Most companies experimenting with AI are stuck in demo mode. We build the production systems — data pipelines, retrieval architecture, LLM orchestration — that turn AI experiments into revenue-generating infrastructure.
Large language models are extraordinary at reasoning. What they're not good at, by default, is knowing anything about your business — your products, your customers, your internal documentation, your processes.
The gap between a ChatGPT demo and a production AI system is a data engineering problem. Retrieval-Augmented Generation (RAG) is how you close that gap: instead of fine-tuning a model (expensive, slow, brittle), you build a pipeline that retrieves relevant context from your own data and feeds it to the model at inference time.
Done right, this means an AI that answers questions using your actual knowledge base, generates content grounded in your real product catalog, or automates workflows using your live business data — without hallucinating facts you can't afford to get wrong.
Done wrong, it means a demo that looks great in a slide deck and falls apart the moment it touches real data at scale.
We don't start with the model. We start with your data — because that's where every RAG project either succeeds or fails.
We identify where your relevant data lives — databases, document stores, APIs, CRMs, SharePoint, S3 buckets — and assess quality, structure, and update frequency. Bad data in means bad answers out, so this step is non-negotiable.
We design the document processing pipeline — how content gets split, cleaned, and converted to vector embeddings. The chunking strategy has an outsized impact on retrieval quality and is often where off-the-shelf solutions fall down.
We deploy and configure the vector database (Pinecone, pgvector, or Weaviate depending on your stack), implement semantic search, and tune retrieval parameters. We also implement hybrid search — combining semantic and keyword retrieval — where it improves accuracy.
We wire the retrieval layer to the language model, design the prompt architecture, and implement guardrails against hallucination. We work with OpenAI, Anthropic Claude, Azure OpenAI, and open-source models depending on your data privacy requirements.
We deploy the system as a production API, implement logging and observability, and set up evaluation pipelines so you can measure answer quality over time — not just at launch.
Every engagement is different, but these are the patterns we build most often.
An AI that answers employee questions using your actual documentation, SOPs, policies, and institutional knowledge — instead of hallucinating plausible-sounding wrong answers.
LLM pipelines that generate product descriptions, reports, or structured content at scale using your real data as the source of truth.
Replace keyword search with meaning-based retrieval across product catalogs, document libraries, or customer support tickets. Users find what they mean, not just what they type.
Automated ingestion, classification, and extraction from contracts, invoices, reports, or any unstructured document type — feeding structured outputs into your downstream systems.
LLM-powered agents that can reason across multiple steps, use tools, query your APIs, and complete multi-stage tasks autonomously — going beyond single-turn question answering.
Production AI features embedded in your product — chatbots, recommendation engines, AI-assisted workflows — designed for reliability and scale, not just for demo conditions.
We choose tools based on your existing infrastructure, data privacy requirements, and scale — not on what's trending on Hacker News this week.
15 minutes, no obligations. Tell us what you're working with and we'll tell you honestly whether we can help — and what it would actually take.
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