What We Build
Off-the-shelf AI tools solve general problems. Your business has specific ones. Custom AI development creates applications tailored to your exact business logic, data, and workflow requirements. These are not science projects. They are production-grade tools built to solve specific business problems that generic software cannot address.
Intelligent Search
Search systems that understand intent, not just keywords. Employees find documents, products, and answers across your entire knowledge base using natural language. Semantic search powered by vector embeddings surfaces relevant results that keyword search misses.
Predictive Models
Forecast demand, predict churn, estimate project timelines, and anticipate equipment failures using models trained on your historical data. Predictions that account for the patterns specific to your business, not generic industry averages.
Business Logic AI
AI that understands your specific rules, classifications, and decision criteria. Underwriting models for your risk profile. Pricing engines for your market dynamics. Classification systems for your product taxonomy. Built around how your business actually works.
Prototype to Production
We take AI concepts from proof-of-concept through production deployment with proper error handling, monitoring, scaling, and maintenance plans. No prototypes that never ship. No demos that cannot handle real-world data volume.
Development Process
Scope
Define problem and success criteria
Prototype
Rapid proof of concept
Validate
Test with real data and users
Ship
Production deployment and monitoring
Scope
Define problem and success criteria
Prototype
Rapid proof of concept
Validate
Test with real data and users
Ship
Production deployment and monitoring
Custom AI Architecture
Technical Approach
Custom AI development requires balancing ambition with pragmatism. We use the simplest approach that solves the problem effectively. Sometimes that means fine-tuning a large language model. Sometimes it means a classical machine learning model. Sometimes it means retrieval-augmented generation with a well-designed prompt. The goal is a working solution, not a showcase of technical complexity.
Model selection. We evaluate options across the spectrum. Flagship models for complex reasoning tasks. Smaller fine-tuned models for cost-sensitive applications with focused use cases. Open-weight models for on-premise deployments where data cannot leave yourinfrastructure. The model serves the application, not the other way around.
Data architecture. Custom AI applications need proper data foundations. Vector databases like Pinecone, Weaviate, or pgvector for semantic search. Feature stores for machine learning models. Data pipelines that keep training data current. We design the data layer alongside the application so the AI always has access to current, high-quality information.
Production engineering. A model that works in a notebook is not a product. We build API layers, implement caching strategies, design fallback behavior for when models are slow or unavailable, add observability with tools like LangSmith and Weights and Biases, and set up the infrastructure to handle your production traffic reliably.
Who This Is For
Companies with unique business processes that generic AI tools do not address. Organizations sitting on valuable proprietary data that could power competitive advantages. Teams that have experimented with AI but need help getting from prototype to production. Any business where the right custom AI tool would meaningfully change how work gets done.
Contact us at ben@oakenai.tech to discuss your custom AI application requirements.