How CTOx AI works
A technical decision engine built for founders, CTOs and engineering teams.
Not generic AI. A personalised architectural partner shaped by decades of engineering leadership.
Built on real systems, shaped by real decisions.
Ask. Analyse. Decide.
You ask a real engineering question. CTOx AI analyses your context and returns a structured, actionable decision, not a generic explanation.
Powered by a deep, curated knowledge base
Hundreds of books, engineering articles, research papers, architectural patterns and real-world systems, distilled into one AI that reasons like an experienced technical leader.
foundational books
engineering patterns
years of leadership
Architecture & distributed systems
Microservices, event-driven design, data modelling and system boundaries
Scaling, SRE & production operations
Reliability engineering, observability, incident response and capacity planning
Product strategy, team structure & delivery
Engineering management, hiring, roadmaps and execution frameworks
This is what makes CTOx AI different from general-purpose AI models.
Industry-specific CTO personas
FinTech, MedTech, SaaS, AI/ML, with more domains continuously added.
Compliance-first architecture for regulated financial systems
How should we handle risk scoring before executing high-value transfers?
Use a multi-factor scoring model combining behavioural signals, device reputation, velocity rules and historical fraud patterns. Keep decisions under 200ms using asynchronous scoring pipelines. Record each scoring event for AML auditability and dispute resolution.
Audit-ready systems for healthcare and medical devices
How do we design audit-ready data flows for a Class II medical device?
Begin with a validated data lifecycle: acquisition, transformation, storage and audit. Map each stage to ISO 13485 and FDA Part 820 requirements. Use signed, immutable logs for clinical decisions. Apply traceability matrices to link functional requirements to test protocols. Ensure your ML models follow controlled re-training workflows with dataset versioning.
Scalable multi-tenant platforms built for growth
How do we introduce multi-tenant isolation without rewriting the whole platform?
Introduce a tenancy boundary at the data layer first. Use row-level security or schema-per-tenant depending on your compliance model. Extract shared compute behind idempotent services. Phase migration using a strangler-fig model to avoid downtime during adoption.
Production-grade machine learning infrastructure
How do we implement safe model updates without breaking production?
Use shadow deployment to compare live inference against the new model. Apply statistical drift detection to validate dataset consistency. Version every model artefact and enforce reversible rollouts. Adopt feature stores to keep inference behaviour aligned with training signals.
CTOx AI learns your product over time
Remembers previous decisions
Builds long-term engineering memory and retains architectural context.
Builds evolving context
Understands constraints, trade-offs and past directions.
Adapts as your architecture grows
Guidance evolves with your product, not static responses.
Becomes your long-term partner
A persistent technical system, not a chatbot.