Case studies

Software development case studies: systems we built, and still run.

In-house software development case studies: how we designed, built and operate our own products, written so you can judge how we would build yours. They describe engineering, not client results.

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LeadSpidey dashboard: a live lead feed beside an AI qualifying conversation (sample data).

All case studies

Systems we built and run ourselves.

In-house product engineering case study LeadSpidey: building an AI SDR for inbound leads How we built an AI agent that answers every inbound lead on WhatsApp or email, qualifies it in conversation and books the meeting, with people in control. Read the LeadSpidey case study
In-house product engineering case study Serpify: engineering AI visibility and rank tracking How we built daily data pipelines, dashboards and client reports that track a brand in Google search and in AI answers, every figure traceable to source. Read the Serpify case study

About case studies

Why our own products.

Scribbify publishes no client results it can’t show evidence for. Our own products are different: we built them, we run them, and we can explain every decision, including the ones we got wrong.

Each case study shows the problem, the architecture, the controls that keep it safe to run, and what it taught us about building the same kind of system for clients.

For example, the LeadSpidey case study explains when the AI SDR stops and hands a conversation to a person, and why that exit was designed before anything else. Those 2 products run every day, so the lessons come from use, not from a launch.

The same decisions come up in every AI agent we build for clients: which sources an agent may answer from, what it must never promise, when it hands over, how every conversation is logged, and how a person reviews and pauses it. The case studies show how we made those decisions on our own products first.

If you are weighing an AI SDR for inbound leads, an AI customer service agent or outreach automation, read the case study closest to it, then the service page that builds the same kind of system for your business.

How a project runs

01

Understand the workflow

A written picture of where the work stalls
02

Choose the first gap

One agreed gap and what should change
03

Implement the fix

A working fix your team uses
04

Measure and improve

A plain review, and a decision on what is next

Questions

Questions people ask first.

Are these case studies about client projects?

No. They are in-house product engineering case studies about Scribbify’s own products. They show how we build, not results for a client.

Which products are covered?

LeadSpidey, an AI SDR for inbound leads, and Serpify, an AI visibility and rank tracker.

Why publish case studies about your own products?

Because we can show every decision behind them. Running software after launch is the best evidence of how a team builds it.

Can you build something similar for us?

Yes. Each case study links to the services that build the same kind of system for clients, from AI agents to dashboards.

Do you build AI agents for customer support too?

Yes. AI customer service agents use the same controls as our AI SDR: approved sources, handoff rules, logging and review. The AI customer support agents service page sets out how we build them.

Don’t see your exact problem?

Most projects start from one problem that costs you money, not a category. Tell us where the work or the revenue gets stuck.

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