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What if anyone could chat with a legal document instead of having to read the whole thing?
I wanted to create a tool where users could paste a link to a legal document and instantly start asking it questions like:
Today, is live and working. It took surprisingly little time to build, thanks to .
The stack
Here’s the technology that powers Fineprint
Submitting a document to analyze & index
Step 1 - We first create an index name based on the url and then use Browserless to load and scrape the visible text from the page.
const processDocument = async (url: string) => {
setIsProcessing(true)
setError(null)
const indexName = getIndexNameFromUrl(url)
const content = await scrapeContent(url)
// Index the document using the nextjs API (code in 2. below)
const response = await fetch("/api/document", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
indexName,
title: indexName,
content,
url
})
})
if (!response.ok) throw new Error("Failed to index document")
return { indexName }
}
Step 2 - A new index is created for each document and the extracted “fine print” is indexed using [Ducky.ai’s API]
().
export async function POST(req: Request) {
const { indexName, title, content, url } = await req.json()
// First create the index
await DuckyClient.createIndex(indexName)
// Then index the document
const result = await DuckyClient.indexDocument({
indexName,
title,
content,
url
})
return NextResponse.json(result)
}
...
export type DuckyIndexDocumentParams = {
indexName: string
title: string
content: string
url: string
docId?: string
}
export class DuckyClient {
static async createIndex(indexName: string) {
const response = await fetch(`${DUCKY_API}/indexes`, {
method: "POST",
headers: {
"x-api-key": DUCKY_API_KEY,
"Content-Type": "application/json"
},
body: JSON.stringify({ index_name: indexName })
})
if (!response.ok) {
throw new Error(`Failed to create index: ${response.body}`)
}
return response.json()
}
static async indexDocument(params: DuckyIndexDocumentParams) {
const response = await fetch(`${DUCKY_API}/documents/index-text`, {
method: "POST",
headers: {
"x-api-key": DUCKY_API_KEY,
"Content-Type": "application/json"
},
body: JSON.stringify({
index_name: params.indexName,
title: params.title,
content: params.content,
url: params.url,
doc_id: params.docId || ""
})
})
if (!response.ok) {
throw new Error(`Failed to index content: ${response.statusText}`)
}
return response.json()
}
}
Users ask questions about the document
Step 1 - Fineprint retrieves relevant chunks of content from
const { indexName, question } = await req.json() // From UI
// First, get relevant chunks from Ducky
const duckyResponse = await DuckyClient.retrieve({
indexName,
query: question,
alpha: 1,
topK: 3 // Get top 3 chunks for better context
})
// Prepare context from chunks
const context = duckyResponse.chunks.map((chunk) => chunk.content).join("\n\n")
...
type DuckyRetrieveParams = {
indexName: string
query: string
alpha?: number
topK?: number
}
static async retrieve(params: DuckyRetrieveParams): Promise<DuckyRetrieveResponse> {
const response = await fetch(`${DUCKY_API_BASE}/documents/retrieve`, {
method: "POST",
headers: {
"x-api-key": DUCKY_API_KEY,
"Content-Type": "application/json"
},
body: JSON.stringify({
index_name: params.indexName,
query: params.query,
alpha: params.alpha || 1,
top_k: params.topK || 1
})
})
if (!response.ok) {
throw new Error(`Failed to retrieve documents: ${response.statusText}`)
}
return response.json()
}
Step 2 - The uses OpenAI to synthesise a clear, natural response based on the context.
// Prepare the prompt for OpenAI based on context and question
const messages = [
{
role: "system",
content:
"You are a helpful assistant that answers questions about terms and conditions documents. " +
"Use the provided context to answer questions accurately and concisely."
},
{
role: "user",
content: `Context from the document:\n\n${context}\n\nQuestion: ${question}`
}
]
const response = await fetch(OPENAI_API_URL, {
method: "POST",
headers: {
Authorization: `Bearer ${OPENAI_API_KEY}`,
"Content-Type": "application/json"
},
body: JSON.stringify({
model: "gpt-3.5-turbo",
messages,
temperature: 0.7,
max_tokens: 500
})
})
// Simplified without error handling for readability
return { answer: result.choices[0].message.content }
System Flow
Here’s a simple breakdown of how it all connects:
Why Ducky.ai?
Honestly, Ducky was a game changer for this project.
In a world where retrieval is often the hardest part of building an AI product, Ducky made it easy.
What’s Next
I’m excited to keep building on top of :
This project helped reinforce the idea that you don’t need a giant ML team to build something magical with AI.
With tools like , OpenAI and Next.js/Vercel, developers can prototype and launch powerful AI-powered tools incredibly quickly.
Fineprint was essentially a 1 day project — imagine what’s possible when we really push the boundaries.
If you’ve been thinking about building something with AI — this is your sign to start.
Try it yourself
I wanted to create a tool where users could paste a link to a legal document and instantly start asking it questions like:
- Who owns the content I create?" - Figma ToS
- What happens if I violate these terms?" - Apple T&Cs
Today, is live and working. It took surprisingly little time to build, thanks to .
The stack
Here’s the technology that powers Fineprint
- Frontend | Next.js - React.js & TypeScript
- Backend Scraping | Browserless (headless Chrome)
- Semantic Search & Retrieval | Ducky.ai
- Language Generation | OpenAI
- Hosting | Vercel
Submitting a document to analyze & index
Step 1 - We first create an index name based on the url and then use Browserless to load and scrape the visible text from the page.
const processDocument = async (url: string) => {
setIsProcessing(true)
setError(null)
const indexName = getIndexNameFromUrl(url)
const content = await scrapeContent(url)
// Index the document using the nextjs API (code in 2. below)
const response = await fetch("/api/document", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
indexName,
title: indexName,
content,
url
})
})
if (!response.ok) throw new Error("Failed to index document")
return { indexName }
}
Step 2 - A new index is created for each document and the extracted “fine print” is indexed using [Ducky.ai’s API]
().
export async function POST(req: Request) {
const { indexName, title, content, url } = await req.json()
// First create the index
await DuckyClient.createIndex(indexName)
// Then index the document
const result = await DuckyClient.indexDocument({
indexName,
title,
content,
url
})
return NextResponse.json(result)
}
...
export type DuckyIndexDocumentParams = {
indexName: string
title: string
content: string
url: string
docId?: string
}
export class DuckyClient {
static async createIndex(indexName: string) {
const response = await fetch(`${DUCKY_API}/indexes`, {
method: "POST",
headers: {
"x-api-key": DUCKY_API_KEY,
"Content-Type": "application/json"
},
body: JSON.stringify({ index_name: indexName })
})
if (!response.ok) {
throw new Error(`Failed to create index: ${response.body}`)
}
return response.json()
}
static async indexDocument(params: DuckyIndexDocumentParams) {
const response = await fetch(`${DUCKY_API}/documents/index-text`, {
method: "POST",
headers: {
"x-api-key": DUCKY_API_KEY,
"Content-Type": "application/json"
},
body: JSON.stringify({
index_name: params.indexName,
title: params.title,
content: params.content,
url: params.url,
doc_id: params.docId || ""
})
})
if (!response.ok) {
throw new Error(`Failed to index content: ${response.statusText}`)
}
return response.json()
}
}
Users ask questions about the document
Step 1 - Fineprint retrieves relevant chunks of content from
const { indexName, question } = await req.json() // From UI
// First, get relevant chunks from Ducky
const duckyResponse = await DuckyClient.retrieve({
indexName,
query: question,
alpha: 1,
topK: 3 // Get top 3 chunks for better context
})
// Prepare context from chunks
const context = duckyResponse.chunks.map((chunk) => chunk.content).join("\n\n")
...
type DuckyRetrieveParams = {
indexName: string
query: string
alpha?: number
topK?: number
}
static async retrieve(params: DuckyRetrieveParams): Promise<DuckyRetrieveResponse> {
const response = await fetch(`${DUCKY_API_BASE}/documents/retrieve`, {
method: "POST",
headers: {
"x-api-key": DUCKY_API_KEY,
"Content-Type": "application/json"
},
body: JSON.stringify({
index_name: params.indexName,
query: params.query,
alpha: params.alpha || 1,
top_k: params.topK || 1
})
})
if (!response.ok) {
throw new Error(`Failed to retrieve documents: ${response.statusText}`)
}
return response.json()
}
Step 2 - The uses OpenAI to synthesise a clear, natural response based on the context.
// Prepare the prompt for OpenAI based on context and question
const messages = [
{
role: "system",
content:
"You are a helpful assistant that answers questions about terms and conditions documents. " +
"Use the provided context to answer questions accurately and concisely."
},
{
role: "user",
content: `Context from the document:\n\n${context}\n\nQuestion: ${question}`
}
]
const response = await fetch(OPENAI_API_URL, {
method: "POST",
headers: {
Authorization: `Bearer ${OPENAI_API_KEY}`,
"Content-Type": "application/json"
},
body: JSON.stringify({
model: "gpt-3.5-turbo",
messages,
temperature: 0.7,
max_tokens: 500
})
})
// Simplified without error handling for readability
return { answer: result.choices[0].message.content }
System Flow
Here’s a simple breakdown of how it all connects:
Why Ducky.ai?
Honestly, Ducky was a game changer for this project.
- Simple indexing — I didn’t have to build any custom indexing/search engine.
- Reliable retrieval — The API delivers semantically relevant context with minimal tuning.
- Zero infra management — No hosting embeddings, no worrying about scale.
In a world where retrieval is often the hardest part of building an AI product, Ducky made it easy.
What’s Next
I’m excited to keep building on top of :
- Support PDFs and uploads (not just URLs)
- Highlight referenced sections directly in the document view
- Expand offering and categorise documents: include privacy policies, insurance docs, contracts etc.
- More sophisticated multi-document search (e.g., compare terms between providers)
This project helped reinforce the idea that you don’t need a giant ML team to build something magical with AI.
With tools like , OpenAI and Next.js/Vercel, developers can prototype and launch powerful AI-powered tools incredibly quickly.
Fineprint was essentially a 1 day project — imagine what’s possible when we really push the boundaries.
If you’ve been thinking about building something with AI — this is your sign to start.
Try it yourself
- Visit