Intsurfing builds and runs data systems for mid-sized businesses. We work on data pipelines, backend services, unstructured data extraction, and legacy modernization, operating directly inside the client’s infrastructure.
Our Data Engineering Services
Automated ETL pipelines
Get end-to-end pipelines that extract, transform, and load data automatically to feed analytics, APIs, or internal systems.
Scheduled data ingestion
Data arrives when it should, not when someone remembers to trigger a job. We ingest from FTP, SFTP, S3, or HTTP/S on schedule, with retries and pre-processing built in.
Data lakes & warehouses
Get a single place for your data. We model and load data so it stays query-ready, consistent, and usable across teams and tools.
Data pipeline orchestration
Your pipelines follow a clear execution order and run even when issues occur. We control dependencies and retries so one failed step doesn’t derail the entire process.
Data pipeline optimization
We find what slows your pipelines or drives up costs, then fix it. Jobs run faster, scale more predictably, and stop wasting cloud resources.
Monitoring & failure recovery
We add monitoring, alerts, and recovery logic so failures are resolved before they affect downstream systems.
Data quality checks
We surface broken, incomplete, or unexpected data at the pipeline level before it reaches reports, models, or customers.
Validation rules
We define what valid data means for your use case and enforce it in the pipeline. When data breaks those rules, it’s stopped, isolated, or flagged.
Deduplication
Get cleaner datasets, more accurate counts, and fewer issues caused by repeated or conflicting entries.
Data matching
Have a consistent view of the same entity across systems. We apply matching logic that links related records and removes ambiguity from analytics and operations.
Data engineering services in AWS
Keep your AWS data workloads reliable and under control. We design and run data pipelines and backends that match your scale, usage, and cost expectations.
API development for data platforms
Give your systems clean access to data. We build stable interfaces that connect pipelines, services, and applications.
Microservices
Break large data systems into services you can change without side effects. We design microservices that isolate logic, scale independently, and don’t bring the whole system down.
Serverless, containerized architectures
Run data services without managing long-lived servers. We use serverless and Docker-based setups to keep deployments simple and costs predictable.
Data collection from websites
We rely on our own tooling to shorten delivery timelines and reduce the cost of ongoing maintenance.
PDF data parsing
We use AI to pull specific data points across various document layouts and feed clean results into your pipelines.
Image data parsing
We process printed and handwritten content from images across languages and convert it into data your pipelines can work with.
Legacy data system modernization
Reduce the cost and friction of outdated data systems. We clean up pipelines, logic, and dependencies so maintenance stops eating engineering time.
Migration to cloud-native pipelines
Shift away from rigid, hard-to-scale pipelines running on servers. We migrate workloads to cloud-native architectures for better scalability and your platform growth.
Start with a Focused Data Project
We work with one web source and deliver:
- A sample dataset from the site
- Data in structured format (CSV or JSON)
- Estimated cost for full-scale collection
- Estimated timeline for a production setup
When this is a great fit:
- You need proof before budgeting
- You want to understand real complexity before committing
Cost: $0
Duration: 1-5 business days
We automate ingestion for up to 5 vendor data feeds.
Supported sources:
FTP • SFTP • S3 • HTTP(S) • Google Drive
For each feed, we:
- Pull files on a defined schedule
- Unpack ZIP files & decode
- Deliver data to your storage (database, S3, or file drop)
- Trigger the next pipeline step
When this is a great fit:
- Your team manually downloads vendor files
- Different vendors send data in different ways
- You collect files from slow or fragile sources
Cost: $3,000 initial standard setup + support $500 / mo
Duration: 10-15 business days for development
We process up to 10,000 PDFs (contracts, invoices, resumes, court records, and more) and extract the exact data points you need.
- Format number: 1
- File size: up to 0.5 MB
- Deliver data to your storage (database, S3, or file drop)
- Number of pages: 1-2
The result comes back as CSV or another format that fits your systems.AI or OCR used when needed.
Cost: $3,000
Duration: 5–10 business days
NEW PRODUCT
Production-Ready APIs for Your Data Systems
Plenty of free requests every month.
Pay only for usage.
Full control over API keys, usage, and billing from your account.
Address Parsing API
- Validate addresses
- Correct address mistakes
- Geocode input
5,000 free requests / mo
coming soon
Why Companies Choose Intsurfing for Cloud Data Engineering Services
- Delivery to U.S. and EU markets since 2016
- Deep expertise across data-driven industries
- Long-term, embedded collaboration
- Scala, Airflow, Spark, Hive talent in 1-4 weeks
- All systems are built and operated inside your infrastructure
- PII handling, compliance (GDPR, CCPA, HIPAA)
Case Studies in Data Engineering
PDF data extraction with AI for a leading B2B data intelligence platform
Challenge:
Extract data from 18K scanned PDFs in 30 formats.
Solution:
Trained Gemini Vertex AI model for pattern-based extraction. Automated data load and processing with Airflow Dags on Python.
Result:
Text files with business contact data.
Build back-end for distributed web data collection system
Challenge:
To build a data gathering tool for large-scale information collection with minimal setup.
Solution:
Cloud-based, large-scale data extraction system with resource management, proxy handling, and project monitoring.
Entity resolution for B2B data intelligence platform
Challenge:
Unify records from three sources.
Solution:
A pipeline with probabilistic matching and normalization across name, contact, work, and education fields.
Result:
76% dedupe rate at ≥85% match, 400M records matched in 40 mins
Indian voters ETL pipeline for a political data & consulting firm
Challenge:
1 billion voter records, 22 local languages, 36 input formats (PDFs & hand-written forms).
Solution:
OCR text recognition and ML for transliteration, validation against postal data.
Result:
Transliterated, searchable database in a single format.
Legacy address parsing system modernization for people lookup platform
Challenge:
Modernize the address parsing, verification, and cleaning system that struggled with high traffic and 1M dataset.
Solution:
System migration from MSSQL to Redis. Pre-compiled MSSQL queries. New address parsing algorithms. Internal caching, dedupe, and indexing.
Result:
Stable and easily scalable system for 1M+ records 2x faster data processing 40% reduction in processing traffic 12% error reduction
Data ingestion & processing 150M records for people lookup platform
Challenge:
150M records dataset with 1M records daily update.
Solution:
Automated data pipeline with daily data ingestion, unpacking, validation. Data process with Airflow, AWS Glue Jobs on Scala.
Result:
Searchable database in Snowflake cloud-based data storage.
How We Work
Outsourcing
When you have a clearly defined data project and want it delivered end-to-end with a fixed scope, timeline, and outcome.
Managed team
When you need a dedicated data engineering team embedded into your systems and processes, with long-term ownership and ongoing delivery.
Our Tech Stack
Languages
Scala C# .NET Java Python SQL
Backend & APIs
ASP.NET Core Spring Boot FastAPI gRPC REST API Gateway
Data Processing
Apache Spark AWS Glue EMR Dataflow Dataproc
Streaming
Apache Kafka Amazon Kinesis
Containers
Docker Kubernetes
Warehouses
Snowflake Amazon Redshift Google BigQuery
Databases
PostgreSQL Amazon DynamoDB
Orchestration
Apache Airflow Apache NiFi
Insights on Data Engineering
December 21, 2025
A Beginner’s Guide to Entity Resolution: How It Works and When to Use It
September 14, 2025
False Positives & False Negatives: The Critical Importance of Data Quality in Background Checks
September 2, 2025
Amazon Textract vs Anthropic: PDF to JSON Accuracy, Cost, and Scale
June 3, 2025
Scala Interview Question List: A Guide for HRs, Tech Leads & Business Stakeholders
January 17, 2025
How ETL Data Transformation Helps Businesses Convert Unstructured Data into Strategic Decisions
FAQ
Who is Intsurfing a good fit for?
We work with mid-sized companies that have outgrown ad-hoc data setups and need reliable pipelines, integrations, or backend systems without enterprise overhead.
How quickly can a data engineering team start?
A dedicated team is usually ready in 1–4 weeks, depending on roles and scope. For smaller pilot projects, work can start sooner.
What engagement models do you offer?
We work in two ways: a managed team for ongoing delivery and ownership, or project-based outsourcing for clearly defined data tasks with a fixed scope and outcome.
Do you work inside the client’s infrastructure?
Yes. All systems are built and operated inside your cloud environment. You keep full ownership of data, code, and infrastructure at all times.
What kind of data pipelines do you build?
We build scheduled and event-driven pipelines for ingesting, transforming, and delivering data across systems, including ETL, orchestration, monitoring, and recovery.
Do you work with unstructured data like PDFs or images?
Yes. We extract structured data from websites, PDFs, and images, including handwritten and multilingual content, and integrate the output into data pipelines or backend systems.
Can you modernize existing data systems?
Yes. We modernize legacy data pipelines and backends step by step, reducing operational risk while improving reliability, scalability, and maintainability.
What APIs does Intsurfing offer?
We provide production-ready data parsing APIs, including name parsing and address parsing, built and used in real data systems.
How can I start working with Intsurfing?
Many clients start with a small, well-defined pilot project, such as vendor feed ingestion, web data sampling, or PDF parsing.
Do I need to commit long-term upfront?
No. Pilot projects are fixed-scope and low-risk. You move forward only after reviewing results, timelines, and costs.