Applied AI Engineering

Build AI capabilities that work inside real applications.

Cataluma designs and implements AI-enabled systems that connect models to users, data, APIs, workflows, and operational controls. The emphasis is on useful behavior, measurable quality, and maintainable software—not isolated demonstrations.

What Applied AI Engineering Means

The focus is the system around the model, not the model itself.

Cataluma is not positioning itself as a foundation-model research lab. The work is engineering everything a model needs to behave usefully inside a real application:

Model integration
Orchestration
Retrieval
Tool use
Validation
User interaction
Evaluation
Deployment

That includes connecting models to the right context, giving them tools and permissions to act, validating what they produce, and deploying and monitoring the result like any other production software component.

Core Capabilities

Four capability areas, combined as the problem requires.

AI-enabled applications

LLM-powered product features
Multimodal inputs
Structured outputs
Model-provider integration
AI-assisted user workflows

RAG and knowledge systems

Ingestion pipelines
Chunking and metadata
Embeddings
Vector databases
Retrieval logic
Citations
Access controls
Retrieval evaluation

Agent and workflow orchestration

Cataluma uses deterministic workflows where predictability matters and agentic behavior only where flexible planning or tool selection provides clear value.

LangGraph or equivalent orchestration
Deterministic and agentic steps
Tool calling
State management
Retries and fallbacks
Human approval
Escalation

Evaluation and observability

Task-specific datasets
Regression evaluation
Human review
Traces
Latency and cost monitoring
Prompt and model versioning

Typical Client Situations

Conversations usually start with one of these.

“We want to add AI to an existing SaaS product.”
“Our prototype works inconsistently.”
“We need an internal knowledge assistant.”
“We want to automate a process involving documents and approvals.”
“We need to compare models and prompts systematically.”
“We need AI behavior to remain reviewable and auditable.”

Architecture Patterns

A small set of patterns covers most applied AI systems.

The right pattern, or combination of patterns, depends on the problem, not a default architecture.

Application + model API

A product interface calls a model API directly, with validation and business logic around the request and response.

RAG pipeline

Retrieval, ranking, and generation are chained together, with retrieved context grounding the model’s output.

Agent workflow with human approval

Deterministic and agentic steps combine tool use with a required human checkpoint before consequential actions.

Evaluation loop

Prompt, model, and retrieval changes are tested against task-specific datasets before reaching production.

Provider abstraction

Application logic is decoupled from any single model provider, so models and providers can change without a rewrite. That protects the investment as the model landscape continues to shift.

Production-Readiness Considerations

Model access is the easy part. Reliable operation requires engineering.

Every applied AI engagement addresses these considerations to the degree the application’s risk and users require.

Quality
Latency
Cost
Access control
Prompt injection
Tool permissions
Failure handling
Data governance
Observability
Model changes

Technology Ecosystem

Proven technologies, applied narrowly.

Selected to fit the problem rather than a fixed stack.

Application: Next.js, React, TypeScript
AI orchestration: Python, OpenAI APIs, LangGraph
Data and retrieval: PostgreSQL, pgvector, vector databases
Infrastructure: AWS, Vercel, Docker
ML: PyTorch, PyTorch Lightning

Selected AI Work

Applied AI work in practice.

A sample of AI-enabled applications and workflows Cataluma has designed and built.

Applied AI Workflow Development

RAG Biography Workflow

Experimental project

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.

View project

Applied AI

Contentware

Client project

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.

View project

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