Why I Built conbase.ai
At scale, content errors aren't writing problems. They are process failures.
I built conbase.ai to give teams an Anti-Black-Box—so you can generate, validate, and publish content with predictable quality at any scale.
3 Lessons I learned
Why manual review fails at scale, and how great content must be engineered.
The Multiplier Effect
As an apprentice, I misread just one line of documentation and deployed software to all company servers instead of one. I learned fast: unclear "content" can cause massive damage.
Content is Code
While working for the Google tech support, I experienced that documentation isn't just text, it's the operating system. If the content is buggy, support costs could explode. Quality at scale requires thoughtful infrastructure, not simple hope.
The Review Bottleneck
In agency life, we had infinite demand but finite senior eyes. We were forced to choose: ship fast and pray, or review slow and die. Manual review became a math problem we couldn't win.
How tiny mistakes explode
My apprenticeship as an application developer was in many ways an insightful experience, but one moment still comes back to mind often.
I got the assignment to install a software on just one of the company servers.
Long story short: I misread the (not so clear) documentation and accidentally installed it on all servers of the company — oops.
The conversation with my team lead wasn't fun. We spent about a week cleaning up the mess.
What stuck with me till today was experiencing how a single misread detail generated repercussions across multiple systems.
When "almost correct" costs money
For a decade, I ran my own online shops and also built online stores and SEO setups for clients.
One of the first client shops I built and optimized all the content, was a car parts online store.
What I realized pretty fast was, that when specs are wrong or unclear, customers order the wrong part.
That turns into returns, refunds, support tickets, and often products you can’t resell as new because packaging is opened or damaged.
Calling it "just bad copy" misses what’s really happening.
A small content mistake becomes a real operational cost, in logistics, in support, and in trust.
Why Google relies on systems
After my Apprenticeship, I moved to Barcelona and worked in technical support for Google Workspace and the Google Maps API.
At that scale, "good content" aka documentation and internal knowledge aren’t just a nice-to-have.
"They are the main operating system."
Because when a FAQ, SOP, or internal note contains one wrong line, that line doesn’t stay in a document.
It can spread, through hundreds of support agents and into hundreds of customer interactions. (Luckily, Google had incredible systems in place.)
Quality at scale requires process infrastructure, not hope.
When review can't keep up
When I came back to Germany, I worked a few years at an e-commerce agency.
Across clients, the same pressure kept returning: ship fast, keep costs low, still protect quality.
In a typical agency, a few senior people define the strategy and SOPs. But delivery many times depends on juniors executing under time pressure.
And that’s where the recurring issues show up:
- a forbidden term slipped into a batch of pages
- tone and structure varied across deliverables
- a claim was copied across many pages and later had to be corrected everywhere
- avoidable typos shipped because review didn’t scale
"At volume, quality breaks less in the “writing”… and more in the execution of the process."
Output scales faster than review capacity. When page volume goes up but senior time and QA stay flat, the error rate compounds, and quality becomes a process problem, not a writing problem.
AI solved the writing cost.
But it spiked the verification cost.
Suddenly, we had 10x the output with the same manual QA capacity. The bottleneck just moved downstream.
"I refused to accept that 'hallucination' is just the price of using AI."
So we built the Anti-Black-Box.
conbase.ai isn't a chatbot. It's a content engineering platform at scale designed to give you control back.
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Engineered Quality
Setup validator prompts to run automatically on every row. You define what "good" looks like, and the system enforces it.
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Transparent Process
See every step, input, and output. No hidden prompts. No "magic" black boxes.
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Peace of Mind
Ship without the dread. Review only the exceptions (flagged risks) and merge only what's clean.
"Scale without control is just risk. I don't believe 'hallucination' is simply the price of using AI. We need systems that validate content similar to software, so we can trust our content at scale."
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Daniel Manco
Founder & CEO
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