RAG & Custom AI Assistants
AI that knows your business.
A generic AI assistant that doesn't know your business is worse than useless. It hallucinates, gives generic answers, and can't access what your team actually needs.
A properly built RAG system changes that. We connect your documents, databases, and processes to language models grounded in your context, with cited sources, retrieval you can audit, and answers traceable back to the document they came from.
What We Build
- Internal policy and SOP Q&A systems. Employee handbooks, operating procedures, compliance references answered from the source of truth.
- Contract and document review pipelines. Ingest, classify, summarize, and flag at volume.
- AI-powered onboarding and HR assistants. New-hire context, benefits Q&A, policy lookups.
- Customer-facing knowledge base chatbots. Support deflection with cited answers, not hallucinated ones.
- Automated insight and report generation. LLM over your structured + unstructured data, on a schedule.
- Unstructured document data extraction. Pull specific fields from PDFs, scans, contracts, and emails into structured records.
What "Proper" RAG Means
Most RAG builds fail at chunking. Wrong chunks = wrong retrieval = wrong answers. We build RAG with:
- Semantic chunking. Splits on meaning, not character count.
- Metadata filtering. Queries hit the right subset of documents (by department, date, client, document type).
- Hybrid retrieval. Vector search combined with keyword (BM25) for better recall.
- Source citation. Every answer includes the document and passage it came from.
- Evaluation pipelines. We test retrieval accuracy against a ground-truth set before shipping to production.
- Guardrails. Refuse-to-answer logic when retrieval confidence is low, instead of confident hallucination.
When You Need This vs. a General LLM
If you're asking an LLM questions about your own business data and getting generic or hallucinated answers, you need RAG, not a better prompt. If the information lives in your documents, databases, or internal systems, it has to be indexed, not guessed.
Stack
- Vector Databases: Pinecone, Qdrant, Supabase pgvector, Weaviate, Chroma. Picked for scale, latency, and whether you need managed or self-hosted.
- Frameworks: LlamaIndex, LangChain, Haystack
- Embedding Models: OpenAI, Cohere, or open-source (BGE, E5) when self-hosted
- LLMs: Claude Sonnet, GPT-4o, Gemini 2.5 Pro, or self-hosted Llama / Mistral via Ollama, picked for accuracy and cost profile
- Data Ingestion: PDF, Word, HTML, Markdown, structured databases, APIs, webhooks
- Infra: Cloud or self-hosted, including air-gapped for regulated environments
Selected Work
- Niggle.ai: AI study platform that ingests documents, PDFs, and YouTube videos, runs semantic chunking, and generates source-traced notes, flashcards, and quizzes.
- Aiden Solutions: B2B AI technical support platform grounded in product documentation, resolving first-line tickets and escalating complex cases with full context.
- Albin: AI analysis platform for visual arts professionals with a structured knowledge base, contextual research, and auditable reasoning for artwork evaluation.
Start Here
Book a 15-minute audit. We'll map where your team is losing time to searching, re-reading, or re-typing, and scope the RAG system that replaces it. No commitment to proceed.