Customers

How teams use Citrusiq to automate data workflows

Companies across industries use Citrusiq to collect, process, and act on web data — replacing manual workflows with reliable, intelligent pipelines.

Web Sources

Extraction

Processing

AI Analysis

Structured Dataset

running
processing
complete
pipeline run #2,341
Who uses Citrusiq

Built for every team that runs on data

From AI startups to enterprise data teams — any team that needs structured, automated web data has a use case with Citrusiq.

AI Startups

Training data at the speed of iteration

AI teams use Citrusiq to continuously collect domain-specific web content, enforce schemas, and deliver labeled datasets directly into their training pipelines — without a dedicated data team.

  • Domain-specific content collection at scale
  • Automated deduplication and quality scoring
  • Schema-enforced output for training pipelines
  • Continuous refresh for RAG and fine-tuning

10×

faster dataset creation

Zero

manual data wrangling

Pipeline active
run #2,341 — in progress
Data Teams

Structured pipelines without the engineering overhead

Data engineers and analysts use Citrusiq to replace brittle scraping scripts with maintainable, scheduled pipelines that deliver structured data directly to their warehouses.

  • Replace fragile scripts with managed pipelines
  • Deliver data to warehouses on any schedule
  • Monitor pipeline health with status dashboards
  • Schema versioning and backward compatibility

90%

less pipeline maintenance

Daily

scheduled delivery

Pipeline active
run #2,341 — in progress
Market Intelligence

Real-time intelligence on competitors and markets

Strategy and research teams use Citrusiq to monitor competitor websites, pricing pages, and job boards — automatically summarizing changes and delivering alerts when signals emerge.

  • Detect website changes as they happen
  • AI-powered summarization of competitor moves
  • Automated alerts to Slack and email
  • Structured intelligence reports on a schedule

<1hr

time-to-alert on changes

100s

of sources monitored

Pipeline active
run #2,341 — in progress
Growth & Sales

Lead lists that arrive already enriched

Sales and growth teams use Citrusiq to automate the entire lead research process — from identifying targets to enriching contacts with firmographic data and delivering them to the CRM.

  • Enrich contacts with company and role data
  • Automated delivery to CRM on daily schedule
  • Custom qualification rules and scoring
  • Zero manual research for outbound teams

3hrs

saved per rep per day

Fresh

data every morning

Pipeline active
run #2,341 — in progress
Product Teams

Power AI product features with reliable web data

Product teams building AI-native applications use Citrusiq as the data layer — structured, current web data flowing into their systems via API to power search, recommendations, and agents.

  • Real-time web data via REST API
  • Structured output matching your product schema
  • Powers search, recommendations, and AI agents
  • Reliable SLA-backed data delivery

API

delivery to any system

Live

data refreshes

Pipeline active
run #2,341 — in progress
Workflow Stories

Real pipelines, real outcomes

Explore how specific teams use Citrusiq pipelines — from data collection to structured output delivery.

Sales & GrowthSaaS

Lead Research Automation

Automated enrichment pipeline that takes a prospect list, fetches company and role data from the web, scores leads using AI, and delivers ready-to-work lists to the CRM every morning.

Results

  • 3 hours saved per rep per day
  • Ready-to-work lists in CRM by 8am
  • Zero manual research required

Pipeline

Prospect CSV
Web Enrichment
AI Scoring
Filter & Rank
CRM Delivery
output sampleschema valid
{
  "company": "Acme Corp",
  "domain": "acme.com",
  "employees": 320,
  "funding_stage": "Series B",
  "icp_score": 94,
  "contact_email": "vp@acme.com"
}
source
ai
process
output
Market IntelligenceEnterprise Software

Competitor Monitoring Pipeline

Hourly monitoring pipeline that scans competitor websites for pricing changes, new feature announcements, and job postings — triggering Slack alerts and updating the intelligence dashboard automatically.

Results

  • Changes detected within 1 hour
  • Automatic Slack alerts on high-relevance signals
  • Full audit trail of competitor activity

Pipeline

Competitor Sites
Change Detector
AI Summarization
Relevance Filter
Slack + Dashboard
output sampleschema valid
{
  "competitor": "RivalCo",
  "change_type": "pricing_update",
  "detected_at": "2025-02-14T09:12:00Z",
  "summary": "Pro plan price increased $20/mo",
  "relevance_score": 0.97
}
source
ai
process
output
Research & StrategyFinance

Market Intelligence Data Collection

Scheduled research pipeline that collects funding news, company profiles, and earnings data from across the web — structuring and delivering formatted intelligence reports every week.

Results

  • Weekly structured intelligence reports
  • Coverage expanded 10× without new headcount
  • Data delivered directly to the warehouse

Pipeline

News & Filings
Entity Extraction
AI Signal Analysis
Data Structuring
Report + Warehouse
output sampleschema valid
{
  "company": "NovaTech",
  "funding_round": "Series A",
  "amount_usd": 12000000,
  "investors": ["a16z", "Sequoia"],
  "signal_score": 0.88,
  "report_date": "2025-02-10"
}
source
ai
process
output
ML EngineeringAI / ML

AI Training Dataset Creation

Continuous collection pipeline that gathers domain-specific web content, deduplicates and quality-scores each record, enforces the training schema, and pushes clean datasets to the model pipeline.

Results

  • 10× faster dataset iteration
  • Automated deduplication and quality scoring
  • Schema-enforced output for training pipelines

Pipeline

Domain Sources
Content Extraction
Quality Scoring
Schema Enforcement
Training Pipeline
output sampleschema valid
{
  "record_id": "doc_00814",
  "source_url": "https://...",
  "content_tokens": 1842,
  "quality_score": 0.93,
  "dedup_hash": "a3f9c...",
  "label": "technical_documentation"
}
source
ai
process
output
Industry Use Cases

Every industry has a pipeline

Structured web data powers decisions across every vertical — from e-commerce pricing to financial research.

Hourly

E-commerce

Competitor Pricing Intelligence

Monitor competitor pricing pages on a daily or hourly schedule. Structured pricing data flows into dynamic pricing engines — no manual collection.

Competitor Sites
Price Extraction
Change Detection
Pricing Engine

price monitoring

<1hr

SaaS

Competitor Feature Monitoring

Track feature releases, pricing changes, and job postings from competitor websites. AI summarizes changes and routes alerts to product and strategy teams.

Competitor Sites
Change Detector
AI Summary
Slack + Dashboard

change detection

10×

AI / ML

Training Dataset Generation

Collect, clean, and label domain-specific content at scale. Deduplication and quality scoring built in. Output delivered directly to training infrastructure.

Web Sources
Content Extract
Quality Score
Training Pipeline

dataset velocity

Daily

Real Estate

Property Data Aggregation

Aggregate property listings, pricing history, and market trends from multiple listing portals into a single, normalized database on a continuous schedule.

Listing Portals
Data Extraction
Normalization
Property Database

market refresh

Weekly

Finance

Financial Data Analysis

Collect earnings reports, funding announcements, and market signals from news and financial portals. Deliver structured datasets to research dashboards weekly.

News & Filings
Entity Extraction
AI Analysis
Research Dashboard

structured reports

Real-time

Recruiting

Talent Intelligence Pipeline

Aggregate job postings and hiring signals from competitor websites and job boards. Infer headcount growth, strategic priorities, and hiring trends automatically.

Job Boards
Role Extraction
AI Classification
Talent Dashboard

hiring signals

Full Pipeline View

Observable at every step

Every Citrusiq pipeline run is tracked node-by-node with real-time status, throughput metrics, and full log output.

Pipeline nodes

Web Sources

240 target URLs active

Citrusiq Extractor

JS render + auth handling

Parser

Cleaning batch #14 — 880 records

AI Processing

Classifying 1,240 records

Structured Dataset

12,840 records validated

Export → API / DB

Awaiting upstream

citrusiq — pipeline run #2,341
Running
09:14:02[INFO]Pipeline run #2,341 started — 240 targets queued
09:14:05[INFO]extractor: spawned 8 Chrome workers
09:14:29[INFO]parser: batch #14 received — 880 records
09:14:31[WARN]rate-limiter: backing off target #87 (429 received)
09:14:48[INFO]ai-processing: 1,240 records queued for classification
09:15:03[INFO]schema-validator: 12,840 records passed — 0 rejected
09:15:04[INFO]export-handler: waiting for ai-processing to complete
09:15:11[INFO]throughput: 1,847 records/min — p99 latency 340ms
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