LangChain is the fastest way to prototype an LLM app and one of the easiest ways to end up with a chain nobody can debug six months later. We build RAG pipelines and AI agents on LangChain (and LangGraph where a graph, not a chain, is the right model) with the retrieval quality, evaluation, and observability a production system actually needs.
A weekend RAG demo and a production LLM app share almost no engineering concerns beyond the model call. We build for the second one.
Chunking strategy, hybrid search, reranking, and citation grounding — the parts that determine whether answers are actually correct, not just plausible-sounding.
Agents that call your internal systems with the right guardrails, instead of an open-ended chain that occasionally does something unpredictable in production.
A test set and automated scoring for answer quality before launch, so you know accuracy numbers instead of guessing from spot checks.
Simple chains where a chain is enough, LangGraph where you need cycles, branching, or multi-agent coordination — not one framework stretched past its fit.
Model selection, caching, and prompt design tuned so a RAG answer doesn't cost more or take longer than the product can tolerate.
Documented chains, tracked prompts, and clean eval pipelines so your team can extend the system without reverse-engineering someone's notebook.
Scoped to the LLM app you're actually building — not a generic chatbot template.
Document ingestion, chunking, embeddings, and retrieval tuned against your real content and queries, not a demo dataset.
Task-specific agents that call internal APIs and tools, with scoped permissions and predictable failure modes.
Multi-step and multi-agent flows with branching, retries, and human-in-the-loop checkpoints where the stakes require them.
Picking and configuring the right store — Pinecone, pgvector, Weaviate, or similar — for your scale and latency needs.
Answer-quality test sets, automated scoring, and production tracing so regressions get caught before users notice.
Taking an existing LangChain proof of concept and making it reliable, observable, and cost-controlled for real traffic.
RAG (retrieval-augmented generation) grounds an LLM's answers in your own documents or data instead of relying purely on what the model memorized during training. You need it whenever answers must reference your specific content — internal docs, product catalogs, support history — and be current, not frozen at training time.
It depends on the shape of the problem. LlamaIndex tends to be faster to stand up for pure retrieval-heavy use cases; LangChain and LangGraph fit better once you need agents, multi-step reasoning, or tool orchestration. For simple, well-defined pipelines a thin custom layer is sometimes less overhead than either framework. We recommend based on your actual requirements, not a default preference.
It depends heavily on data volume, retrieval complexity, and evaluation requirements — a focused internal Q&A tool costs far less than a customer-facing system with strict accuracy and latency requirements. We give a fixed estimate after a short scoping call rather than a generic range.
Yes. That's most of the agent work we do — connecting an LLM to your existing systems (CRMs, internal APIs, databases) with scoped permissions so the agent can act without unpredictable side effects.
We build a test set from real or representative queries, score answers automatically against expected outcomes, and wire up tracing so you can see what the model retrieved and generated in production. That's what catches quality regressions before users report them.
Tell us what you're building — a RAG system, an agent, or a prototype that needs to become production-ready — and we'll scope an approach and a fixed estimate.