UnGeneric is an open-source research framework for content differentiation.
How different? For your content to be generic with UnGeneric, another company would have to:
Solve your exact problem
Solve it your exact way
For your exact audience
If that happens, you don’t have a content problem; you have a clone situation.
Unless you skipped the hard thinking.
In which case, awkward.
Let’s be brutally honest for a second
Everyone’s Content Sounds Identical. Including Yours. Including Ours Before We Fixed It.
You’re doing everything the “experts” told you to do:
Reading industry reports
Following thought leaders
Analyzing competitors
Using keyword tools
And yet: nobody remembers your content.
Here’s what’s actually happening: everyone’s using those sources the exact same way.
Everyone reads that “AI in Marketing” article. Everyone writes their version of “How AI is Transforming Marketing.” Everyone publishes on LinkedIn the same week. Everyone gets the same void engagement.
Then AI writing tools showed up.
“Create content 10x faster!” they said.
Great. Now we’re all creating the same monochromatic content 10x faster.
The internet didn’t need more speed. It needed different thinking.
The actual problem:
It’s not your sources. It’s not your tools. Plot twist: it’s not even your writing.
It’s that you’re trying to speak to “everyone in your industry” instead of solving one specific, painful, keeps-someone-up-at-3am problem.
Cast a wide net, catch nothing. Every. Single. Time.
Two Research Methods
We’re Betting Everything On
Neither requires a PhD. Both require you to stop phoning it in.
Method 1: Make Existing Content Hyper-Specific
Everyone’s reading the same sources in your industry.
The difference: they write for “everyone.” You write for one person with one specific problem they’d pay money to solve.
Real Example:
Source everyone reads: “How AI is Changing SEO”
What everyone writes: “5 Ways AI is Transforming SEO” (yawn, skip, forget)
What actually works: “How AI Search Optimization Finally Solves B2B Attribution (The Problem Your Board Keeps Asking About)”
Same research. Completely different target. The first gets skimmed. The second gets forwarded to the VP.
When to use: Good sources exist but everyone’s being too safe with generic angles.
Method 2: Steal Knowledge From Other Fields (Legally)
When your industry has exhausted a topic (spoiler: they have), look where your competitors aren’t looking.
Psychology. Behavioral economics. Game design. Neuroscience. Operations research.
Real Example:
Your industry says: “We’ve covered attribution from every possible angle”
Behavioral economics says: “Actually, choice architecture research explains exactly why attribution models give you false signals”
You write: “Why Your Attribution Model Is Lying To You (According To Research Your Competitors Don’t Read)”
Different source entirely. Applied to the same problem, to your business context.
When to use: Your industry is out of fresh ideas (which is most of the time).
What Makes Either Method Work:
Three non-negotiables:
You know exactly what problem you’re solving (not “brand awareness” – an actual problem that costs money)
It connects to business results (pipeline, revenue, market position)
It’s backed by real research (not just your hot take)
Skip any of these, you’re back to generic.
The academic research parallel:
Researchers advance knowledge two ways:
Build on existing work in their field
Import frameworks, concepts, ideas, etc, from completely different disciplines
Both work. Both require one specific question to answer.
We’re applying that same logic to content marketing.
Stop writing faster. Start thinking differently.
Where We Actually Are (No Marketing Spin Version)
Phase 1: Research Methods
98%
(They work. We use them daily. Skeptical? So were we.)
Phase 2: Tool Development
90%
(Some parts brilliant. Some parts learning slowly. Very slowly.)
Phase 3: Testing on Own Projects
10%
(Using both methods on our content. Breaking things. Learning things.)
Phase 4: Real Company Testing
0%
(Lab Partner program opens Q1 2026. 5 spots. Application process TBD.)
Phase 5: Public Beta
0%
(Late 2026/Early 2027. If we don't spectacularly fail before then.)
Phase 6: World Domination
What Exists Right Now:
The Methods – Both work. We use them daily on our own content. Results: measurably different angles that competitors can’t replicate. Weird part: they work almost too well. Makes our old content embarrassing to look at.
Working Prototypes – Tools we use internally. Not pretty. Extremely functional. Think: incredibly smart but dressed by a 5-year-old.
AI Automation – 30% built. Some parts are legitimately impressive. Some parts are stubbornly stupid. Turns out building systematization is way harder than building intelligence. Who knew.
Testing on Real Projects – Just started (10%). Using both methods on our own business content. If it doesn’t work for us, why would it work for anyone else?
The Full Platform – Converting “think differently” into software people can actually use without a manual. Harder than it sounds. Way, way harder.
Timeline (What Will Probably Actually Happen):
Now → Q4 2025: Test methods on our own projects
Reality: Use both methods on our actual content. Break things. Fix things. Document everything. Build case studies from our own work.
Q1 2026: Open Lab Partner program
Reality: If our own results are good, we’ll look for 5 companies to test with. Application process, selection criteria, pricing – all TBD.
Q2-Q4 2026: Lab Partner validation
Reality: Test across different industries. Learn what works. Fix what breaks. Make the tools actually good.
Late 2026/Early 2027: Public beta
Reality: If results are real and companies are happy, we’ll open it up. If not, we’ll have very expensive lessons to share.
Watch Us Build This In Public
Monthly updates on what we’re building, what’s working, what’s face-planting, and what we’re learning about making content that doesn’t blend into the background.
Tool development progress (the good, the bad, the “we thought this would work but LOL nope”)
Method applications (real examples from our own content – before/after transformations)
Testing results (what’s working on our own projects, with real metrics)
Methodology deep-dives (for the nerds who actually care how this works)
Honest failures (because those are more useful than success stories)
Timeline updates (when Lab Partner program opens, what the process will be)
Why follow now (before Lab Partners)?
See the methods in action on real content
Watch the tools develop
Understand the methodology before it’s packaged
Get early notice when Lab Partner applications open
Influence what gets built (we read every email)
Three Ways To Stay Connected
Option 1: Get Monthly Updates
Progress reports, method examples, honest failures. Once a month. No spam.
Option 2: DM us on Linkedin
Questions, thoughts, “this sounds interesting but I’m skeptical” – we read everything.
What to DM about:
Questions about the methods
“I tried this approach and here’s what happened”
Collaboration ideas
“When does the Lab Partner program open?”
General thoughts on content differentiation
Please don’t
Sales pitches (we’re building, not buying)
“Can you write content for us?” (not what we do
Generic “let’s connect” messages (please have a reason)
Real-time updates, methodology discussions, build notes.