How long does it take to build a client portal on an existing data stack?

A production-ready client portal usually takes 6 to 12 weeks, depending on scope, data readiness, integrations, and access requirements. Data Front builds on top of your existing Snowflake, BigQuery, Postgres, dbt, APIs, or data warehouse setup.

How is Data Front different from embedded analytics tools?

Embedded analytics tools give you components to build with. Data Front delivers the finished data application. We handle the application layer, data connections, authentication, permissions, workflows, integrations, and production launch.

When does a SaaS company need a client portal development service?

A SaaS company needs a client portal when customers need self-serve access to data, reports, dashboards, exports, or account-specific insights. It is also useful when the team is spending too much time on manual reporting, spreadsheets, or recurring client data requests.

Your warehouse works. Your pipelines run. But your client is still waiting on an export. Your ops lead is still stuck in Slack. Your AI demo is still something nobody trusts with real data.

Orbit by Datum Labs builds the layer that fixes all three, on the stack you already have.

Book a 30-minute scoping call

See what we build

Existing Data Stack

Input

Warehouses & databases

APIs & event streams

Analytics pipelines

Business systems

Data Apps · Datum Labs

Production software
on your data layer

Application layer active

Existing data stack

Warehouses, APIs, pipelines -> untouched

Application layer

Portals, analytics, AI workflows on top

Access & permissions

Auth, roles, multi-tenant control

Users, workflows, actions

Teams and customers on real data

Portals Analytics AI Workflows Auth

Access & Permissions

Control

Auth & role-based access

Multi-tenant isolation

Audit logs

Users, Workflows, Actions

Use

Self-serve insights

Ops & approval flows

AI in production

31%

Retention lift after adding embedded analytics

27%

Churn drop vs export-based reporting

$50.6B

Embedded analytics market size in 2025

100%

AI outputs verified against real data before showing a user

A human interacting with a dashboard on a laptop screen

What is the data application layer?

The data application layer is the production software that sits between your data and the people, or systems, that need to act on it. Sometimes that's authentication and a role-based interface. Sometimes it's an AI system that reads the data and checks its own answer before anyone sees it.

Orbit builds this layer, in whichever form your data actually needs, on the stack you already have.

See what we build on top of your stack

[ The problems ]

Q: What is the data application layer?

The data application layer is the software between your data stack and the people who need to use the data. It includes user login, role-based access, workflows, and interactive screens. Data Front builds this layer on top of existing warehouses, dbt models, APIs, and business data

Is your data ready but your users still waiting ?

These are the specific gaps that appear once a data stack matures and the business starts demanding more than reports.

The Friday export problem

Your data team spends hours pulling from the warehouse, formatting spreadsheets, and answering follow-up questions by Monday. The data is accurate. The delivery is not a product.

The "can we see our data?" request

A client asked for real-time access to their account three months ago. It is still in the backlog. Every week of delay is a retention risk you're paying for.

Your ops team is running on spreadsheets

Your ops team manages approvals, reviews, and records across Google Sheets and Slack. No audit trail. No real-time visibility. Errors go unnoticed until they cost you.

The AI prototype nobody will turn on

Your AI prototype works fine in a demo. Nobody will connect it to real data, because nobody can prove it won't guess. Six months later, it's still just a demo.

[ What we build ]

What You Get With Orbit

Customer Data Portals

Secure, branded portals where customers access their own data without waiting for manual reports. Multi-tenant, role-based, with account-level filtering, embedded analytics, and exports.

Data Layer Apps or Data Management Interfaces

Controlled internal tools that replace spreadsheet-driven workflows and manual approvals. Validation screens, approval flows, work queues, audit trails, and role-based admin access.

Scheduled Actions & On-Demand Reports

Renewal reminders that fire against real contract dates, not a calendar someone checks. Branded reports generated on demand, not assembled by hand. Built on your data, so nothing waits on a person to remember.

AI Classification Pipelines

AI that turns messy inbound data, emails, documents, and web pages into clean, warehouse-ready records. One bounded judgment call; everything after stays deterministic and auditable.

Grounded AI Agents

Agents, your team or customers can ask a plain question and trust. Every answer is checked against real data before it's shown, sources attached, always.

Build Your Orbit Today

[ Who this is for ]

You are probably one of these four

If you recognise your situation below, Orbit was built for you.

Head of Product or
CTO

Clients keep asking for data access inside your product. Your engineers are too stretched to build it, and low-code builders just hand you more to maintain.

Head of Data or Analytics Lead

You built the warehouse. It's clean and production-ready. Now the business wants it accessible to customers, and building the portal became your problem too.

Ops Lead or RevOps Lead

Your team runs critical workflows through spreadsheets and Slack. No audit trail. Approvals take days. You know it's broken, but you can't get engineering time to fix it.

Head of Engineering or AI Lead

Your AI prototype works fine in a demo. Nobody will approve it for production, because nobody can prove it won't guess on real data.

Book a 30-minute scoping call

Find the workflow gap

We identify where your data is ready but still trapped behind exports, spreadsheets, manual approvals or a demo nobody's approved for production.

Shape the experience

We define who's using this or what's reading it, what they need to see or know and which permissions and actions are required.

Build and validate on your data stack

We connect to your warehouse, APIs, and models. For AI builds, this is also where we benchmark models against your real data and design the guardrails that catch a bad answer before anyone sees it.

Ship with production controls

We launch with authentication, role-based access, monitoring, logging and documentation, so what ships is trusted on day one, whether it's a portal or an AI system

[ HOW ORBIT WORKS ]

From existing data to usable software

Orbit starts where dashboards, exports, and AI demos stop. We take one data-heavy workflow and turn it into something your users, or your systems, can open, trust, and use.

[ WHY ORBIT ]

Why do SaaS teams choose Orbit over building internally?

Category	Building Internally	Orbit
Time to production	4 to 9 months depending on backlog and team capacity	6 to 12 weeks for a production-ready first release
Engineering cost	Full sprint cycles pulled from your core product roadmap	Fixed-scope engagement that does not touch your product backlog
Data layer expertise	Application engineers treat the warehouse as a black box	We learn your warehouse, dbt models, and pipelines before writing a line of code
Trust in what ships	A prototype that impresses in a demo and stalls before production	Every build, portal or AI, validated against real data before a customer ever sees it

Plan Your First Data App

[ Why Orbit ]

Built on the stack you already have

Orbit does not ask you to replace your infrastructure. We add the layer, portal, interface or AI on top of what you already use.

Data warehouses

Snowflake

BigQuery

Redshift

Postgres

Application layer

React

Next.js

Node.js

Python

Hono

Django

Auth and access

Auth0

Supabase Auth

Google OIDC

Custom RBAC

Row-level security

Transformation

dbt Core

dbt Cloud

AI and agentic

LangChain

LangGraph

OpenAI

Anthropic APIs

Infrastructure

AWS

GCP

Azure

Docker

GitHub Actions

[ Case Studies ]

What happens when the application layer gets built?

HR tech · Candidate intake

Hireflow

Recruiters retyped every resume by hand, and screening ran on gut feel. Replaced with a conversational AI agent and resume intelligence that write structured, scored candidate data into one record.

From manual screening to structured intake that never guesses.

Read case study →

Legal services · Competitive intelligence

Westwise

Manual analyst research couldn't scale, and a wrong AI guess would poison every record. Replaced with a self-healing pipeline that reads, scores, and validates every competitor profile automatically.

From analyst research to a pipeline that runs itself.

Read case study →

Fintech · Payments support

Market 4U

Stuck payments meant an hour of manual log digging by a senior engineer. Replaced with a read-only AI agent that answers in plain English, grounded in real data, in seconds.

From an hour of digging to seconds.

Read case study →

Creator economy · Streaming

YourStage.live

Revenue, invoices, and payouts across separate systems with no creator visibility. Replaced with a portal that ingests streaming data, calculates earnings automatically, and executes payouts end to end.

From manual reconciliation to automated revenue operations.

Read case study →

SaaS · Contract operations

Techsource

Scattered contracts, missed renewals, and no spend visibility. Replaced with a multi-tenant portal giving every team role-scoped access to their contracts, vendors, and spend in one place.

From scattered records to one operational command centre.

Read case study →

Real estate · Asset management

Ember Capital

Investors waited on manual reports. Analysts hand-routed incoming files by deal and system. We built a portal for portfolio views, backed by an AI pipeline that classifies every report automatically.

From manual reporting to a portal with a self-classifying pipeline behind it.

Read case study →

[ Ready to Build? ]

Your data has been ready for months. Your users are still waiting.

Every week that gap stays open, a competitor closes it first. Whatever's been sitting on your roadmap, this is the week it ships.

Scope It This Week

Frequently asked questions

What is the data application layer?

It's the software between your data stack and the people or systems that need to use it, logins, permissions, workflows, or an AI that checks its own answers before showing them. Orbit by Datum Labs builds this layer on your existing warehouse, no migration required.

What's the difference between a client portal and a dashboard?

A dashboard shows data. A client portal lets customers log in, filter their own data, export it, and act on it. One is a report. The other is a product.

What makes an AI system production-grade instead of a demo?

It validates its own answer against real data before anyone sees it, and keeps working when the input gets messy. A demo only works once, on data nobody stress-tested. Orbit builds every AI system to survive contact with real customers.

How do you stop AI from hallucinating on business data?

Every output gets traced back to real data before it's shown. If it can't be traced, it's blocked, not guessed. That's the same rule a portal uses for permissions, applied to AI.

How long does it take to build on an existing data stack?

6 to 12 weeks for a production-ready first release, portal or AI, on top of Snowflake, BigQuery, Postgres, dbt, or whatever's already running. Nothing gets replaced to get there.