LLM integration & deployment

What "LLM integration" covers at QwertyBit

LLMs change the economics of work that involves unstructured language — reading, summarising, classifying, drafting, reasoning over documents. The value is real. Deploying them reliably into production is harder than most demos suggest. Our LLM integration service takes a business workflow from "we wonder if an LLM could help" to "it's live, it's measured, and we know what it costs".

What we deliver

• Scoped LLM-backed applications. Customer-support assistants, compliance-aware document workflows, contract review engines, internal knowledge agents, call transcription and action-item extraction — integrated into your existing tools with proper APIs and audit trails.
• RAG pipelines that actually work at scale. Chunking strategy, embedding model selection, retrieval reranking, eval harness for retrieval quality, graceful degradation when the knowledge base changes.
• On-prem LLM deployments. Full open-weights model deployment on your infrastructure via LLM Studio — Llama, Qwen, Mistral, DeepSeek, Gemma. Hardware sizing, fine-tuning pipelines, observability, disaster recovery.
• Hybrid routing layers. A routing layer in front of multiple models (Claude for reasoning, GPT-4o for tool use, Llama on-prem for sensitive data, a small model for classification) so each call goes to the cheapest model that is good enough.
• Eval harnesses and observability. Every integration ships with an eval set that catches regressions before your users do, plus production monitoring for latency, cost, token usage, and downstream outcome quality.

The models and tools we use

• Anthropic Claude — our default for high-reasoning, long-context, tool-use heavy agents.
• OpenAI GPT — when function-calling ecosystem breadth matters.
• LLM Studio — on-prem open-weights models for regulated or sovereignty-sensitive clients.
• CrewAI and LangGraph — when multi-agent orchestration earns its complexity.

Where LLM integration meets the rest of QwertyBit

LLM integrations are rarely standalone — they live inside AI agents, business automation pipelines, and bespoke software. The LLM integration service is the engineering core that makes those other engagements reliable in production. Any of those services can bundle this in, or you can contract us for the LLM layer alone if your team will handle the application around it.

How to start

Book a scoping call with a specific use case in mind. We will tell you within one week whether an LLM is the right tool, which model to use, what it would cost to build, and what the monthly run cost looks like. If the honest answer is "you don't need an LLM here," that is what you'll hear.