load
- Frequent production incidents
- Manual deployments
- No real monitoring
What We Did
- Re-architected core services in Golang
- Introduced proper CI/CD with zero-downtime deployments
- Added full observability (metrics, logs, alerts)
- Optimized database access patterns
Results
- Latency reduced from ~2s → ~120ms
- Uptime improved to 99.99%
- Deployments reduced from hours → minutes
- Engineering team velocity +40%
Timeframe
8 weeks
Focus
Reliability, CI/CD, observability, performance
Cloud Cost & Scalability Audit
Context
API-first product with unpredictable cloud bills.
Problem
- Cloud costs growing faster than revenue
- No cost visibility
- Over-provisioned infrastructure
What We Did
- Full infrastructure audit
- Load analysis and right-sizing
- Auto-scaling strategy
- Cost monitoring & alerts
Results
- Cloud costs reduced by ~45%
- Predictable monthly spend
- System capable of handling 10× traffic without re-architecture
Focus
Cost control, scaling headroom, operational visibility
Productionizing an AI-Generated MVP
Context
Founder-built AI MVP using generated code.
Problem
- No security model
- No testing
- No deployment safety
- Impossible to onboard engineers
What We Did
- Refactored architecture for maintainability
- Added authentication, authorization, and secrets management
- Introduced CI/CD, monitoring, and failure handling
Results
- Production-ready system in 6 weeks
- Safe onboarding of new engineers
- Foundation ready for compliance and scale
Timeframe
6 weeks
Focus
Security, CI/CD, maintainability, operability
CTO-Led by Design
Neyto is led by Jeremy Jacquemus, CTO with over 20 years building and operating production systems across startups and enterprises.
Founder-led engagement. You work directly with Jeremy — he’s accountable for outcomes.
Experience includes:
- High-traffic SaaS platforms
- Enterprise environments (Airbus, Orange)
- Cloud-native architectures
- Security-sensitive systems
This isn’t theoretical advice — it’s battle-tested engineering.
What We Do
Production-grade engineering for SaaS platforms that must survive real-world traffic at scale
CTO-as-a-Service
Technical strategy, architecture decisions, team structure, and delivery governance. Executive-level engineering leadership without the full-time executive cost.
Production System Audits
Architecture review, security analysis, scalability bottlenecks, and cloud cost inefficiencies. Identify what breaks before it breaks.
MVP → Production Transformation
Refactoring, observability, CI/CD, disaster recovery, and load testing. Turn demo-ready prototypes into systems that handle real traffic and survive failures.
Infrastructure & DevOps at Scale
Kubernetes, cloud architecture, monitoring, and zero-downtime deployments. Enterprise-grade reliability for high-traffic systems.
Who Neyto Is For
Neyto is built for you if:
- You have a live SaaS or API with real users
- Your infrastructure is starting to show cracks
- Scaling makes you nervous
- Downtime, security, or cloud costs are becoming business risks
Neyto is not a fit if:
- You’re looking for a cheap MVP
- You want fast features without long-term responsibility
- Price is your primary decision factor
Serious products require serious systems.
Why CTO-Level Decisions Matter
Early architectural choices compound over time—for better or worse
In the first months of a startup, every hour counts. It's tempting to take shortcuts: skip the database indexes, hardcode configurations, deploy without monitoring, postpone security hardening. Most founders assume they'll 'fix it later' when they have more resources.
Here's what I've learned after 20+ years building production systems: architectural decisions made in the first 6-12 months determine whether your platform can scale to handle real traffic, or whether you'll spend the next two years rewriting it while competitors move faster.
The cost difference between doing it right from the start versus fixing it later isn't 2x or 3x—it's often 10x to 20x in time, money, and opportunity cost. Worse, some decisions create technical debt so deep that the only solution is a complete rewrite, which most startups don't survive.
Three Decisions That Destroy Startups
Skipping Database Design
The Bad Decision
A SaaS founder launched with a MongoDB schema treating it like a JSON dump. No indexes, no query optimization, deeply nested documents with arrays of arrays. It worked fine for the first 100 customers.
What It Cost Them
At 2,000 customers, queries took 8-15 seconds. Customers churned before onboarding finished. The schema was so broken that adding indexes wouldn't fix it—they needed a complete data model redesign and migration with zero downtime. Cost: 6 months of development, $180K in engineering costs, and lost runway that killed their Series A.
Ignoring Security from Day One
The Bad Decision
An early-stage B2B platform stored API keys in plaintext, had no rate limiting, used MD5 for passwords, and committed .env files to public GitHub repos. The founders planned to 'harden it before enterprise customers.'
What It Cost Them
A security researcher found the exposed credentials and reported it publicly before they could respond. Three signed enterprise contracts ($450K ARR) were immediately canceled. Their insurance didn't cover the breach because gross negligence voided the policy. The company shut down 4 months later.
No Observability or Monitoring
The Bad Decision
A fintech startup shipped their MVP with no logging, no error tracking, no performance monitoring, and no alerting. When things broke, they'd hear about it from angry customers. Debugging meant adding console.log statements and redeploying.
What It Cost Them
A payment processing bug went undetected for 11 days, causing $340K in failed transactions and duplicate charges. They only discovered it when customers mass-reported their credit cards as fraud. The cleanup took 4 months, cost them their payment processor relationship, and required migrating to a new processor with 6 months of reserve holdbacks.
The Pattern Is Always the Same
These aren't edge cases—they're the most common failure modes I see across dozens of startups. The founders who survive are the ones who treated infrastructure, security, and observability as first-class concerns from day one, not nice-to-haves for later. CTO-level architectural decisions aren't expensive—they're what keeps your startup from becoming another statistic.
Before / After
The transformation from fragile MVP to production-grade system
System Stability
Before
- 15-20 min outages during traffic spikes
- Manual restarts when services crash
- Production fires discovered from customer complaints
- No automated failover or recovery
After
- 99.99% uptime with automated health checks
- Self-healing infrastructure with Kubernetes
- Proactive alerting before users are impacted
- Zero-downtime deployments and rollbacks
Performance at Scale
Before
- API response times degrade under load
- Database queries timeout during peak hours
- No caching, every request hits the database
- Single region, no CDN or edge optimization
After
- p95 latency under 100ms at 10x traffic
- Redis caching for read-heavy operations
- Optimized queries with proper indexing
- Multi-region CDN with edge caching
Security & Compliance
Before
- Missing auth on internal admin endpoints
- Dependencies with known CVEs
- Secrets hardcoded or in environment files
- No audit logs or access control
After
- Role-based access control (RBAC) enforced
- Automated vulnerability scanning in CI/CD
- Secrets managed in encrypted vaults
- Complete audit trail for compliance
What Breaks at Scale
At scale, systems don’t fail randomly — they will fail predictably.
If you're seeing any of this, the failure is already scheduled.
Databases under real concurrency
Locks, slow queries, and exhausted pools appear the moment traffic becomes real.
CI/CD pipelines that can’t deploy safely
Flaky tests, risky releases, and manual rollbacks turn shipping into a gamble.
Missing observability when incidents happen
Without tracing, logs, and alerts, incidents become guesswork under pressure.
Security gaps exposed by growth
As the attack surface expands, shortcuts become breach and compliance risks.
Cloud costs growing faster than revenue
Over-provisioning and inefficiency quietly increase burn every month.
Teams slowed down by firefighting
Engineers spend their time reacting to incidents instead of building.
These are CTO-level problems, not development tasks.
What Happens If You Don’t Act
Technical debt compounds silently.
- Outages appear during peak usage
- Security issues surface when it’s too late
- Cloud costs spiral unpredictably
- Teams slow down due to firefighting
- Rewrites cost 3–5× more than early fixes
The most expensive system is the one you fix too late.
Why Trust Neyto
Authority built through decades of production systems engineering—not marketing promises
20+ Years Production Systems Engineering
CTO-level expertise spanning enterprise infrastructure and startup scaling. Systems architected to handle millions of concurrent users, billions of daily requests, and mission-critical financial transactions. Experience earned debugging production failures at 3 AM, not reading blog posts.
Enterprise & Startup Proven
Built infrastructure for Fortune 500 companies processing billions in transactions. Scaled startups from prototype to Series B, handling 100x traffic growth without architectural rewrites. We understand both worlds—enterprise reliability requirements and startup velocity constraints.
Full Ownership, Zero Excuses
When we build a system, we own its reliability. No finger-pointing at infrastructure, no blaming 'the previous team', no excuses when things break. We architect for failure, plan for the worst-case scenario, and take full responsibility for uptime and data integrity.
Calm Engineering Under Pressure
Production crises require clear thinking, not panic. Our approach: systematic debugging, root cause analysis, transparent communication with stakeholders. We document what broke, why it broke, and what we changed to prevent recurrence. No drama, no heroics—just disciplined engineering.
Why Neyto
Production systems expertise earned through 20+ years of building and fixing mission-critical infrastructure
20+ Years Production Experience
Ex-CTO with enterprise and startup background. We've architected and scaled systems handling millions of concurrent users, billions of requests, and mission-critical transactions.
Reliability Over Hype
We don't chase trends or buzzwords. We build architectures proven to survive traffic spikes, handle cascading failures gracefully, and scale horizontally without rewrites.
Full Accountability
We own outcomes, not deliverables. When we declare a system production-ready, we mean it will survive real-world traffic and failures. Our reputation depends on it.
CTO-Level Systems Architecture
Deep expertise in Kubernetes orchestration, Go/Node.js services, AWS/GCP infrastructure, CI/CD automation, security hardening, and performance optimization under load.
Use Cases
Production systems engineering for platforms that must scale reliably under real-world traffic
SaaS Scaling
Transform your growing SaaS into a production-grade system that handles traffic spikes, maintains 99.9%+ uptime, and scales horizontally without architectural rewrites.
Technical Debt Rescue
Refactor unstable MVPs into maintainable, well-architected systems. Eliminate performance bottlenecks, improve observability, and prepare for 10x growth.
Security & Compliance
SOC 2, ISO 27001, GDPR, and enterprise security requirements. Harden infrastructure, implement proper authentication and encryption, pass audits on first attempt.
Cloud & Infrastructure
Kubernetes orchestration, multi-region failover, disaster recovery automation, and cloud cost optimization for production-critical systems.
Performance Engineering
Database query optimization, intelligent caching strategies, load balancing, and distributed tracing for high-traffic, mission-critical APIs.
AI Prototype → Production-Ready System
Transform AI-generated MVPs into reliable, production-grade platforms. We take your AI prototype (ChatGPT-generated code, hastily built POC, or LLM-accelerated MVP) and rebuild it with proper error handling, security controls, optimized queries, comprehensive observability, and cost management. Production-first engineering that ensures your AI-built system survives scale.
How Engagements Work
Engagements designed to reduce risk and increase production confidence.