I'm trying to build what I wish existed before starting any business.
Every founder I speak to has the same questions.
Is this actually a good market?
Who should we sell to first?
Which competitors should we worry about?
How big is the opportunity?
What should our GTM strategy look like?
Which countries should we expand into?
Where do we even find customers?
Today, answering those questions means jumping between dozens of tools.
You read industry reports.
Search Reddit. Use ChatGPT. Look through Crunchbase. Search LinkedIn. Analyze competitors manually. Build spreadsheets. Talk to people. Then try to connect everything yourself.
It can take weeks, and you're still making educated guesses.
That's the problem I'm trying to solve with IIDATECH.
Instead of being "another AI research tool," I'm building it as a connected business intelligence system.
1. Research Engine
The starting point.
You ask something like:
"Should our B2B SaaS expand into Germany or Singapore?"
Instead of giving a generic AI answer, the system gathers and evaluates evidence from multiple sources:
Market size & growth
Customer demand
Regulations
Competitors
Industry reports
Funding activity
Hiring trends
Pricing
Technology adoption
Macroeconomic indicators
The output isn't a summary.
It's an evidence-backed recommendation.
2. Business Intelligence Layer
Research is only useful if it answers business questions.
This layer transforms raw information into decisions.
Examples:
Which market has the highest probability of success?
Which ICP should we prioritize?
Which industries have the shortest sales cycle?
Which competitors are weakest?
Which positioning is differentiated?
Instead of collecting information...
…it recommends what to do.
3. Automation Layer
Once decisions are made, repetitive work shouldn't be manual.
For example:
Monitor competitors automatically.
Track market changes.
Watch funding announcements.
Detect regulatory changes.
Follow pricing changes.
Track hiring activity.
Monitor customer sentiment.
Instead of checking everything yourself, the platform continuously watches what matters.
4. Execution Agents
This is where I think things become interesting.
Once IIDATECH identifies an opportunity...
it shouldn't stop at saying:
"Healthcare in Southeast Asia looks promising."
It should ask:
"Would you like me to start executing?"
Then specialized agents could:
Generate prospect lists
Build outreach campaigns
Find channel partners
Create market-entry plans
Generate competitor battle cards
Prepare investor research
Produce sales collateral
Build GTM plans
Continue monitoring execution
Research becomes action.
5. Business Planning
Everything above feeds into a living business plan.
Instead of writing a static document once...
your strategy updates continuously as markets change.
The platform knows:
your industry
your competitors
your customers
your goals
your expansion plans
So recommendations become increasingly contextual over time.
The long-term vision
I'm trying to build an operating system for strategic business decisions.
Research feeds intelligence. Intelligence feeds planning. Planning triggers execution. Execution generates new data. That data improves future decisions. A continuous loop instead of disconnected tools.
I'm still early, validating the idea and speaking with founders.
I'd genuinely love feedback:
If a platform like this existed today, which part would you actually pay for first? Research? Competitive intelligence? GTM planning? Automation? Or autonomous execution?
