What We Build and How We Build It
Every service Stackvik delivers is production-grade from day one. We design for scale, build for reliability, and hand over systems your team can own independently.
Data Engineering
Your data is only as useful as the infrastructure that moves and transforms it. We design and build end-to-end pipelines that take data from wherever it lives, transform it into something your business can use, and load it into the platforms your analysts and AI systems need.
WHAT THIS COVERS
- — Pipeline architecture design for batch and streaming workloads
- — ETL and ELT development using Python, Apache Airflow, and dbt
- — Data warehouse and lakehouse setup on Snowflake, Databricks, BigQuery, and Azure Data Factory
- — Medallion architecture with bronze, silver, and gold layer separation
- — Schema design, indexing, and query optimization on PostgreSQL and cloud warehouses
- — Data quality frameworks: null checks, schema drift detection, SLA monitoring
- — Pipeline observability: task-level logging, failure alerting, and data lineage tracking
- — Docker containerization for portable, reproducible pipeline deployment
WHEN YOU NEED THIS
- — Your pipelines break under load or fail silently without alerting
- — Your analysts spend more time cleaning data than analyzing it
- — You are migrating from legacy on-premise databases to a cloud warehouse
- — You need a reliable data foundation before building AI or BI on top
- — Your current ETL is a collection of scripts no one fully understands
OUR APPROACH
We start by understanding your data sources, volumes, and downstream consumers. We design the simplest architecture that meets your requirements without over-engineering. We build incrementally with working pipelines at every stage. We hand over with full documentation, runbooks, and observability so your team can operate independently.
Web Scraping and Data Collection
The data your business needs often does not come with an API. We build production scraping infrastructure that collects data from any web source reliably, at scale, and without getting blocked. From single-site extraction jobs to multi-source data collection platforms running on schedule.
WHAT THIS COVERS
- — Custom scrapers using Scrapy and Playwright for static and dynamic JavaScript-rendered pages
- — Anti-detection infrastructure: rotating proxies, user agent rotation, browser fingerprint management
- — Automotive parts catalog extraction using TecDoc and supplier APIs
- — E-commerce pricing and inventory monitoring across multiple marketplaces
- — Competitor intelligence: product listings, pricing changes, availability tracking
- — Data cleaning and normalization pipelines after extraction
- — Scheduled cloud deployment on Docker with failure alerting and retry logic
- — Delivery to PostgreSQL, Snowflake, BigQuery, or any warehouse of your choice
WHEN YOU NEED THIS
- — You need pricing or inventory data from competitor sites at scale
- — Your automotive parts platform needs TecDoc catalog or supplier feed integration
- — You are building a data product that requires ongoing web data collection
- — Your manual data gathering process is slow, error-prone, or not scalable
OUR APPROACH
We audit the target sites before writing a single line of code. We identify the data structure, JavaScript rendering requirements, rate limits, and anti-bot measures. We build incrementally, validate data quality at every stage, and deploy with monitoring so you know immediately if a scraper breaks. We do not build scrapers that work once and then silently fail.
AI Automation and Agents
Most businesses have manual workflows that could be automated but are too complex for simple if-then tools. We build intelligent automation systems that use AI reasoning to handle multi-step processes, make decisions, and operate autonomously without human handholding.
WHAT THIS COVERS
- — Multi-step workflow automation using n8n and Make.com with custom Python function nodes
- — LLM-powered decision logic using LangChain and direct OpenAI API integration
- — Document processing: classification, data extraction, routing, and summarization at scale
- — Lead enrichment pipelines: web data fetch, LLM analysis, CRM write-back
- — Multi-channel output: database writes, email dispatch, Slack alerts, webhook triggers
- — AI agent architectures with memory, tool use, and multi-agent coordination
- — Error handling, retry logic, and self-monitoring with Slack alerting on failure
- — Full observability: task logs, execution history, and performance dashboards
WHEN YOU NEED THIS
- — Your team spends hours on repetitive data processing that follows clear rules
- — You want to automate customer communication or lead handling using AI
- — You have documents arriving at scale that need classification or data extraction
- — You want AI to make operational decisions based on your business data without manual review
OUR APPROACH
We map the existing manual workflow before designing the automation. We identify the decision points, the data inputs, the edge cases, and the failure modes. We build the automation incrementally with human checkpoints until confidence is established, then move to full autonomy. Every system we deploy is self-monitoring so problems surface immediately.
RAG Systems and Knowledge Pipelines
Generic AI gives generic answers. RAG systems ground your AI in your own data, your own documents, and your own knowledge base. The result is an AI that gives accurate, specific, cited answers based on what your business actually knows, not what the model was trained on.
WHAT THIS COVERS
- — Full document ingestion pipeline: PDF, Word, HTML, and structured data sources
- — Text chunking strategy design with overlap and context preservation
- — Embedding generation using OpenAI, Cohere, or open-source embedding models
- — Vector store setup and management using pgvector, Pinecone, Weaviate, or Chroma
- — Semantic retrieval with cosine similarity search, metadata filtering, and hybrid search
- — LangChain orchestration with prompt engineering for accuracy and citation formatting
- — Evaluation frameworks to measure retrieval precision and answer groundedness
- — FastAPI deployment with authentication and rate limiting for production use
WHEN YOU NEED THIS
- — You want employees to query internal documentation, policies, or knowledge bases using natural language
- — You need AI to answer questions grounded in your proprietary data rather than general knowledge
- — You are building a customer-facing AI assistant that needs accurate, sourced answers
- — Your team is losing time searching through large document libraries for specific information
OUR APPROACH
We start by auditing your document corpus and understanding your query patterns. We design the chunking and retrieval strategy based on your specific content type. We test retrieval quality rigorously before connecting the LLM layer. We tune for accuracy, not just for impressive demos. Every RAG system we build is evaluated against a ground truth set before going live.
Ready to build something that actually works in production?
Tell us about your data challenge. We will respond within 24 hours with a clear assessment and a practical plan.