Document ingestion
Connectors for PDFs, knowledge bases, tickets, and databases, kept in sync.
AI & Agent Engineering
RAG Development Company
Retrieval-augmented generation (RAG) lets a language model answer from your own documents and data instead of guessing from its training.
We build the whole pipeline — ingestion, chunking, embeddings, vector search, re-ranking, generation, and evaluation — so answers are accurate, cited, and secure.
- LangChain
- Pinecone
- PgVector
- Chroma
- Embeddings
- Re-ranking
- Citations
- Evals
CAPABILITIES
What we build
Chunking and embeddings
Chunking strategies and embedding models matched to your content.
Vector databases
Pinecone, PgVector, or Chroma — picked for your scale, latency, and hosting needs.
Retrieval and re-ranking
Hybrid search and re-ranking so the right passages reach the model.
Grounded generation
Answers with citations, confidence states, and safe fallbacks.
Enterprise RAG security
Per-tenant isolation, access-aware retrieval, and private deployments.
TOOLS
The AI stack we work in
Chosen per use case, not by default.
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OpenAI
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Claude AI
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Gemini
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Llama
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Gemma
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LangChain
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LangGraph
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CrewAI
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AutoGen
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Hugging Face
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Pinecone
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PgVector
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Chroma
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Whisper
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Stable Diffusion
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PyTorch
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TensorFlow
OUR APPROACH
Our AI-native process
Seven steps, one team. Intelligence is scoped at the start and evaluated at the end.
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01
Discovery & AI Opportunity Mapping
We start every engagement by mapping where intelligence belongs in your product: which workflows an agent can own, where an LLM adds real value, and where plain software is the right answer. You get an honest AI scope — including what not to build.
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02
UI/UX Design
Research, flows, wireframes, and high-fidelity interfaces, validated with users before development begins. For AI features, we design the states that matter: streaming, uncertainty, and human-in-the-loop control.
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03
Intelligence Engineering
Model selection, prompt and agent architecture, RAG pipeline design, and evaluation harnesses — built with LangChain, LangGraph, CrewAI, and the vector store that fits your data.
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04
Web Development
Frontend and backend engineering on React, Next.js, Node, and Python, delivered in short sprints with working software at the end of each one.
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05
Mobile Development
Native and cross-platform apps for iOS and Android, built against the same APIs and design system as the web product.
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06
Cloud & Deployment
Infrastructure as code, CI/CD, and monitored releases on AWS, Azure, or GCP — including GPU/inference infrastructure and model-cost monitoring where AI workloads demand it.
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07
Testing & AI Evals
Automated test suites cover every release. AI features get their own evaluation suites — regression tests for prompts, hallucination checks, and output-quality scoring — so intelligence ships with the same rigor as code.
FAQ
RAG Development — common questions
What is RAG?
Retrieval-augmented generation retrieves relevant passages from your own data and gives them to a language model, so the answer is grounded in your content and can cite its source.
RAG vs fine-tuning: which is better?
Use RAG when answers depend on knowledge that changes or must be cited. Use fine-tuning to change style, format, or a narrow skill. Most products start with RAG and add fine-tuning only if evaluation shows a gap.
Which vector database should we use?
PgVector suits teams already on PostgreSQL, Pinecone suits large managed workloads, and Chroma suits prototypes and self-hosting. We choose based on scale, latency, and where your data must live.
How do you measure RAG quality?
With a test set of real questions scored for retrieval accuracy, answer faithfulness, and citation correctness, run on every change.
Ready to build AI-native?
Tell us what you're building — we'll show you where AI belongs in it.