AI Software Built for Production — Not Just Demos
Building AI software that works in a demo environment is straightforward. Building AI software that performs reliably at production scale — with proper error handling, observability, cost control, security, and the operational rigor your engineering teams can maintain — is an engineering discipline. AxiomAim designs and builds production-grade AI applications, integrations, and data pipelines for organizations that cannot afford to ship AI that fails in ways they cannot diagnose.
What We Build
Retrieval-Augmented Generation (RAG) Systems
Design and build production RAG architectures that ground LLM responses in your organization's data — document processing pipelines, embedding generation, vector database design, retrieval strategies, and re-ranking layers. We engineer RAG systems that produce accurate, cited, and auditable outputs rather than hallucinated responses to domain-specific queries.
AI API Design & Integration Engineering
Design and implement the API layers that expose AI capabilities to your applications and users — structured prompting, model routing, response validation, streaming interfaces, token budget management, and fallback handling. Production AI APIs require the same engineering rigor as any other critical service — rate limiting, authentication, caching, and thorough error handling.
Fine-Tuning & Model Customization
Design and execute fine-tuning programs that adapt foundation models to your domain — dataset curation, training pipeline engineering, evaluation framework design, and benchmarking against your production use cases. Fine-tuning is appropriate when prompt engineering and RAG have reached their limits and domain-specific behavior must be baked into the model weights.
ML Pipeline Engineering
Build the data ingestion, preprocessing, feature engineering, training, evaluation, and deployment pipelines that ML systems require to operate reliably — with version control for data and models, reproducible experiment tracking, and CI/CD integration so model updates move through the same controlled delivery process as application code.
AI-Native Application Development
Build full-stack applications designed from the ground up around AI capabilities — where AI output is a first-class citizen of the product architecture rather than a feature bolted onto a traditional application. This includes the UI patterns, latency management, streaming UX, feedback collection, and user trust design that AI-native products require.
Model Evaluation & Testing Frameworks
Design the evaluation frameworks that tell you whether your AI system is actually working — automated test suites, human evaluation protocols, regression detection pipelines, adversarial testing, and benchmark datasets that reflect your real production distribution. You cannot improve what you cannot measure objectively.
How We Build
Production AI software requires engineering discipline at every layer — from the data pipeline to the user interface. We apply the same rigor to AI systems that we apply to any critical enterprise software.
Prototype & Validate
Build the smallest possible version of the AI capability that can answer the core feasibility question — validating that the approach works on your data, in your environment, at acceptable quality thresholds before committing to full production engineering investment.
Engineer for Production
Harden the validated prototype into production software — adding error handling, observability instrumentation, cost controls, security review, load testing, and the operational runbooks your on-call engineers will need when something behaves unexpectedly at 2am.
Transfer & Enable
Hand off to your engineering team with complete documentation, test coverage, architecture decision records, and knowledge transfer sessions — so your team owns what was built and can maintain, extend, and improve it without dependency on external consultants.
Technology Depth
We work across the AI engineering stack — from foundation model APIs and vector infrastructure to ML training frameworks and the cloud platforms that run them in production.
Foundation Models
OpenAI, Anthropic, Google Gemini, Meta Llama, and open-weight models — selected and evaluated against your specific use case, cost requirements, and data privacy constraints.
Vector Infrastructure
Pinecone, Weaviate, pgvector, Chroma, and Vertex AI Vector Search — designed for scale, latency, and the hybrid search patterns production RAG applications require.
ML Frameworks
PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, and Vertex AI — with MLflow and Weights & Biases for experiment tracking and model registry.
Cloud Platforms
GCP (Vertex AI, Cloud Run, BigQuery ML) and AWS (SageMaker, Bedrock, Lambda) — with containerized deployment, managed inference, and cost-optimized scaling configurations.