AI/ML Engineering

What we build

• Model Integration — AWS Bedrock, OpenAI, or any foundational model via standard APIs
• AI-Enabled ETL — Intelligent data transformation pipelines that extract structured metadata from unstructured sources
• Custom AI Applications — Built from first principles with proper prompt engineering and inference logic
• Serverless ML — Lambda-based inference with S3 event triggers and Step Functions orchestration
• Cost Optimization — Right-sized models and caching strategies to minimize token usage and API costs

Production AI, not prototypes

AI/ML projects need production engineering—proper error handling, observability, prompt versioning, and cost controls. We build systems that handle API failures gracefully, log model inputs/outputs for debugging, and don't surprise you with token bills.

The gap between "it works in a notebook" and "it runs reliably in production" is where most AI projects fail. We bridge that gap with proper software engineering practices combined with deep understanding of how these models work under the hood.

Academic foundation meets production engineering

Beyond MLOps and infrastructure, we bring formal AI/ML training to the table. This means understanding attention mechanisms, embeddings, and model architectures—not just API integration. Better architecture decisions come from understanding the underlying technology.

Whether building custom AI applications from first principles or integrating foundational models into production systems, the combination of academic rigor and production engineering delivers reliable, maintainable solutions.

Who this is for

Companies integrating AI into production workflows beyond proof-of-concept. You need reliable inference pipelines that process real data at scale, not Jupyter notebooks that worked once on sample data.

If you've tried integrating LLMs but hit problems with rate limits, costs spiraling, or inconsistent outputs breaking downstream systems, you need production AI engineering.

AI capabilities

Document Processing

• Foundational models (Claude, GPT-4, etc.) for intelligent document extraction
• Structured data extraction from PDFs, images, and text
• Classification, tagging, and metadata generation
• Multi-document comparison and analysis

Data Transformation

• Converting unstructured data to structured formats (JSON, Parquet)
• Entity extraction and relationship mapping
• Data quality improvement through AI-powered validation
• Intelligent deduplication and normalization

Retrieval-Augmented Generation (RAG)

• Vector databases (Pinecone, pgvector) for semantic search
• Embeddings generation with any model provider
• Context-aware LLM responses using private knowledge bases
• Citation tracking and source attribution

Production engineering practices

Error Handling & Reliability

• Exponential backoff for rate limit errors
• Dead-letter queues for failed inferences
• Circuit breakers for cascade failure prevention
• Graceful degradation when models are unavailable

Cost Control

• Response caching to reduce duplicate API calls
• Token usage monitoring and alerting
• Model selection based on task complexity
• Batch processing for cost-efficient inference

Observability

• CloudWatch metrics for latency, cost, and error rates
• Prompt versioning and A/B testing infrastructure
• Input/output logging for debugging and auditing
• Performance dashboards for model monitoring

What you inherit

Production-ready AI infrastructure with complete documentation, cost dashboards, and operational runbooks. Your team gets prompt libraries, error handling patterns, and monitoring dashboards—not experimental code that only one person understands.

All infrastructure deployed as code, making changes reviewable and repeatable. No hidden configurations or tribal knowledge required to maintain the system.

See our work for examples of AI/ML engineering projects and production deployments.