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From systems engineering to modern software — a rigor that changes what gets delivered.
Paul combines a PhD in Physics and 15+ years of industrial delivery — embedded, real-time, cloud-native — to produce architectures informed by physics, resource constraints, and failure modes, not just software design patterns. Latency budgets account for hardware constraints. Reliability targets draw from industrial safety norms. Every architectural decision is validated against what the target team can actually build, test, and operate.
Software architecture at Codotek means systems engineering rigor applied to software — not theoretical diagrams, but structures that survive contact with production reality.
Selected by problem, team size, operational maturity, and business context — not by what appeared at the latest conference.
Service boundary design via DDD and bounded contexts. Communication protocol selection (synchronous REST/gRPC vs. asynchronous messaging), distributed tracing, and eventual consistency trade-offs.
Event sourcing, CQRS, message broker selection (Kafka, RabbitMQ, AWS EventBridge), consumer group design, dead-letter queues, and schema evolution strategies.
Service mesh (Istio/Linkerd), GitOps deployment (ArgoCD/Flux), multi-cluster topologies, resource quota and autoscaling design.
Function decomposition, cold-start mitigation, stateless design patterns, edge caching strategies, and cost modeling for variable-load workloads.
LLM integration patterns (RAG, tool-calling, agent orchestration), ML pipeline design, inference serving (batch vs. real-time, GPU scheduling), and vector database selection and integration.
Contract-first development, versioning strategies, gateway patterns (rate limiting, auth, aggregation), and SDK generation from OpenAPI specs.
Terraform module design, environment promotion pipelines, secret management, and policy-as-code for infrastructure compliance.
Two levels of representation — for stakeholders and for development teams.
System-level structure — components, boundaries, interactions, deployment topology. Produces a high-level view that decision-makers can understand and teams can implement.
UML notation: component, sequence, and deployment diagrams.
Module-level structure — class and interface diagrams, design patterns (OOP, SOLID). Translates architectural decisions into concrete code structure.
UML notation: class, state, and interaction diagrams.
UML is used as a notation tool, not a constraint. Communication clarity takes priority over standard conformance.
Designing AI systems is not just a data science problem — it is an architecture problem.
AI capabilities reach production reliably only when the architecture supports them — scalability under load, maintainability as models evolve, observability for non-deterministic workflows.
RAG pipeline design (chunking strategies, embedding model selection, vector store indexing), tool-calling architectures, prompt pipeline orchestration (LangChain, LlamaIndex, custom), and context window management for long-running sessions.
Data ingestion and validation (Great Expectations, dbt), feature engineering pipelines, experiment tracking (MLflow, W&B), model versioning, and CI/CD for ML with continuous training triggers.
Synchronous REST vs. asynchronous job queue serving, batching strategies for throughput optimization, GPU resource allocation and cost modeling, model caching and A/B testing infrastructure for model variants.
Feature store design (online vs. offline), vector database selection and schema design (Pinecone, Weaviate, pgvector), data lake organization for training data, lineage tracking, and privacy-preserving architectures.
Five principles applied to every engagement — not ideals, operational commitments.
Every architectural decision is validated against what the target team can actually build, test, and deploy within their operational constraints. No component diagram that cannot be traced to a deliverable artifact.
Systems are designed for the likely evolution paths, not all conceivable futures. Modularity is achieved through explicit interface contracts and enforced dependency boundaries — not by avoiding decisions.
Threat modeling (STRIDE or equivalent) is performed at architecture time, not during a pre-release security audit. Trust boundaries, authentication perimeters, and data classification zones are first-class architectural concerns recorded in ADRs.
Observable systems by default: structured logging, distributed tracing, and health endpoints are architectural requirements, not optional additions. Dependency management is explicit — no hidden coupling, no undocumented shared state.
Architecture artifacts are communication tools first. A new engineer joining the team should be able to understand the system's structure, the rationale for key decisions, and the boundaries they must not cross — from reading the architecture documentation alone, on day one.
Architecture evolves with the software — not frozen at project kickoff.
Architecture Decision Records (ADRs) are the primary traceability mechanism — each significant decision is captured with its context, decision, consequences, and alternatives considered. Architecture reviews are scheduled at meaningful delivery milestones. Architectural fitness functions (automated checks that verify constraints are not violated by new code) are implemented as part of the CI pipeline.
Architecture documentation is a measurable strategic asset. It reduces new engineer onboarding from weeks to days. It is the primary tool for communicating system-level risk to non-technical stakeholders. It prevents incremental boundary violations that accumulate into expensive rewrites. It enables identifying where new capabilities — including AI integrations — can be added without breaking existing contracts.
A concrete methodology — not a generic I use AI tools statement.
70+ specialized agents. 20+ orchestration prompts. Architecture deliverables produced faster, with a consistency that manual approaches cannot guarantee. Each agent is configured for a specific architecture concern: pattern selection, trade-off analysis, domain model extraction, security threat modeling.
AI agents explore pattern alternatives and architectural trade-offs — surfacing prior art and research that a human working alone would take days to find. The design phase produces more thoroughly explored alternatives in less elapsed time.
Once architectural decisions are recorded, agents generate the foundational code structure that enforces those decisions: project scaffolding with correct layering, module boundaries, interface contracts as TypeScript types or OpenAPI specs, and dependency injection wiring. The scaffold reflects the architecture — it does not drift from it.
Agents enforce architectural constraints continuously during development: no unauthorized cross-layer imports, no direct database access from the presentation layer, no circular dependencies between bounded contexts. Violations are caught at pull request time, not discovered months later during an architecture audit.
When architecture needs to evolve — a monolith being decomposed into services, a synchronous integration being converted to event-driven — agents assist with systematic refactoring: identifying all violation points, generating interface adapters, producing migration plans with sequenced steps that maintain production stability.
Before committing to an architectural decision, agents rapidly prototype two or three competing approaches — enough working code to evaluate performance characteristics, operational complexity, and development ergonomics. Technical decision-makers get evidence, not opinions.
Every project starts with a conversation.