Data pipelines that make your data usable by AI
We build the ingestion, cleaning, and indexing layers that analytics, RAG systems, and AI agents depend on. The unglamorous plumbing that makes everything downstream work. Fixed scope, $5k–$25k.
Scope your pipeline →See our AI workWhat we build
The data layer everything else needs
Plain list. Boring on purpose — boring is what makes data reliable.
Proof
AI-ready data is our most-requested build
Every AI system we ship needs a data layer underneath. We've built a lot of them.
Document & embedding pipelines
The ingestion and indexing behind private RAG systems — including fully local deployments.
Structured data models
Clean, accessible data that AI agents can actually act on — the layer under Finance OS.
Warehouses that answer questions
Data models designed around the decisions your team needs to make.
Know the number before you commit
Fixed-scope data engineering runs $5k–$25k for most pipeline and warehouse builds: a focused pipeline in 2–3 weeks at the low end, a full ingestion-warehouse-dashboard platform toward the top. Data quality and source complexity are the main cost drivers.
How we build
Reliable beats clever, every time
Data engineering fails silently — a broken pipeline looks fine until the numbers are wrong. We build to catch that.
Assess the sources
We look at your actual data first — its shape, its mess, its volume.
Design for reliability
Idempotent pipelines, quality checks, and monitoring designed in from the start.
Build & validate
Tested against real data, with quality gates that fail loudly, not silently.
Hand over & document
Your team gets the pipelines, the runbook, and the monitoring to operate them.
How we work & why it matters
We combine process, technology, and expertise so your product gets built right—from idea to launch.
We use proven stacks and clear delivery so you get results you can measure.
Technologies we use
We build with the stacks and tools your product needs.
PostgreSQL / SQL
Spark / FlinkCommon questions
Hiring senior data engineers takes months and $150k+/year each; most companies need working pipelines, not a permanent team. A fixed-scope build gets you a production data platform with documentation and handover — then your existing team operates it. Outsource the build, own the result.
We use ELT for modern cloud warehouses (Snowflake, BigQuery) to leverage distributed compute; ETL for near-real-time streaming. Choice depends on latency and cost goals.
Yes. We build bridge pipelines from on-prem to cloud and use Strangler Pattern or phased extraction so migrations happen without business disruption.
Fixed-scope data engineering at Essen Software runs $5k–$25k for most pipeline and warehouse builds: a focused pipeline in 2–3 weeks at the lower end, a full ingestion-warehouse-dashboard platform toward the upper end. Data quality and source complexity are the main cost drivers; scope and price are agreed up front.
Yes — this is increasingly why clients come to us. We build the ingestion, cleaning, and indexing layers that RAG systems and AI agents depend on: document pipelines, embeddings, vector stores, and structured data models. It pairs directly with our RAG development and AI agent work.
Why choose Essen for data engineering
Need a data layer your AI or analytics can trust?
Thirty minutes. Tell us your sources and what you're building on top. We'll tell you honestly how to structure the data layer, what it costs, and how fast it ships.
Scope your pipeline