This is the practice we are known for. We move data from wherever it lives — databases, portals, spreadsheets, APIs, scraped sources — into one place it can be queried, then build the reporting and the AI layers on top. The order matters: an assistant answering from bad data is worse than no assistant at all.
What this covers
- ETL and ELT pipelines with scheduling, retries and alerting
- Data warehouses and lakehouses on AWS or Azure
- Large-scale web scraping and structured data collection
- Search and analytics with Elasticsearch and OpenSearch
- Executive dashboards and automated reporting
- Retrieval-based AI assistants over your own documents and records
The pipeline layer
We build scheduled pipelines with Apache Airflow, process volume with PySpark, and collect external data with Scrapy at production scale. Every pipeline is monitored: if a source changes shape or a load fails, someone is notified before the morning report goes out wrong. Data quality checks run as part of the job, not as an afterthought.
The AI layer, kept honest
We build retrieval-augmented assistants with LangChain and LlamaIndex over your own content — policy documents, product catalogues, ticket history, listings. Answers cite their sources so a human can verify them. We are equally clear about limits: where a model is unreliable for a task, we will recommend a rule-based approach instead of dressing up a guess.
The essentials
| Typical timeline | 4 to 12 weeks for a first production pipeline |
|---|---|
| Stack we favour | Python, Apache Airflow, PySpark, Scrapy, Elasticsearch, LangChain, LlamaIndex |
| Cloud | AWS (Glue, EMR, Redshift, S3) and Azure; AWS-certified engineers on staff |
| Engagement | Discovery sprint, then fixed-scope delivery or a monthly retainer |
| You receive | Pipeline code, infrastructure as code, runbooks and monitoring dashboards |
Included as standard
The details that decide whether it works.
Airflow orchestration
Dependencies, schedules, retries and backfills managed properly.
Warehouse modelling
Star schemas and incremental loads that stay fast as volume grows.
Cloud native
AWS Glue, EMR, Redshift, S3, Lambda; Azure Data Factory and Synapse.
Search at scale
Elasticsearch clusters tuned for relevance, aggregation and autocomplete.
RAG assistants
Question answering over your documents, with citations and access control.
Cost control
Right-sized infrastructure and a monthly bill you can predict.
Start here
Have a system in mind? Let's scope it together.
Send us a short brief. You get a written scope, a fixed timeline and a clear price — no obligation to proceed.