The trustworthy data layer your AI and BI actually run on.
We build the foundation everything else sits on: the pipelines that move your data, the warehouse or lakehouse that holds it, the quality and governance that make it trustworthy, and the dashboards that turn it into decisions.
One clean, documented, owned data layer — built in your own cloud, fixed scope, full IP, steady-state in 4–8 weeks.
Because the data layer underneath was never engineered. Finance reports one revenue number, the dashboard shows another, and a third lives in a spreadsheet someone maintains by hand.
A pipeline silently breaks on a Friday and nobody notices until Monday’s report is wrong. Then a machine learning or AI initiative kicks off and discovers the real project isn’t the model — it’s six months of untangling where the data lives and whether it can be trusted.
Data engineering services exist to remove that tax: to build one trustworthy data layer so the reports reconcile, the models have something real to learn from, and the question “which number is right?” stops being asked.
This isn’t one deliverable. It’s the set of capabilities that make data usable, each earning its place in a specific, recurring problem. For each: what it does, the benefit it produces, and a one-line illustration of the help.
Moves data from your source systems — apps, databases, files, third-party APIs, event streams — into one place on a reliable schedule, transformed into a consistent shape. Benefit — one source of truth instead of brittle manual exports, so reports stop disagreeing and nobody rebuilds the same extract by hand each month.
For example, sales, support, and billing data that used to live in three disconnected tools land in one warehouse every morning, so a “total active customers” number means the same thing in every report.
Designs and builds the central store — a cloud data warehouse or lakehouse — modeled so it’s fast to query and cheap to scale. Benefit — analytics that run in seconds on data you can actually afford to keep, instead of queries that time out or a bill that balloons.
For example, a five-year trend that used to crash the production database now returns from the warehouse in seconds, without slowing the app it came from.
Turns the warehouse into self-serve dashboards and reports the business reads on its own. Benefit — decisions made on current numbers, not a week-old slide someone exported by hand.
For example, an operations lead opens a live dashboard at 8 a.m. and sees yesterday’s numbers already reconciled, instead of waiting for an analyst to assemble the weekly deck.
Validates records as they flow, catches the duplicates, gaps, and bad values, and resolves the same customer or product appearing five different ways across systems. Benefit — trustworthy data and far less time wasted reconciling it, directly attacking the cost cited above.
For example, “Acme Corp,” “ACME Inc.,” and “acme corporation” collapse into one verified customer, so revenue isn’t triple-counted and a mailing doesn’t go out three times.
Builds streaming pipelines for data that’s only useful fresh — events, transactions, sensor and clickstream data — processed as it arrives. Benefit — decisions and alerts that fire in seconds, not after the nightly batch.
For example, a fraud signal or an out-of-stock event reaches the team the moment it happens instead of surfacing in tomorrow’s report, when it’s too late to act.
Documents where each field comes from, who may see it, and how it’s defined — so the data layer is auditable, not a black box. Benefit — trust, compliance, and an answer to “where did this number come from?”
For example, an auditor asks how a regulated figure is calculated and the lineage traces it field by field back to the source system, instead of triggering a week-long manual hunt.
The scope below is the difference between a data layer the whole business trusts and a tangle of brittle exports nobody owns.
We assess your current sources, tools, and pain points and design the target architecture — warehouse or lakehouse, batch or streaming, the modeling approach — sized to your real volume and budget. Run as part of our AI readiness assessment, with the honest “you don’t need a lakehouse for this” call included.
We build the ingestion and transformation pipelines (ELT/ETL) that pull from every source on a reliable schedule, with retries, alerting, and tests — so a broken feed pages someone instead of quietly poisoning a report.
We build the central store and model it for the questions the business actually asks — fast to query, documented, and structured so analysts and tools can self-serve without re-deriving definitions each time.
We put validation at the point of ingestion, build the quality rules and monitoring, and resolve the duplicate-and-conflicting-record problem (master data), so what lands in the warehouse can be trusted.
We build the dashboards, semantic layer, and reporting on top — defining each metric once so “revenue” and “active user” mean one thing everywhere — and connect the tools your teams already use.
Where data must be fresh we build streaming pipelines; across all of it we document lineage and access, instrument the platform for freshness and cost, and train your team to operate, extend, and trust it.
What you get when you hire us — all assigned to you under full work-for-hire IP
The same delivery model behind all our AI development work, tuned for the data layer — one accountable lead, fixed scope, no handoffs.
Inventory the sources, the questions the business needs answered, and the data-quality and freshness requirements.
Output: a target architecture & the success metrics
Design the warehouse or lakehouse schema, the pipeline plan, and the metric definitions, in your own cloud tenant.
Output: a data model & a documented pipeline design
Engineer the pipelines, the store, the quality checks, and the dashboards behind tests and data contracts, so each layer is validated before the next depends on it.
Output: a working, tested data platform
Instrument for freshness, quality, and cost, set the alerts, and train your team to run and extend it.
Output: a production data layer & a team that owns it
Most engagements reach steady-state in 4–8 weeks, full IP signed at kickoff, payment tied to the ROI we agreed to deliver — not billable hours.
A data platform is only worth what the engineering and operating discipline underneath it can sustain — and running data-driven systems in production for the long haul, without them falling over, is exactly our track record.
We don’t claim a published case study for every component above; what we can show is that we build and operate data-dependent platforms that stay reliable for years, not prototypes that pass a demo.
The clearest adjacent evidence is Bridge Athletic — a product partnership since 2012 that we carried from a day-one build through repeated modernization, re-platforming, and re-engineering, with the data-driven platform never going offline, now used by USC, the LA Rams, and MLB and MLS teams.
Operating a live, evolving data platform for 12+ years without downtime is the same discipline a trustworthy data layer demands: get the foundation right, then keep it right as everything changes around it. That same evals-before-launch, monitor-after rigor is what held a 200+ location restaurant chain at twice-a-week releases with zero critical defects across four years (BJ’s Restaurants).
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We’ll tell you plainly when you don’t need the platform you came in asking for, which a vendor paid to build the biggest one won’t.
What sets our data engineering services apart is a record of operating data-dependent systems in production for years, and a charter built around your owning the result:
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 actually run on — so the model has something real and trustworthy to learn from.
Quality and lineage are the product, not a phase. We treat data quality, freshness, and “where did this come from?” as measured, monitored properties — because an unauditable data layer is a liability, especially in fintech and healthcare.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes the platform answers for it.
Built to transfer. Pipelines, models, dashboards, and documentation are assigned to you, and your team is trained to operate and extend the platform when we step back. You own the asset, not a dependency on us.
Reconciled transaction data, real-time fraud and decisioning pipelines, and audit-grade lineage on every regulated figure. Fintech software →
Unified patient and operational data inside HIPAA-compliant architectures, every field’s access and origin documented. Healthcare software →
One source of truth across orders, catalog, and behavior, powering live dashboards and the features that feed recommendation and forecasting.
Consolidated reporting across locations so every site and the head office read the same reconciled numbers, not five conflicting spreadsheets.
What teams want to know before they commit to a data platform.
This page is about the data layer — the pipelines, warehouse or lakehouse, quality, governance, and BI that make data trustworthy and usable. That’s the foundation; machine learning models and AI infrastructure sit on top of it.
Most stalled AI projects are really stalled data projects, so we often build or fix the data layer first. We’ll scope which one your problem actually needs — and sometimes the answer is a clean warehouse and a dashboard, not a model.
The honest answer comes early, in the architecture phase. We size the platform to your real data volume, the questions you need answered, and your budget — sometimes that’s a full lakehouse, often it’s a right-sized warehouse, and occasionally it’s fixing the pipelines you already have. We’ll tell you when the bigger build isn’t worth it rather than sell you one you don’t need.
Usually, yes. We design around your existing cloud, sources, and BI tools rather than forcing a rip-and-replace, and we build on open, standard approaches so you’re not locked into one vendor’s stack. Where a tool genuinely needs replacing we’ll make the case with the cost, but the default is to integrate what you have.
By engineering trust in, not assuming it. We validate records at ingestion, build data-quality rules and monitoring, resolve duplicate and conflicting records (master data), and define each metric once in a semantic layer so a number means the same thing everywhere.
Lineage documents where every field comes from, so “is this right, and where did it come from?” has an answer — directly attacking the poor-data-quality cost that runs into the millions.
The platform is built inside your own cloud tenant under your access controls, and every engagement starts with an NDA and a security review. We document data lineage and access so regulated data is auditable rather than opaque — which matters most in fintech and healthcare, where we work inside HIPAA-compliant and audit-grade architectures.
You do — completely. The pipelines, the warehouse or lakehouse, the quality rules, the dashboards, and all documentation transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate and extend them. The engagement is built around the handover, not around locking you in.
Most data platforms reach a working steady-state in 4–8 weeks under a fixed-scope engagement with one accountable lead, and payment is tied to the ROI we agreed to deliver. Build cost depends on scope and the state of your current data — our AI development cost guide gives real ranges — and we model the ongoing cloud and pipeline running cost before building, so the running cost is a forecast you’ve already seen.
Thirty minutes · No pitch deck
Bring the problem — reports that disagree, an AI project stalled on the data, a warehouse that costs too much or runs too slow — and we’ll tell you honestly what the data layer needs, what it takes to build, and what it costs to run.