Services · 07
Data engineering and analytics for trustworthy reporting
We build the data layer first: clean pipelines, warehouse or lake patterns where needed, and dashboards teams actually use.
Dashboards and AI features are only as good as the data underneath them. When product events are missing, CRM syncs lag, or two teams define “active user” differently, every chart and retrieval answer inherits that confusion. Data engineering work closes that gap by making sources explicit, pipelines reliable, and metrics defined once—not re-argued in every meeting.
We start with the questions your team actually needs answered: revenue by segment, funnel conversion, ops throughput, or document coverage for retrieval. From there we map source systems, freshness requirements, and who owns each dataset. Pipelines are designed for observability—failed runs alert someone, partial loads are visible, and critical tables have quality checks before downstream dashboards refresh.
Modeling choices follow how people use the data, not warehouse fashion. Star schemas, incremental loads, and event streams are tools, not goals. We document grain, join keys, and business definitions so a founder, operator, or engineer can read the same number and mean the same thing. That documentation is part of the deliverable, not a wiki page that never gets written.
Analytics work often feeds product decisions and AI features in parallel. Clean ingestion and access boundaries make RAG and reporting safer because retrieval pulls from approved sources with clear ownership. We sequence data foundations with product priorities so you are not blocked on a six-month warehouse project before shipping a single useful dashboard.
Whether you need your first ops dashboard, a warehouse layer for SaaS metrics, or pipelines that prepare documents for search and AI, the outcome is trustworthy numbers your team can act on—and a path to extend without rebuilding from scratch every quarter.
Problem we solve
AI and dashboards fail when source data is incomplete, delayed, or poorly modeled.
Ideal for
Companies that need reliable numbers before adding more AI or reporting—and teams tired of reconciling spreadsheets against product analytics.
Use cases
Concrete examples of how teams use this service.
Product and revenue dashboards
Unified views of signups, activation, retention, and revenue tied to a documented data model so leadership sees one version of performance.
Ops and fulfillment reporting
Pipelines from orders, inventory, or dispatch systems into dashboards that reflect same-day status—not yesterday’s CSV export.
Warehouse foundation for a growing SaaS
Incremental loads from app database and billing into a warehouse with clear grain for accounts, subscriptions, and usage events.
Document ingestion for RAG
Structured ingestion from docs, tickets, or knowledge bases with access rules and freshness so retrieval uses approved, up-to-date sources.
Metric reconciliation and cleanup
Audit of existing reports, duplicate definitions, and broken joins—with a prioritized fix list and quality checks to prevent silent drift.
Deliverables
- Documented data model
- Scheduled pipelines
- Core dashboards
- Quality checks for critical tables
- Source-to-metric lineage notes
- Runbook for pipeline failures
What we build
- ETL / ELT pipelines
- Warehouse modeling for product and ops metrics
- Dashboards for founders and operators
- Data prep for RAG and analytics
- Data quality checks on critical tables
- Metric definitions and documentation
Tech stack
SQL warehouses, Python or Node pipelines, BI dashboards, cloud storage, dbt or similar modeling where appropriate
Industries we serve
How this service shows up in the industries we work with. See all industries.
What affects cost and timeline
- Number and quality of source systems—undocumented APIs and manual exports extend discovery.
- Volume and freshness requirements: near-real-time pipelines cost more to build and operate than daily batch loads.
- Model complexity: a handful of core metrics ships faster than a full dimensional warehouse.
- Historical backfill scope when past data must be loaded or corrected before dashboards go live.
- Dashboard count and audience: executive summary views differ from self-serve explorer builds for many teams.
- Your team’s capacity to validate metric definitions and review sample outputs during build.
For a directional band, use the project cost estimator, then we refine on a call.
Why work with Apexic
- We define metrics and document grain before building dashboards so numbers do not silently drift after launch.
- Pipelines include failure alerts and quality checks on critical tables—not set-and-forget jobs that break quietly.
- Data work is sequenced with product and AI priorities so you get useful reporting without a blocking multi-month project.
- We prepare ingestion and access patterns that support both analytics and retrieval when AI features are on your roadmap.
How engagements run
Discover, design, build, launch, and support, with clear checkpoints at every stage.
Discover
We align on outcomes, users, budget band, and technical constraints.
Design
We propose the architecture, UX direction, and delivery milestones.
Build
We ship in short cycles with demos, reviews, and tests.
Launch & support
We release carefully, then keep improving with a shared backlog.
Frequently asked questions
Should data work come before AI features?
Often yes. Retrieval and dashboards both depend on clean, timely source data. We help you sequence data foundations with product priorities so you are not blocked unnecessarily.
What tools do you use?
SQL warehouses, Python or Node pipelines, BI dashboards, and cloud storage—chosen to fit your stack and team. We favor tools your team can maintain after handoff.
How do you keep metrics trustworthy?
Documented models, scheduled pipelines, and quality checks on critical tables so numbers do not silently drift. Failed runs alert someone instead of publishing stale data.
Can you prepare data for RAG?
Yes. We structure ingestion and access so retrieval uses approved sources with clear ownership, freshness rules, and boundaries around sensitive material.
Can you fix our existing reports instead of starting over?
Often yes. We audit current pipelines and definitions, fix the highest-impact gaps first, and add checks so regressions are caught early.
Ready to talk through scope?
Book a free consultation or get a directional project estimate.