01
RAG architecture
Chunking, embedding, and retrieval strategies tuned to your document types and query patterns.
Generative AI & RAG
Generative AI development services built on your data
Generative AI development services for businesses that need AI to answer from their own knowledge, not the public internet. We build LLM apps, OpenAI and ChatGPT integrations, RAG assistants, and AI agents, with evaluation and guardrails built in from the start.
Technologies we use
- OpenAI
- LangChain
- LlamaIndex
- Pinecone
- pgvector
- Python
- FastAPI
- AWS
- Answers grounded in your data
- Reduced support load
- Built for evaluation
Generic chatbots don't hold up against real business questions
Off-the-shelf AI assistants tend to hallucinate, lose context, or simply don't know your product, policies, or internal data.
We build RAG pipelines and agent architectures specifically around your content — with citation, evaluation, and guardrails built in from the start, not bolted on afterward.
Capabilities
Key capabilities
02
Vector databases & embeddings
Production-grade vector search infrastructure with metadata filtering and hybrid retrieval.
03
AI agents & orchestration
Multi-step agent workflows that call tools, APIs, and internal systems with human-in-the-loop checkpoints where needed.
04
Evaluation & guardrails
Automated evaluation suites, hallucination checks, and content safety layers before anything ships to users.
Process
How we deliver
Every project moves through the same clear steps, so you always know what is happening next.
- 01
Use case definition
We identify the specific questions or tasks the AI system needs to handle reliably.
- 02
Data & retrieval design
Document ingestion, chunking, and retrieval strategy tailored to your content.
- 03
Prototype & evaluate
Rapid prototyping against a labeled evaluation set, not just manual spot-checks.
- 04
Production hardening
Guardrails, monitoring, and fallback handling before the system reaches end users.
- 05
Launch & iterate
Continuous evaluation as usage patterns and source content evolve.
Benefits
What you get
Answers grounded in your data
Responses cite and reflect your actual documentation, not general internet knowledge.
Reduced support load
Internal and customer-facing assistants deflect repetitive questions accurately.
Built for evaluation
Every system ships with a way to measure whether it's actually working.
FAQ
Frequently asked questions
Common questions about our Generative AI & RAG services, cost, and how projects start.
What is RAG, and do we need it?
RAG (retrieval-augmented generation) means the AI first finds the relevant passages in your documents, then answers from them and shows the sources. You need it when the AI must answer about your own products, policies, or data rather than general knowledge.
Which LLM do you use: OpenAI, Claude, or open-source?
We pick the model for each use case based on answer quality, cost, and privacy needs. That can be OpenAI, Anthropic Claude, Google Gemini, or an open-source model you host yourself. The system is built so the model can be switched later.
Is our data safe?
We keep data in your own cloud account where possible, use model APIs that don't train on your data, and add access control so users only see answers from documents they are allowed to read.
How do you stop the AI from making things up?
Answers are grounded in retrieved sources with citations, tested against a set of real questions before launch, and guarded so the assistant says it doesn't know, or hands off to a person, instead of guessing.
What does a generative AI app cost to run?
Running cost is mainly model API usage and hosting. We estimate the monthly usage cost before building and keep it down with caching and smaller models where they are good enough. For the free 15-day MVP, paid AI model usage is covered by you or runs on your own accounts.
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Learn moreReady to talk about your Generative AI & RAG project?
Share a few details about what you're building and we'll reply within one business day. Eligible projects start with a free 15-day MVP.