Your Systems Determine Your Options
AI tools do not operate in isolation. They need to read data from your existing systems, trigger actions in your workflows, and return results where your team can act on them. A tech stack review maps every integration point, identifies compatibility gaps, and determines whether your infrastructure can support the AI workloads you are considering. This prevents the common failure pattern where teams select an AI platform only to discover their systems cannot feed it the data it needs.
Integration Point Mapping
We catalog every system-to-system connection in your architecture: REST APIs, webhooks, database links, file transfers, and manual data bridges. Each gets assessed for reliability and extensibility.
API Availability Audit
Modern AI integrations depend on API access. We evaluate which of your systems expose APIs, what data is accessible programmatically, and where custom connectors would be required.
Legacy System Compatibility
Mainframes, on-premises ERP, and older databases are not disqualifying. We assess middleware options, ETL pipeline feasibility, and wrapper patterns that bridge legacy systems to modern AI services.
Cloud Readiness Evaluation
We assess your cloud infrastructure maturity across compute, storage, networking, and security. Hybrid architectures, data residency constraints, and egress costs all factor into AI deployment planning.
Review Process
Inventory
Catalog all systems and connections
Test APIs
Probe integration capabilities
Assess Debt
Evaluate technical debt impact
Report
Deliver compatibility matrix
Inventory
Catalog all systems and connections
Test APIs
Probe integration capabilities
Assess Debt
Evaluate technical debt impact
Report
Deliver compatibility matrix
Tech Stack AI Readiness
Technical Debt Evaluation
Technical debt is the hidden tax on AI adoption. Systems with outdated authentication, inconsistent data models, or brittle deployment pipelines increase the cost and risk of every AI integration. We do not recommend eliminating all technical debt before starting AI work. We identify the specific debt items that would block your highest-priority AI use cases and recommend targeted remediation.
Authentication and authorization. We review how your systems handle identity: OAuth 2.0, SAML, API keys, session tokens. AI agents operating across systems need consistent, secure credential management. We flag systems that rely on shared credentials or lack programmatic access controls.
Data model consistency. When the same entity (a customer, an order, a product) is represented differently across systems, AI tools struggle to reconcile information. We map entity relationships across your stack and identify where canonical data models or transformation layers are needed.
Deployment and monitoring. AI components need the same operational rigor as any production system: CI/CD pipelines, health checks, logging, and alerting. We assess your current DevOps maturity and recommend the minimum infrastructure needed to run AI workloads reliably.
What This Looks Like
Engagements typically produce a compatibility overview mapping potential AI use cases to the systems they depend on, with a clearassessment of integrationreadiness at each point. This kind of analysis becomes the technical foundation for your AI roadmap.
Contact us at ben@oakenai.tech to start your tech stack review.