AI is only as good as its data. We build clean, reliable pipelines and unified data foundations that make AI, agents, and analytics trustworthy.
Data engineering builds the pipelines and platforms that move data from wherever it is created to wherever it is used — cleaned, governed, and current. Apexify's data engineering services focus on making data AI-ready: unified, well-modeled, and reliable enough that the models, agents, and dashboards built on top of it can be trusted.
Typical deliverables:
Engagements range from building a first warehouse for a company graduating off spreadsheets, to preparing an enterprise data estate for AI agents that read and act on live data. The common thread is the standard we build to: every dataset has an owner, a definition, a freshness guarantee, and a test — and everything is built as code, versioned and reproducible.
Assessment. Data pipeline consulting starts with an inventory: sources, current flows, quality issues, and what downstream teams actually need. The output is a target architecture and a sequenced build plan, with quick wins identified for the first month. It typically takes two to three weeks and needs only read access to your systems.
Foundation build. We stand up the core: ingestion from priority sources, a modeled warehouse layer, and quality checks that fail loudly instead of silently. We work in your existing stack where it is sound and recommend changes only where current tooling is the real bottleneck.
Use-case delivery. Pipelines exist to serve something. We deliver against concrete consumers — a forecasting model, a Data Cloud deployment, an AI assistant's retrieval index — so progress is measured in working use cases, not tables created.
Operate and harden. Monitoring, alerting, documentation, cost tuning, and runbooks. We hand off to your team when you are ready, or keep running the platform as a managed service. Handoffs include recorded walkthroughs, so the knowledge survives your own team changes too.
We build data foundations with the end use in sight, because we are also the team building what sits on top. Apexify's AI practice delivers predictive models, agents, and assistants — so our pipelines are designed for those workloads from day one: retrieval-ready documents, leakage-safe training data, and event streams agents can act on. As an official Salesforce Partner, we are equally strong on CRM data — the messiest and most valuable source most companies have.
This service fits teams whose reports disagree with each other, whose AI initiatives stalled on data quality, or whose entire pipeline is one person exporting CSVs. It also fits companies preparing for Data Cloud or a first serious machine-learning project who want the foundation done right once.
We are deliberately boring about reliability. Pipelines that fail loudly, dashboards that show yesterday's load status, alerts that reach a human — these unglamorous habits are why the platforms we build keep their credibility after the launch excitement fades.
Apexify is headquartered in Calgary and delivers data engineering remotely for clients across Canada and the US, with 20+ years of combined team experience and a 98% client satisfaction rate.
Cost tracks the number of sources, data volume, and the quality of what exists today. A focused build — a few sources into a modeled warehouse with quality checks — is a mid-sized project; enterprise-wide platforms are multi-phase programs. The assessment is a small fixed engagement that produces a sequenced estimate before any build spend.
First production pipelines typically flow within 3-6 weeks; a solid foundation across priority sources usually takes a few months, delivered incrementally. We sequence the build so something useful ships every few weeks rather than a big-bang platform at the end. Source-system access is the most common schedule risk.
Usually some, rarely all of it. You do not need a perfect platform to start — you need the specific data behind your first use case to be reliable. We scope the minimum foundation for that use case and expand as AI initiatives multiply. Starting AI on unowned, unvalidated data is how projects quietly fail.
We are stack-flexible: the major cloud warehouses and lakehouses, standard orchestration and ELT tooling, and Salesforce Data Cloud. We recommend based on your team's skills, existing licenses, and workload — not a reseller agreement. Where your current stack is sound, we build in it rather than forcing a migration.
Yes — CRM data is a specialty. Duplicate accounts, inconsistent picklists, orphaned records, and integration drift are familiar territory for our Salesforce and CRM practices. We pair the cleanup with pipeline-level validation so quality holds after the one-time fix instead of decaying back within a quarter.
Salesforce, CRM, and AI working as one system — explore the rest of the stack.