AI pipelines that do more than a single model call.
LangChain for multi-step AI workflows — retrieval-augmented generation, agent loops, multi-step chains, and the orchestration logic that connects language models, vector databases, and tools into AI features that handle complex tasks.
Application that needs AI capabilities beyond a single prompt-response — document Q&A, multi-step research, AI agents that use tools, or RAG pipelines that ground the model in specific data
Single-call AI integrations (send prompt, get response) handle simple AI features. More sophisticated AI features require multi-step pipelines:
Retrieval-Augmented Generation (RAG). The model's training data has a knowledge cutoff and doesn't include company-specific data. RAG retrieves relevant documents from a vector database before the model call — grounding the model's response in current, specific information.
Agent loops. AI agents that decide which tools to use and in what order: search the web for current information, query a database for relevant records, run a calculation, and return a synthesized answer. LangChain's agent abstractions handle the tool selection and execution loop.
Multi-step chains. Some AI tasks require sequential steps: extract structured data from a document, validate the extraction, enrich it with database lookups, and format the output. LangChain chains compose these steps.
LangChain vs. direct model API calls: LangChain adds abstraction overhead. For simple prompt-response features, call the model API directly. LangChain is the right choice when the application needs: RAG pipeline orchestration, agent tool use, multi-model pipelines, or the evaluation and tracing tooling LangSmith provides.
LangChain-powered AI pipeline with RAG, chain orchestration, tool use, and the evaluation framework that validates AI output quality
RAG pipeline
Document ingestion and chunking. Embedding generation and vector store upsert. Retrieval chain with context injection. Response generation with citations.
Vector store integration
Pinecone, pgvector (Neon/Supabase), or Chroma integration. Similarity search for retrieval. Metadata filtering.
LangChain agents
Tool definition and registration. React agent with tool selection. Agent execution with LangSmith tracing.
Structured output
Zod schema for structured extraction. LangChain's `withStructuredOutput` for type-safe AI responses.
LangSmith tracing
Trace logging for every chain execution. Evaluation datasets. Prompt versioning.
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
LangChain-powered AI pipeline with RAG, chain orchestration, tool use, and the evaluation framework that validates AI output quality
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
AI pipeline scope is defined by the feature requirements and the data sources. Fixed price.
Related engagements.
Questions, answered.
Direct API calls for: single prompt-response features, streaming chat, and simple tool use. LangChain for: RAG pipelines, agent loops with multiple tools, complex chains with conditional logic, or when LangSmith observability is needed. LangChain adds overhead; don't use it unless the feature complexity justifies it.
LangSmith evaluation for testing prompts against a dataset of expected outputs. Structured output (Zod schemas) for extracting typed data from model responses. Human review workflows for high-stakes outputs.
AI application from $25k. Complex RAG or agent pipelines are scoped in the proposal. Fixed-price.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.