Production LLM applications, RAG pipelines, AI agents and MCP servers, engineered with evaluation and observability from day one.
RUNTIMESOLUTION is an AI development company that builds systems which survive contact with real users. We work on LLM applications, retrieval-augmented generation, autonomous agents, machine learning pipelines and the MCP servers that connect models to the tools and data they need.
The hard part of AI engineering is almost never the model call. It is retrieval quality, evaluation, cost control, latency, failure handling and knowing whether a change made the system better or quietly worse. A demo can be built in an afternoon. A system your team trusts with customer-facing work needs an evaluation harness, tracing, guardrails and a rollback story, and that is what we build.
We are model-agnostic by default. We route through the providers that fit your accuracy, latency, cost and data-residency constraints, and we design so that swapping a model later is a configuration change rather than a rewrite.
Customer-facing and internal AI products — assistants, copilots and business intelligence tools — built with streaming, structured outputs, tool calling and the failure handling that keeps them usable when a model misbehaves.
Ingestion, chunking, embedding and reranking pipelines tuned against a real evaluation set, because retrieval quality determines answer quality far more than model choice.
Agents with scoped tools, explicit termination conditions, durable state and human-in-the-loop checkpoints, so long-running work is inspectable rather than mysterious.
Model Context Protocol servers that expose your internal systems to AI clients safely, with authentication and permission boundaries designed in rather than bolted on.
Golden datasets, regression suites, tracing and cost dashboards, so you can prove a prompt or model change is an improvement before it reaches users.
Machine learning work — fine-tuning, distillation and prompt optimization — applied only where it beats a cheaper approach, with the measurement to back that decision up.
In practice: LLM-powered applications, retrieval systems over your own documents and data, agents that carry out multi-step work with tools, and the infrastructure around all of it, meaning evaluation, tracing, guardrails and cost control. The engineering work is mostly in that surrounding infrastructure rather than in the model itself.
Retrieval-augmented generation gives a model relevant excerpts from your own content at question time, so answers are grounded in your data instead of the model's general training. You need it when answers must reflect information the model was never trained on, such as internal documentation, product catalogs or customer records. You do not need it when the task is pure reasoning or transformation of text the user already supplies.
You cannot eliminate it, and anyone claiming otherwise is selling something. You reduce it substantially by grounding answers in retrieved sources, constraining outputs with schemas, citing the source of every claim, and measuring accuracy against a fixed evaluation set on every change. Just as importantly, you design the interface so users can see the source and the system can say it does not know.
Whichever fits the constraints. We commonly work with Claude, GPT and Gemini models, and with open models where data residency or cost demands it. We build behind a provider-agnostic layer so you are not locked to one vendor's pricing or availability.
A well-scoped RAG system or internal assistant is typically a six to ten week build. A multi-agent system integrated across several internal tools is larger. Beyond the build there is ongoing inference cost, which we model up front, because an architecture that is elegant and unaffordable is not a solution.
Yes, and that is usually the point. Most of our AI work involves connecting models to existing databases, document stores, CRMs and internal APIs, with authentication and permission boundaries preserved so the AI layer cannot see anything the requesting user could not already see.