SourceRivet

Production-Grade Data Extraction & Automation Pipelines.

We engineer robust scraping architectures and API integrations that navigate complex data sources, stream real-time results, and scale without breaking. Built for teams that need clean data—not maintenance headaches.

Scope Your Project Explore technical proof
Expertise in Python Playwright asyncio WebSockets
pipeline.live LIVE
STATUSProcessing
SUCCESS RATE99.9%
LATENCY184ms
Sources Web · API · Portal
SourceRivet Extract · Validate
Structured JSON · DB · CRM
SUCCESS

Validated 2,408 records

STREAM

WebSocket delivery active

CHECK

Schema integrity confirmed

PROVEN IN PRODUCTION

200+ websites scraped across public sites, APIs, portals, and apps.

200+
Websites scraped
200K+
Records processed daily
20K+
Profiles delivered in 48 hours
Live
WebSocket data delivery
INFRASTRUCTURE

Built for the edge cases.

Reliable data collection requires more than a script. We design the orchestration, recovery, validation, and delivery layers that keep production pipelines running.

01

Advanced Bot Handling

Resilient browser orchestration, proxy strategy, session management, and challenge handling for collecting permitted data from complex sources.

TurnstileProxy PoolsPlaywright
02

Real-Time Streaming

Move beyond batch files with live WebSocket pipelines designed for instant capture, transformation, retry protection, and delivery.

WebSocketsasyncioRetry Queues
03

Data Cleansing & Formatting

Transform raw HTML, documents, and media into validated records using deterministic rules, OCR, and LLM-assisted enrichment.

Gemini APIOCRJSON Schema
SELECTED WORK

Systems built for real-world pressure.

Not demo scripts—working data systems designed around scale, unreliable sources, strict schemas, and the way your team actually uses the output.

CASE STUDY 01 · MOBILE DATA

20,000+ account profiles extracted and delivered in under 48 hours.

A mobile-first collection workflow captured account data, validated every record, and delivered clean results directly into the client’s database—on an urgent timeline.

20K+
profiles
<48h
delivery
Direct
database load
Mobile ExtractionAuthenticationDatabase Delivery
mobile_pipeline.live Processing
Mobile appAuthenticated data
SourceRivetExtract · validate
Client DBStructured records
Schema validated20,000+ records0 failed batches
CASE STUDY 02 · REAL ESTATE

Real estate opportunity engine

Zillow listings matched against AirDNA projections, normalized by address, and filtered into a focused shortlist of high-potential rental opportunities.

OUTPUTAutomated opportunity shortlist
Cross-Source MatchingPythonData Validation
CASE STUDY 03 · REAL TIME

Real-time TikTok comment pipeline

Live comments captured, transformed into structured events, and delivered over WebSockets with retry logic to keep downstream applications current.

OUTPUTLive, structured event stream
Live ExtractionWebSocketsRetry System
property_match.json 
{
  "property_id": "SR-84720",
  "source_match": {
    "zillow": "verified",
    "airdna": "verified"
  },
  "address": "Miami, FL",
  "valuation": 685000,
  "annual_revenue": 76420,
  "confidence": 0.984,
  "status": "validated"
}
Schema valid184ms
99.9%pipeline uptime
200K+records / day
TECHNICAL PROOF

Built to handle complexity.

From high-volume real estate data matching to checkpointed regulatory databases, we build for the failure modes that ordinary scripts ignore.

✓

Cross-source property matchingZillow and AirDNA records normalized into a consistent, validated schema.

✓

Checkpointed data collectionSQLite-backed NASBA CPAverify workflows that resume safely after interruptions.

✓

Delivery that fits your stackStructured data shipped to JSON, databases, sheets, APIs, or live WebSockets.

Discuss a similar system →
PIPELINE ARCHITECTURE

What “production-grade” looks like.

Each stage owns a specific failure mode—from unstable sessions at the source to interrupted delivery downstream.

01 · SOURCE
Target source Session & challenge handling Cloudflare · Turnstile
02 · EXTRACT
Extraction engine Browser and request orchestration Playwright · Selenium
03 · RECOVER
SQLite checkpoint Resume without duplicated work State · Retry · Validation
04 · DELIVER
Live delivery Clean records into your stack WebSocket · REST API
INTEGRATION ECOSYSTEM

Delivery that fits your stack.

Route validated records into automations, databases, internal tools, or live applications without another manual handoff.

Explore infrastructure →
Validated data Webhook · API · Batch
n8nn8nWebhook workflows
✦ZapierApp automation
MMakeVisual scenarios
DBDatabasesPostgreSQL · SQLite
</>REST APIsDirect application sync
{ }FilesJSON · CSV · Sheets
CLIENT REVIEWS

Trusted when the data is difficult.

Feedback from clients who needed fast delivery, careful execution, and dependable output—not another fragile scraper.

Within less than two days, he was able to scrape more than 20,000 account details and present them in a perfect format. 1000/10.
Verified client feedbackMobile app extraction
Muhammad hit every deadline and was even early for two of them. 10/10.
Verified client feedbackReal estate automation
It’s been a pleasure, and I look forward to future collaborations.
Verified client feedbackReddit data extraction
Muhammad Shahbaz Akhtar, founder of SourceRivet
200+websites & sources handled
FOUNDER & ENGINEERING PHILOSOPHY

Systems should survive the real world—not just the first successful run.

I’m Muhammad Shahbaz Akhtar, founder and data systems engineer at SourceRivet. After working across more than 200 websites, portals, APIs, and applications, I’ve learned that extraction is only the beginning. The real engineering starts with interruptions, changing structures, inconsistent records, and delivery failures.

“I design every pipeline around the edge cases: preserve completed work, make failures observable, and keep recovery practical.”
01Design for interruptionCheckpoints and recovery are part of the architecture.
02Treat edge cases as coreUnusual records are handled before they become production failures.
03Troubleshoot the full pathSource, transformation, storage, and delivery are examined together.
Discuss your system with the founder →
HOW WE WORK

From difficult source to dependable delivery.

A focused four-step engagement keeps scope clear, proves feasibility early, and moves your project into production without unnecessary meetings.

  1. 01

    Share the source

    Send the website, portal, API, app, or documents—and explain the business outcome you need.

  2. 02

    Approve a sample

    We confirm feasibility, define the schema, and provide sample records before the full build begins.

  3. 03

    Build and validate

    The extraction, quality checks, retries, and recovery logic are engineered as one reliable system.

  4. 04

    Deliver and maintain

    Receive clean data through CSV, JSON, database, API, or WebSocket—with support when sources change.

TECHNICAL FAQ

Questions technical buyers ask before production.

Clear answers about maintenance, reliability, access constraints, and what happens after the first successful run.

Ask a technical question →
What happens when the target website changes its DOM?

Source-specific selectors and parsing rules are isolated from the rest of the pipeline, so they can be repaired without rebuilding delivery, storage, or validation. For maintained systems, changes are investigated against saved examples and current source behavior.

What is the repair SLA if a production pipeline breaks?

Response and repair targets are agreed before launch based on run frequency and business criticality. Critical daily or live pipelines can include priority maintenance; no fixed SLA is promised unless it is explicitly included in the engagement.

How are proxy rotation and sessions managed?

The strategy is source-specific and rate-aware. Stable session identity, controlled concurrency, proxy health, and retry limits are preferred over unnecessary IP churn. Collection is designed for authorized and permitted use.

How do you approach Cloudflare and Turnstile challenges?

Feasibility is evaluated first. Depending on the authorized access available, the system may use stable browser sessions, client-provided credentials, permitted APIs, or human-in-the-loop challenge handling. We do not promise access where a source does not authorize it.

Which delivery formats and integrations are supported?

Common destinations include JSON, CSV, Google Sheets, SQLite, PostgreSQL, REST APIs, webhooks, and live WebSockets. Webhooks can trigger tools such as n8n, Zapier, and Make.

Can you maintain or repair an existing scraper?

Yes. Existing Python, Selenium, Playwright, or API-based projects can be audited for the actual failure point, then repaired or restructured where the current design makes maintenance unreliable.

LET'S TALK SYSTEMS

Ready to automate your data pipeline?

Book a 15-minute technical scoping call. No sales reps—just an engineering discussion about your extraction needs.

Book a Technical Call Usually replies within one business day.