Optimally acquired, stored, and transformed data is the strongest decision-making force in your business — and the foundation every AI initiative is built on. We turn raw, scattered data into trusted, AI-ready insight.
Follow your data from source to decision. Tap any stage to see how we design it — with governance, security, and observability woven through the whole flow.
At QCerris we've been harvesting the latest data-engineering technologies to build solutions that guarantee a robust, secure infrastructure for seamless data collection, storage, and management.
Every organization can become a data-driven company that thrives with an agile, proactive, and cost-efficient approach. All that crucial business information can be shaped into precious insight for informed, future-proof decisions — and to power AI.
From maturity assessment to big-data pipelines, we deliver end-to-end on AWS, Azure, or GCP — certified and platform-agnostic.
Our engineers build customized solutions on state-of-the-art tooling and continuously optimize the flow for streamlined data management.
Proven where data is biggest and most regulated — logistics, pharma, and finance companies across the U.S.
From first assessment to a fully automated, AI-ready data platform — everything you need, delivered by one senior team.
A clear read on your current data, the gaps, and the highest-value path to becoming a data-driven — and AI-ready — organization.
Reliable ingestion and ELT/ETL across apps, APIs, events, and files — incremental, observable, and built to scale.
Governed lakehouse and warehouse design on open table formats, unifying structured and unstructured data for analytics and AI.
Trusted, well-modeled data with tests, documentation, and lineage (dbt and friends) so every consumer can rely on the numbers.
Feature stores for ML and vector / embedding pipelines for RAG — the layer that grounds AI in your own proprietary data.
CI/CD for data, automated quality checks, freshness and cost monitoring, and alerting that catches issues before users do.
AI is only as good as the data behind it. We build the governed, AI-ready foundations — lakehouse, RAG and vector pipelines, feature stores, and quality controls — that turn models from a liability into a dependable advantage.
Governed, lineage-tracked foundations with PII masking and monitored refresh, so models train and retrieve on trusted data.
Embedding, chunking, and vector stores that ground LLMs in your proprietary data and cut hallucinations.
Consistent offline and online features, reused across models, with point-in-time correctness.
Event backbones and near-real-time pipelines that feed live dashboards and online inference.
Tests, data contracts, and access controls that make AI outputs dependable and auditable.
Clean, versioned datasets and pipelines that plug straight into model training and serving.
Pay for the capacity you need and scale up or down as projects demand — no hiring, onboarding, or idle overhead.
Vetted data engineers and architects delivering from day one, across any cloud.
Your team concentrates on decisions and strategy while we own the pipelines.
Overlapping time zones and embedded ways of working keep delivery fast and aligned.
Data-engineering talent is scarce, expensive, and hard to retain — and a growing engineering backlog quietly paralyzes decision-making across the business. Outsourcing the work to a dedicated QCerris pod turns unpredictable hiring and capital costs into a predictable, scalable operating expense.
You get senior engineers and proven delivery from day one, the freedom to scale the team up or down as projects demand, and meaningful time-zone overlap for real-time collaboration — so your people focus on strategy and insight instead of maintaining pipelines.
Our data engineers combine battle-tested fundamentals with the modern lakehouse and AI stack.
Platform-agnostic by design — we pick the right tool for your stack, budget, and scale.
Map current and future data sources and match them to business goals to set the ground for tailor-made solutions.
A governed lakehouse and ETL design, ready for storage, analytics, and machine learning.
Connect multiple sources and warehouses, organize code, and optimize queries for performance.
An effective DevOps strategy that deploys and automates the pipeline and streamlines releases.
Validate every element, then manage and upgrade the pipeline with the latest advancements.
Consultation is free. Tell us what decisions you want your data to drive — we'll map the path.
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