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Before and After data cleaning — duplicates removed, inconsistent text standardized, missing values handled, dates reformatted. Raw data transformed into analysis-ready output.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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We just wrapped a website redesign for DataLab USA, a data analytics and marketing company that's been in the space for decades.
Going into the project, they already had almost everything:
Expertise
Impressive track record
Proprietary technology
But the website didn’t reflect any of it.
You couldn’t really tell how established the company was unless you already knew them.
3 things were working against them:
Repetitive content that buried the most important information
Messaging that couldn't clearly explain what they do or why it matters
A visual identity that no longer reflected the company's actual position in the market
That's what we were brought in to fix.
The direction we landed on was minimal, sharp, and structured. Black and white as the base, with subtle blue gradients to guide attention and create hierarchy. Angular layouts. Thin dividers. Clean sections. Every visual decision was designed to mirror the analytical nature of the brand.
On the content side, we rebuilt the information architecture and rewrote the messaging from the ground up. The goal was clarity without stripping away the technical depth that gives DataLab credibility.
The result is a site that finally matches the company behind it.
If your product or company has outgrown the way it presents itself online, that's a fixable problem.
Book a call and let's talk about it.
Link in the comments.
A thoughtful rebuilding to mirror the exact brand look.
How We Map Repeated Business Work Before Building Automation
A service agency came to us with a familiar problem: their team was manually chasing clients for updates, following up on internal tasks, and losing hours every week to the same repetitive loop.
Most people jump straight to "automate it." We don't.
Step 1: Map the mess first.
We shadowed the actual workflow. Who sends what, when, and why. Turns out, 60% of the follow-ups existed because there was no shared visibility into task status. People were pinging each other just to figure out where things stood.
Step 2: Fix the process before adding tech.
We restructured the handoff points. Clear ownership at each stage. One source of truth for task status. No more "just checking in" messages clogging up Slack.
Step 3: Layer automation on top of a clean process.
Only after the workflow made sense on paper did we wire up automated status updates, reminders, and escalation triggers. The automation didn't replace the process. It enforced it.
The result:
Fewer manual follow-ups. Faster handoffs. The team stopped chasing and started executing. The client said it felt like they "hired two extra people" without adding headcount.
The takeaway: Automation built on a broken process just breaks faster. Map it first. Clean it up. Then automate what's left.
AI Voice Calling Agent — Vapi + Make.com + Google Sheets Built an AI-powered inbound voice agent named Shreeja for KyrosAura, a wealth advisory firm. The agent answers calls from the website, qualifies leads by confirming a minimum 50 Lakh investment mandate, collects caller name, phone number, and preferred meeting time, then automatically logs the booking to Google Sheets via Make.com. Supports Hindi and English detection automatically. Built on Vapi, Claude Haiku, ElevenLabs, and Deepgram.
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