Ingestion Pipelines
Pipelines for transactional data, statements, documents and reference-data feeds, built to handle the schema drift and format inconsistency that vendor feeds routinely produce.
Service
Financial data engineering
Most AI and analytics projects in finance don't fail on the model. They fail on the data underneath it. We build the pipelines, the warehouse, and the reconciliation and lineage layer that make your data trustworthy enough to build on.
What it is
A retrieval system is only as good as what it retrieves, and a forecasting model is only as good as the history it trains on. In finance, that history is usually scattered across a core banking system, a handful of vendor feeds, PDFs nobody parsed, and a spreadsheet someone maintains manually because the "real" system doesn't capture a field the business needs. Before any model or agent can be trusted, that layer has to be fixed.
We build the data infrastructure underneath finance-AI systems: ingestion pipelines for transactional, document, statement and reference-data feeds; a warehouse or lakehouse layer that stores data point-in-time correct, so a backtest run today doesn't accidentally use next month's restated figures; and entity resolution that recognizes the same counterparty or issuer across feeds that spell it four different ways. Lineage is tracked end to end, so when a number looks wrong, you can trace it back to the source record rather than guessing.
This work is unglamorous, and it's also where most AI-in-finance projects quietly die: a model that looked good on a demo dataset starts producing confidently wrong answers once it hits the real feed, because nobody checked whether the training data leaked future information into the past. We treat the data layer as the first deliverable, not an assumption, and we prove it's correct with validation checks before anything downstream is built on top of it.
What we build
Capabilities inside Financial Data Engineering
02
Point-in-Time Correctness
Data stored and versioned so a query run today returns the values that were actually known at that point in time, not values restated later. This is critical for any backtest or historical model training.
03
Entity Resolution
Matching the same counterparty, issuer or customer across feeds that represent it inconsistently, so downstream aggregation and retrieval are not silently fragmented.
04
Data Lineage & Validation
End-to-end tracking from source record to downstream output, with validation checks that catch a broken feed or a schema change before it reaches a report or a model.
05
Warehouse & Lakehouse Architecture
Schema design and storage architecture sized to your actual query patterns, whether analytical, retrieval, or both, rather than a generic template.
06
Vector Store Integration
Document and structured data indexed for retrieval, wired to stay in sync with the source-of-truth systems rather than drifting out of date.
07
Reconciliation Logic
Automated matching and break identification across systems that are supposed to agree but don't, the same logic that underlies both operational reconciliation and model-training data quality.
How we work
Delivery process
01Data audit
Profile existing sources for completeness, consistency and point-in-time correctness, and identify where entity resolution or lineage gaps exist.
02Architecture design
Design the pipeline, storage and validation architecture against your actual query and retrieval needs, not a generic data-platform template.
03Pipeline build
Build ingestion, transformation and validation pipelines feed by feed, with automated checks at each stage.
04Backfill & validation
Load historical data with point-in-time correctness verified against known-good reference points before anything downstream depends on it.
05Handover & monitoring
Deliver documentation, lineage maps and ongoing data-quality monitoring, so your team can trust and maintain the layer without us.
What to expect
Point‑in‑time correct
no forward-leaking data in backtests or historical training sets
4‑12 weeks
typical build time depending on number of source feeds and remediation needed
Full lineage
every downstream number traceable back to its source record
Frequently asked questions
We already have a data warehouse. Why would we need this?
Most warehouses are built for reporting, not for the point-in-time correctness or entity resolution an AI system needs. We often work alongside an existing warehouse, adding the versioning, lineage and reconciliation layer that makes it safe to build models on top of, rather than replacing it.
How do you handle GDPR when building pipelines that touch customer data?
Pipelines are designed with data minimization and purpose limitation from the start: only the fields the downstream use case needs are retained, access is scoped and logged, and we build in the deletion and access-request handling GDPR requires rather than bolting it on afterward.
What does 'point-in-time correct' actually mean in practice?
It means if you query what a figure looked like on a given date, you get what was actually known and reported on that date, not a value restated weeks later. Getting this wrong is the single most common way a backtest or historical model silently cheats.
Can you work with our existing data engineering team rather than replacing them?
Yes. This is usually a collaboration, not a takeover. We often come in for the specific point-in-time, lineage or entity-resolution problem your team hasn't had bandwidth to solve, and hand over documentation and patterns your team maintains going forward.
Do you support on-prem, or does this require moving data to the cloud?
Both. Pipeline and warehouse architecture can run in your cloud account, on-prem, or a hybrid setup, depending on your data-residency requirements and existing infrastructure.
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Talk to us about Financial Data Engineering
A 30-minute call to scope what a first version would look like against your own data and systems.
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