AI & Machine Learning Integrationsby SoftwareCrafting

Service Overview

We integrate language models into products in a way that survives real users, which means treating the model as one unreliable component in a system rather than as the product itself. In practice that means retrieval built so answers are grounded in your own data with citations, evaluation before launch so you know the quality baseline instead of guessing, guardrails at the boundary, and observability that captures the full trace of a request including which documents were retrieved and which tools were called. Cost is designed in rather than discovered on the first invoice: prompt caching with a stable prefix, routing simple requests to smaller models and reserving the expensive one for hard cases, retrieval discipline so you send less context rather than more, and per tenant token budgets with graceful degradation. We build with the current Claude, GPT, and Gemini model families and keep the provider boundary clean so switching is a configuration change. We will also tell you where a language model is the wrong tool and a simpler approach is more reliable and far cheaper.

Technologies we use

Key Features

• LLM assistants for SaaS, support, operations, and internal tools
• Document analysis, resume parsing, report extraction, and summarization
• Retrieval-augmented generation with vector search
• Prompt engineering, tool calling, and structured AI outputs
• Human-in-the-loop review and approval workflows
• AI usage tracking, cost controls, and rate limits
• Model evaluation, regression checks, and fallback behavior
• Secure integration with existing product data and permissions
• Retrieval augmented generation with grounded answers and citations
• Evaluation suites run before launch and in continuous integration
• Full request tracing including retrieved documents and tool calls
• Prompt caching and model routing to control cost per request
• Per tenant token budgets with graceful degradation at the limit
• Guardrails and content filtering at the system boundary
• Provider abstraction so switching models is a configuration change
• Human review and escalation paths for low confidence outputs