Analytics platforms people actually trust the numbers in.
A dashboard is only as good as the moment a user stops second-guessing it. We build multi-tenant portals and reporting platforms that stay fast as the data grows, isolate every tenant cleanly, and make complex numbers legible, so the answer is obvious, not just available.
Why analytics platforms are deceptively hard.
The chart is the easy 10%. The hard 90% is the data being right, the tenants staying separate, the queries staying fast, and a non-expert understanding what they're looking at, every time.
Trust is the entire product
A dashboard that's wrong once gets abandoned. Every figure has to reconcile to a source of truth, with lineage that can answer “where did this number come from?” on demand.
Multi-tenancy with zero leakage
When many customers share one platform, isolation isn't a feature, it's the contract. Row-level security and per-tenant boundaries have to be enforced everywhere, not just in the UI.
Performance as the data keeps growing
A query that's snappy on a million rows can crawl on a billion. Pre-aggregation, caching, and a real query strategy keep dashboards sub-second, without a compute bill that scales faster than the business.
Clarity is a design problem, not a charting one
The win is a non-analyst getting the answer in seconds. That takes sensible defaults, the right level of aggregation, and self-serve that empowers rather than overwhelms.
Governance, access, and sensitive fields
Who can see which metric, at what grain, in which region, with PII handled correctly and every access auditable. Governance has to be built into the model, not patched onto the views.
From raw data to the answer on screen.
The full stack of an analytics product, the pipelines underneath and the experience on top.
Multi-tenant analytics portals
Customer-facing dashboard products with strict isolation, per-tenant theming, and access control baked into the data layer.
Embedded & white-label dashboards
Analytics that live inside your existing product, matching your brand and your auth, not bolted on in an iframe.
Reporting & scheduled exports
Pixel-accurate reports, scheduled delivery, and exports to the formats finance and ops teams actually live in.
Data pipelines & transformation
Ingestion and ELT that land clean, tested, well-modeled data, the foundation every trustworthy dashboard sits on.
Semantic & metrics layers
One governed definition of every metric, so “active users” means the same thing on every chart and in every export.
Self-serve exploration tools
Guided query and filtering that let users answer their own questions, with guardrails that keep results correct and fast.
Trust isn't a tagline. It's controls.
When you hand customers their own data, the security and governance bar is the same one a financial platform answers to. We design to it and produce the evidence your customers' security teams will ask for.
Review our approach with your team →We engineer the systems we build to meet these standards’ controls and produce the supporting evidence; Paragon does not claim to hold these certifications itself.
Model first, then make it fast and clear.
Start from the questions, not the charts
We define what decisions the platform has to support and who's making them, then trace those back to the data sources and the grain the answers actually require.
Build one governed definition of the truth
A tested data model and semantic layer mean every metric has a single definition, so numbers reconcile across dashboards, exports, and tenants.
Engineer for speed, isolation, and cost
Pre-aggregation, caching, and query design keep it sub-second under real data volumes, with multi-tenancy and compute cost handled deliberately, not discovered in production.
Monitor freshness, quality, and the bill
Data-quality checks, freshness SLAs, and cost monitoring keep the platform trustworthy after launch, because a stale or wrong dashboard erodes trust faster than a slow one.
What we bring to your analytics build.
Senior engineers, a discovery-led start, and the engineering this problem actually demands, correctness, isolation, performance at scale, and information design, brought to your platform from the first sprint.
The hard parts transfer directly
Tenant isolation, sub-second queries over large datasets, reconciling numbers to a source of truth, these are the hard problems we engineer for every day, and we bring that proven discipline to your platform.
We get into the data before we build
Discovery means profiling your sources, learning what your users actually need to decide, and validating definitions with your team before a line of production code is written.
We design for trust, and prove it
Lineage, reconciliation, and access controls are built in, with the documentation your customers' security and data teams will want to see.
You own all of it
Pipelines, models, and dashboards in your repositories and your cloud, on open tooling. No proprietary engine, no lock-in on your own data.
What data teams ask us first.
How do you get up to speed on our data and domain?
We open with discovery, profiling your data and learning what your users need to decide, then bring the engineering analytics platforms succeed or fail on: correct numbers, clean isolation, performance at scale, and legibility.
How do you guarantee one tenant can't see another's data?
Isolation is enforced in the data layer with row-level security and tenant scoping, tested as a first-class concern, never left to the application UI to get right.
Can you embed analytics inside our existing product?
Yes, white-label, in-product dashboards that use your branding and your authentication, not a generic embedded widget.
How do you keep dashboards fast as our data grows?
Through deliberate modeling, pre-aggregation, caching, and query design, and by monitoring both latency and compute cost so performance doesn't come at a runaway bill.
How do you make sure the numbers are right?
A single semantic definition per metric, reconciliation to source, automated data-quality checks, and lineage that traces any figure back to where it came from.
Have data your users need to make sense of? Let's talk.
Tell us what decisions the platform has to support and where the data lives. We'll bring a discovery-led plan and a team that sweats correctness and clarity as much as the charts.