Service · Data
The trustworthy data layer your AI and BI actually run on.
One clean, documented, owned data layer — pipelines, warehouse or lakehouse, quality, governance, and dashboards — built in your own cloud, with full IP and steady-state in 4–8 weeks.
One source of truth
The real problem
Why the numbers never agree — and every AI project stalls on the data.
Because the data layer underneath was never engineered. Finance reports one revenue number, the dashboard shows another, and a pipeline breaks on a Friday nobody notices until Monday.
Then a machine learning or AI initiative discovers the real project is six months of untangling where the data lives and whether it can be trusted. The model is rarely the hard part — the data layer is.
A year, on average, is what poor data quality costs organizations.
Gartner, data quality ↗
Of enterprise working time lost to non-value-added tasks caused by poor data quality.
McKinsey, 2019 survey ↗
Where it pays
Where data engineering actually pays — and what each delivers.
Not one deliverable — the set of capabilities that make data usable, each earning its place against a specific problem.
Data pipelines & integration
Moves data from every source system into one place on a reliable schedule, in a consistent shape.
One source of truth instead of brittle manual exports.
Data warehouse & lakehouse
Builds the central cloud store, modeled so it's fast to query and cheap to scale.
Analytics that run in seconds on data you can afford to keep.
Business intelligence & dashboards
Turns the warehouse into self-serve dashboards the business reads on its own.
Decisions made on current numbers, not a week-old slide.
Data quality & master data
Validates records as they flow and resolves the same customer or product appearing five ways across systems.
Trustworthy data and far less time reconciling it.
Real-time & streaming data
Builds streaming pipelines for data that's only useful fresh, processed as it arrives.
Alerts that fire in seconds, not after the nightly batch.
Data governance & lineage
Documents where each field comes from, who may see it, and how it's defined.
An answer to "where did this number come from?"
As of June 2026 · revisit quarterly
What a real data layer does for the business — the measured impact.
Independent industry findings — cited as third-party evidence, not Silicon Prime's own client results.
Cost of bad data. Per year, on average, is what poor data quality costs organizations.
Gartner, data quality ↗
Time wasted. Of enterprise time spent on non-value-added tasks because of poor data quality.
McKinsey, 2019 survey ↗
Data-driven wins. More likely to acquire customers, and to be profitable, respectively, are data-driven organizations.
McKinsey, 2025 ↗
What's included
What our data engineering services cover.
The difference between a data layer the whole business trusts and a tangle of brittle exports nobody owns.
Data architecture & strategy
We design the target architecture — warehouse or lakehouse, batch or streaming — sized to your real volume and budget, with the honest "you don't need a lakehouse for this" call included.
Pipeline & ingestion engineering
We build the ELT/ETL pipelines that pull from every source on a reliable schedule, with retries, alerting, and tests — so a broken feed pages someone instead of poisoning a report.
Warehouse, lakehouse & modeling
We build the central store and model it for the questions the business actually asks — fast to query, documented, and structured so analysts can self-serve.
Data quality, validation & master data
We validate at ingestion, build the quality rules and monitoring, and resolve duplicate and conflicting records — so what lands in the warehouse can be trusted.
Business intelligence & analytics
We build the dashboards, semantic layer, and reporting — defining each metric once so "revenue" and "active user" mean one thing everywhere — on the tools your teams already use.
Streaming, governance & enablement
Where data must be fresh we build streaming pipelines; across all of it we document lineage and access, instrument for freshness and cost, and train your team to run it.
What you get — all assigned to you under full work-for-hire IP
How it runs
How a data engineering engagement runs.
The same delivery model behind our AI development work — one accountable lead, fixed scope, no handoffs.
STEP 01
Map
Inventory the sources, the questions to answer, and the quality and freshness requirements.
Output: a target architecture & the success metrics
STEP 02
Model
Design the schema, pipeline plan, and metric definitions in your own cloud tenant.
Output: a data model & a documented pipeline design
STEP 03
Build
Engineer the pipelines, store, quality checks, and dashboards behind tests and data contracts — each layer validated before the next depends on it.
Output: a working, tested data platform
STEP 04
Operate & enable
Instrument for freshness, quality, and cost, set the alerts, and train your team to run it.
Output: a production data layer & a team that owns it
Proof
The discipline behind a data layer you can build on for years.
A data platform is only worth what the discipline underneath it can sustain. We carried Bridge Athletic since 2012 through repeated re-platforming with the data-driven platform never going offline, now used by USC, the LA Rams, and MLB and MLS teams.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering. We'll tell you plainly when you don't need the platform you came in asking for.
Data-driven platform · 12+ years
Bridge Athletic — operated and modernized since 2012 without the data-driven platform ever going offline.
Operating a live platform 12+ years without downtime is the discipline a trustworthy data layer demands.
Restaurants · 200+ locations
BJ's Restaurants — held at twice-a-week releases with zero critical defects across four years.
The same evals-before-launch, monitor-after rigor a data layer runs on.
Why build your data platform with us.
The foundation under your AI, done right. Most AI projects stall on the data, not the model. We build the layer that machine learning and AI infrastructure run on.
Quality and lineage are the product, not a phase. We treat data quality, freshness, and "where did this come from?" as measured, monitored properties — especially in fintech and healthcare.
Founder-led, one accountable lead. No handoffs — the person who scopes the platform answers for it.
Built to transfer. Pipelines, models, dashboards, and documentation assigned to you; your team trained to operate and extend the platform. You own the asset, not a dependency.
Where it earns its keep
Where a trustworthy data layer earns its keep first.
Fintech
Reconciled transaction data, real-time fraud pipelines, and audit-grade lineage on every regulated figure.
Fintech software →Healthcare
Unified patient and operational data inside HIPAA-compliant architectures, every field documented.
Healthcare software →Ecommerce & retail
One source of truth across orders, catalog, and behavior, powering live dashboards and recommendation features.
Ecommerce software →Multi-site operations
Consolidated reporting so every site and the head office read the same reconciled numbers, not five conflicting spreadsheets.
Enterprise apps →Questions buyers ask before they hire.
Thirty minutes · no pitch deck
Ready to build a data layer your whole business can trust?
Bring the problem — reports that disagree, an AI project stalled on the data, a warehouse too slow or too costly — and we'll tell you honestly what the data layer needs and what it costs.