Collideascope gave their client insights in 5 minutes

Curtis Hays built Collideascope on a principle most agencies won't say out loud: the data shouldn't change based on who's presenting it. Early in his career, he watched marketing teams walk into boardrooms with hand-picked numbers, the story shifting month to month to make the quarter look good. When he started Collideascope, he swore he'd never run an agency that way.

So he built Looker Studio dashboards with the same metrics every month, giving clients the same view he had. Honest, but not enough.

The problem was the data itself. Google Ads lived in one place, Meta in another, LinkedIn in a third, each platform running different attribution windows. Looking at any one in isolation told an incomplete story. To get the real picture, Curtis exported everything to PDF and fed it to an LLM, or uploaded a spreadsheet and asked it to "make sense of this." The result was slow, fragmented analysis from an AI guessing at a static file rather than reading live data.

Setting up a new client took 2–3 hours of building Looker templates, connecting sources, and configuring views, every time from scratch. And when a client asked an urgent question, Curtis dug through reports by hand, which meant two to three hours before he could give a grounded answer. Responses were educated guesses as much as analysis.

Curtis wasn't looking for another dashboard tool. He needed something that could take all the data — Meta, Google, LinkedIn, GA4 — structure it properly, and let him ask questions the way he actually thinks about campaigns.

With Lemonado, he could connect every data source, authenticate, and have everything joined in one environment ready to query in plain English in under 10 minutes per client, with no templates to duplicate and no CSVs to export. The difference from his old workflow wasn't just speed, though. It was accuracy. Feeding a PDF to an LLM means handing it a flat document and hoping it reconstructs the truth. With Lemonado, natural language questions become structured SQL queries against live, warehoused data. The AI isn't guessing. It's reading the actual records.

Curtis also integrated Lemonado into his Claude Code workflow via MCP, giving every client their own structured context: campaign goals, budget parameters, seasonal patterns, change logs. When he queries, the AI understands not just what's happening in the data, but why they're running those campaigns in the first place. Reporting Ninja, the connector tool Collideascope had used to pull Meta and LinkedIn into Looker Studio, was cancelled, made redundant on day one.

A client Curtis had worked with for nearly a year just had a record-breaking e-commerce month, and the CEO had no idea where it came from. With multiple distribution channels and a full picture that wasn't visible in any single platform, he called Curtis and asked what was going on.

Before Lemonado, answering that question meant two to three hours of pulling Meta data, pulling Google data, reconciling attribution windows, and building a narrative by hand. Instead, Curtis opened Lemonado and typed a prompt. Five minutes later he had a complete story: which Facebook campaigns were driving volume, how Google was converting the demand Meta had created, a year-over-year comparison, and a two-month forecast as the client headed into peak season. He sent it as an email, and the CEO replied: "This is exactly what I needed."

That same week, Curtis was reviewing a healthcare client's LinkedIn campaigns (thought leadership, top-of-funnel awareness, and brand validation) and asked Lemonado how performance looked after some recent changes. It returned the engagement data and, without being asked, benchmarked the results against LinkedIn industry standards. The campaigns were running at 5–6% engagement against a LinkedIn benchmark of around 2% for strong performance. Curtis had been doing LinkedIn marketing for ten years and already knew those numbers, but seeing them surface automatically and unprompted changed what he thought was possible.

With those hours reclaimed, Curtis stepped out of the analyst role entirely. Katie Decker, co-founder, now runs analysis in Lemonado independently, querying data, building context, and pulling insights without Curtis as the gatekeeper. For a two-person founding team where every hour compounds, that's not a convenience. It's a structural change.

Cut client setup time by 83%: from 2–3 hours to under 10 minutes, per client.

Cut ad hoc analysis time by 97%: from 2–3 hours to 5 minutes, per question.

Unified Meta, Google Ads, LinkedIn, and GA4 into one queryable layer, with no CSV exports and no static file uploads.

Cancelled Reporting Ninja, eliminating a connector tool and consolidating the stack.

Freed both co-founders from the analyst bottleneck, with Curtis and Katie now running analysis in parallel.

Surfaced unprompted industry benchmarks that gave Curtis client-ready proof of performance without additional research.

Collideascope is already answering CEOs from Lemonado and opening Claude on the same data. Next, with Curtis: which client workflows they want on a schedule, so the five-minute answer is waiting before the call.