I embed intelligent AI capabilities into existing products and build AI-first tools from scratch. OpenAI, Anthropic Claude, and Google Gemini integrations — streaming UIs, RAG pipelines, semantic search, tool-use agents, and LLM-powered workflow automation.
Retrieval-Augmented Generation over your private data. Document ingestion, vector embeddings, semantic retrieval, and LLM synthesis.
Streaming chat interfaces with conversation history, context management, and tool-use for taking real actions in your product.
Automated writing assistance, metadata generation, article summarisation, and content repurposing with structured output.
Vector embedding-based search that finds conceptually related results — not just keyword matches — across your content or product catalogue.
LLM-triggered workflows: classify inbound requests, extract structured data from documents, auto-route tickets, generate reports.
Natural language querying over your data, automated insight generation, and anomaly detection integrated into existing dashboards.
It depends on your use case. Claude (Anthropic) excels at long-context understanding, nuanced writing, and following complex instructions — ideal for document analysis and content generation. GPT-4 has the broadest tool ecosystem and strong general reasoning. Gemini is competitive for multimodal tasks and integrates naturally with Google infrastructure. I help you choose based on your specific task, latency requirements, and cost constraints.
RAG (Retrieval-Augmented Generation) lets an LLM answer questions based on your private data — documentation, product catalogues, support tickets, internal wikis — rather than just its training data. You need it if users should query your own data in natural language. I build RAG pipelines using pgvector (PostgreSQL), Pinecone, or Weaviate depending on your scale and existing infrastructure.
Costs vary significantly by model and usage pattern. GPT-4o mini and Claude Haiku are very cheap for simple tasks (~$0.15–$0.30/1M input tokens). Premium models (GPT-4o, Claude Sonnet/Opus) are more expensive but handle complex reasoning. Prompt caching can reduce costs by up to 90% for repeated context. I design prompts and architectures with cost efficiency in mind and help you estimate monthly API spend before we build.
Yes — this is the most common engagement. I integrate AI features as additive layers: a new Route Handler or Server Action that calls the LLM, a new UI component for the streaming response, and data pipeline changes if RAG is needed. Your existing codebase stays intact. I scope the integration to touch as little of your existing code as necessary.
A chatbot generates text responses. An AI assistant with tool use (function calling) can actually take actions — query your database, call an API, create a record, send an email — and incorporate the result into its response. For business applications, tool-use agents are far more useful. I build both, but lean toward agentic architectures for anything beyond simple Q&A.
Describe what you want the AI to do — I'll reply with an honest feasibility assessment within 24 hours.
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