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Applied AI Engineering and Software Architecture
Cataluma designs and implements LLM workflows, RAG systems, intelligent automation, and modern web platforms connected to your data, APIs, and business processes.
Problems Cataluma Solves
Cataluma helps organizations move from ideas, experiments, and disconnected tools to maintainable applications integrated with real data, workflows, and operational requirements.
Existing product
You have a working product, but adding AI requires more than calling a model API. The new capability must fit your architecture, data, permissions, interfaces, and deployment model.
Cataluma helps by: designing and implementing AI features that integrate cleanly with your current system.
Prototype maturity
Your prototype demonstrates potential, but it lacks the validation, failure handling, observability, security, or maintainability needed for real users.
Cataluma helps by: hardening the surrounding application and operational architecture without overstating what the model itself can do.
Reliability and control
You need to understand whether an AI workflow is accurate, stable, secure, and cost-effective as prompts, models, data, and business rules change.
Cataluma helps by: adding evaluation, tracing, structured validation, versioning, monitoring, and review controls.
Core Services
Cataluma designs and implements complete systems around AI capabilities, including application interfaces, data flows, integrations, evaluation, deployment, and operational controls.
Design and build AI-enabled product features or complete applications that connect language and multimodal models to real users, data, APIs, and business processes.
Build systems that retrieve relevant information from organizational content and use it to produce more grounded, traceable, and useful AI responses.
Automate multi-step processes by combining deterministic workflow orchestration with AI-assisted reasoning, tool use, and human oversight.
Improve application architecture, APIs, infrastructure, and data flows so existing systems can support new capabilities without increasing fragility.
Engagements can begin with architecture and feasibility analysis, a focused proof of value, or implementation within an existing product.
Review the engagement optionsProduction Engineering
A successful AI feature depends on more than model quality. It also requires reliable application architecture, controlled data access, measurable behavior, clear failure handling, and maintainable deployment practices.
Cataluma engineers the surrounding software needed to move AI work beyond isolated demonstrations and into dependable applications and operational workflows.
See how Cataluma worksMeasure whether changes to prompts, models, retrieval, tools, or application logic improve behavior, and capture the traces needed to diagnose failures.
Task-specific evaluation datasets · Workflow and agent traces · Latency, cost, and quality monitoring
Constrain model responses to formats the application can trust, and control which users, models, and tools can access data or take action.
Schema-constrained, validated responses · Authentication and permission boundaries · Secrets and sensitive-data handling
Handle uncertain outputs and failures explicitly, and ship AI capability within a testable, versioned, maintainable software system.
Retries, fallbacks, and human escalation · CI/CD and prompt/model versioning · Modular, maintainable architecture
The appropriate controls depend on the application’s risk, users, data, operating environment, and consequences of incorrect output.
Selected Work
Selected client implementations and engineering projects demonstrating practical AI integration, workflow automation, and modern application architecture.
David Bleeker
Principal Consultant • Applied AI Engineering
Principal-Led Delivery
Cataluma is led by David Bleeker, a software architect and applied AI engineer with extensive experience designing and delivering enterprise and commercial applications.
David remains directly involved in architecture, implementation, technical decision-making, and client communication. Additional engineering capacity can be engaged when project scope requires it, while technical direction and delivery accountability remain principal-led.
Hands-on implementation across Python, TypeScript, Next.js, React, GraphQL, PostgreSQL, AWS, OpenAI APIs, LangGraph, and PyTorch.
Engagement Models
Engagements can begin with a focused technical assessment, a defined proof of value, or direct implementation within an existing product. Scope, deliverables, and decision criteria are established before development begins.
Architecture and feasibility
Evaluate whether AI fits the problem and define a practical implementation path.
Best for: early-stage initiatives and high-uncertainty projects
Product implementation
Core engagementA complete product capability: architecture, development, AI integration, validation, and deployment.
Best for: defined products and production-oriented features
Fractional technical leadership
Technical direction, architecture guidance, and delivery coordination without a full-time role.
Best for: teams needing experienced technical guidance
Not sure where to start? A short conversation identifies the right engagement, or see the full breakdown on Services.
Discuss your project
Applied AI
Campaign creation dropped from a full day of manual writing to a single review-and-approve pass, with the platform integrating social, email, and billing systems and running in production for four years, and one customer reporting a 400%+ engagement increase.
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Applied AI Workflow Development
Every retrieval decision, generated draft, and human approval is logged and reviewable, giving stakeholders a complete, auditable workflow across nine tracked states that shows retrieval, generation, and sign-off working together end to end, without skipping the human review step.
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Applied AI Application Development
Every reasoning step, tool call, and API response streams to the screen in real time, showing the agent orchestrate three real third-party APIs in four to five model-chosen steps — genuine agentic planning driven by roughly 40 lines of code, not a scripted demo.
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