retrieval with citations

Your database, queryable in any language.

AI agents that read your real content and answer your users with sources, not hallucinations.

How it works

01

Connect your data sources

CMS, database, API: wherever your content lives.

02

I build the retrieval layer

RAG, embeddings, vector search, tuned to your content.

03

Embed the chat

Drop the widget on your site, or expose it via API.

Use cases

Internal AI chats connected to real product data

Two examples of custom assistants I worked on: one for editorial research, one for DeFi analytics and chart generation.

Cryptoast internal tool

Cryptoast: news, guides and rankings, queryable

News articles, long-form guides, live crypto rankings, prices, market caps and volumes are all searchable from one internal chat, with citations back to the source content.

Example question

What's Solana's market cap, and which guide should I read first?

Example answer

Solana sits at $X.XB market cap as of today. For a primer, start with "What is Solana?" then move on to the staking guide.

cryptoast.fr/solanacryptoast.fr/guides/solana-stakingapi/markets/SOL
Cryptoast internal AI chat mockup placeholder

DefiLlama client work

DefiLlama: LlamaAI turns DeFi data into answers and charts

I designed LlamaAI and participated in its creation. It can fetch the DefiLlama database, answer analytics questions and generate unique charts directly from the data.

Example question

Create a chart with the top 10 protocols ranked by daily revenue.

Example answer

LlamaAI can query DefiLlama data, rank the top protocols by daily revenue, then render the result as an interactive React chart component instead of a static answer.

api/protocolsapi/fees-and-revenuereact/chart-component
LlamaAI chart generation placeholder

What's included

Custom retrieval pipeline

Tuned to your content: chunking, embeddings, ranking.

Source citations on every answer

Every claim links back to the article, doc or row it came from.

Embeddable widget

Drop in a single script tag, or query via API.

Monitoring + iteration

I watch real questions and improve the retrieval over time.

Pricing

Setup

$5,000–10,000

One-off. Depends on data sources, volume, and complexity of the retrieval pipeline.

Running costs

$150–600/mo

API + vector DB hosting. Scales with traffic; capped to a budget you set.

Tell me about your project

A few sentences is enough to start. I reply within 24 hours.