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We connect data from your products, business systems and documents, then turn it into a reliable platform for reporting, operations and AI. Our data engineering services cover the full lifecycle, from source assessment and architecture to production pipelines, quality controls and ongoing support.
Most data problems cross several systems at once. We build the pipelines, storage, models and controls required to make that data usable across the business.
Our data integration engineering services connect product databases, SaaS tools, CRMs, ERPs, IoT devices and analytics platforms through monitored ETL or ELT pipelines.
We design a warehouse, lakehouse or relational data platform around the questions your teams need to answer, with raw, cleaned and published data kept separate.
We use OCR and extraction workflows to turn contracts, leases, invoices, forms and scanned files into structured, searchable records.
We remove duplicates, standardise formats, flag missing values and validate every load before unreliable data reaches a report, application or model.
Our data analytics engineering services create trusted reporting layers for Power BI, Looker, Streamlit or Dash, so teams no longer maintain separate exports and spreadsheets.
Our data science engineering services prepare training datasets, features and document indexes for models and assistants, with versioning that traces outputs back to the underlying data.
The need usually becomes clear when disconnected data begins slowing down reporting, product development or operational decisions.
Teams export data from several systems and rebuild the same spreadsheet every week.
The work
Customer, sales and operational records live in tools that use different formats and identifiers.
The work
Leases, invoices, forms or scans contain data that cannot be searched, analysed or used in automated workflows.
The work
A model or assistant is planned, but its data is incomplete, duplicated, inconsistent or unlabelled.
The work
Slow queries, failed loads and rising cloud costs make the existing data platform difficult to scale.
The work
Our data engineering consulting services begin with the business questions and source inventory.
We identify the systems and files that hold the required data, how they can be accessed, who owns them and which reports or workflows depend on them.
Deliverables: source map, priority use cases and data quality findingsWe select the storage, processing and integration approach based on data volume, required freshness, security constraints and your existing cloud environment.
Typical technologies: PostgreSQL, Snowflake, BigQuery and DatabricksWe build extraction and loading workflows for each source, including document processing where data arrives in PDFs or scans.
Typical technologies: Python, Airflow, Prefect, Amazon Textract and OCRmyPDFWe transform raw data into documented business entities and add automated checks for completeness, uniqueness, consistency and freshness.
Typical technologies: SQL, dbt and Great ExpectationsWe connect validated data to reports, applications and APIs, with access controlled according to each user’s role.
Typical technologies: Power BI, Looker, Streamlit and FastAPIYour team can take over the platform with its code and documentation, or we can continue monitoring pipelines, resolving failures and adding new sources.
A data platform should be designed around the decisions it supports, not around a list of tools. Before committing to the build, we validate four factors that determine the project’s value and feasibility.
We define which reports, workflows, product features or AI systems need the data and what should improve once it becomes available.
We confirm how each system can be accessed, how frequently its data changes and whether historical records are complete enough for the intended use.
We establish who decides what key metrics and entities mean, so the platform does not automate conflicting definitions from different teams.
We account for permissions, personal data, audit requirements, expected volumes, monitoring and the people who will maintain the system after launch.
Hire data engineersCoding agents reduce the manual work involved in writing connectors, transformations, tests and documentation. Senior engineers remain responsible for architecture, business definitions, data security and the accuracy of published outputs.
AI-native product developmentAgents help draft source mappings, pipeline code and transformation models, allowing engineers to spend more time resolving exceptions and reviewing data behaviour.
Checks for schemas, missing values, duplicates and transformation logic can be created alongside the pipeline instead of being added after failures appear.
Before a new table replaces an established report, both outputs run in parallel and the people who use the numbers confirm that the results match.
We choose the stack based on your current cloud, data volume, processing speed, security requirements and the systems the platform must support.
Stores raw files, structured records and analytics-ready datasets in an architecture designed for reliable access and future growth.
Moves data between source systems and the platform, manages dependencies and keeps scheduled or real-time processing workflows running.
Converts raw data into consistent business entities and checks it for missing values, duplicates, schema changes and processing errors.
Converts raw data into consistent business entities and checks it for missing values, duplicates, schema changes and processing errors.
Turning commercial property documents into searchable data
AI and data platformElysium
Leases, loans and amendments reach Elysium as unstructured files. Our dedicated team owns the extraction pipeline that turns them into data analysts can query, with Amazon Textract, OCRmyPDF, Supabase and S3 in its stack, plus the application on top and CI/CD.
Read the Elysium case studyThe main cost factors are the number and condition of the data sources, how frequently the data must be updated, and the complexity of the target platform. After the initial source assessment, we provide a written scope, delivery sequence and estimate.
When comparing data engineering services companies, look beyond their ability to build pipelines. The right partner should also define the architecture, validate business rules, implement quality controls, manage access and leave your team with documented code it can operate.
We usually begin with one valuable reporting, operational or product use case and connect only the sources it requires. This allows users to work with the first validated output before the entire platform is complete. The timeline depends primarily on source access and data quality.
The right choice depends on your data volume, formats, reporting requirements and existing infrastructure. We recommend the simplest architecture that meets the current need without creating unnecessary limits or complexity.
Yes. We build OCR and extraction workflows for specific document types, standardise the extracted fields and send uncertain results for human review.
You retain ownership of the platform and its data. Pipeline code, transformation logic, infrastructure configuration and documentation remain in your repositories and cloud environment.
Yes. We can monitor pipeline health, resolve failures, manage infrastructure costs, update transformations and connect new sources. We can also document and hand over the platform to your internal team.
We keep the platform in your cloud environment, restrict access by role and design the architecture around your security, compliance and regional requirements. We can sign an NDA before reviewing source data in detail.
Once the data foundation is in place, it can support reporting, customer-facing features and AI systems without creating a separate data flow for each use case.
Share the systems involved, the data they hold and the report, workflow or product you want to support.
We will identify the first sources to connect and propose an architecture, delivery plan and estimate.
We sign an NDA before discussing your data in detail. We reply within 24 hours.
