I spent six years in strategy and business development roles at companies building chips, sensors, and software for autonomous vehicles. Good companies. Smart people. Real budgets for competitive intelligence. And yet, every Monday morning, someone on the team was manually copying earnings call quotes into a spreadsheet, trying to figure out whether TSMC's CoWoS capacity constraint was going to slip our program timeline by a quarter.
That was the CI workflow. A spreadsheet and a prayer.
The Pain Was Specific, Not General
I want to be precise about what was frustrating, because "there's too much noise" is a lazy diagnosis. The problem wasn't volume. The problem was that none of the available tools were built for the questions we were actually asking.
We weren't asking "what is happening in tech." We were asking:
- Has TSMC made any new statements about HBM packaging capacity into 2026?
- Has Mobileye disclosed anything about next-gen ADAS SoC power targets that changes our competitive positioning?
- Which tier-1 automotive OEMs are shifting EV programs into slower ramp timelines, and what does that mean for our design win pipeline?
These are not questions a Google Alert answers. They're not questions a generic CI platform answers either — most of those tools are built for SaaS competitive monitoring (pricing page changes, G2 reviews, blog posts). Useful for someone tracking Salesforce. Useless for someone tracking the HBM3E-to-HBM4 transition timeline.
The "premium" alternative was consultants. We used them. A major strategy firm, a boutique semiconductor analyst shop, a few supply chain specialists. The work was often good — these are smart people with deep networks. But a $40,000 engagement that delivers a 60-page PDF eight weeks later has a structural problem: the questions you needed answered in week two are no longer the questions you're asking in week ten. The market moved. The earnings call happened. The export control rule dropped. The deck arrives and half of it is already footnoted with "as of [date three months ago]."
There was no option in the middle. No purpose-built, continuously updated signal layer for semiconductor and deep-tech strategy teams.
The Market Gap Was Hiding in Plain Sight
Semiconductors, EV/ADAS, and AI infrastructure are three of the most strategically consequential industries in the world right now. Combined, they represent trillions of dollars in capital allocation, thousands of strategic decisions made every year by product, BD, and corporate development teams. And the CI infrastructure serving those teams was — and mostly still is — either generic news aggregation or expensive, slow consulting engagements.
Nobody had built a purpose-built radar for this space. Not because the need wasn't obvious. Because it's genuinely hard. The signals that matter are technical, opaque, and scattered. You need to understand what a CoWoS yield ramp actually means to know whether a TSMC supply allocation shift is signal or noise. You need to track 30 different sources — earnings calls, JEDEC spec releases, CHIPS Act filings, patent databases, trade press — to get a coherent picture of HBM pricing dynamics.
We built the signal layer first. Manually curated, domain-classified, tagged with entities, event types, and severity ratings. The initial cut was about 200 signals. Then 400. Then we started tracking automotive signals alongside semiconductor signals, and AI infrastructure signals on top of that. By the time we had 800 signals with consistent taxonomy, something became obvious: the raw signal library was becoming genuinely valuable. But reading 800 signals a week was not something any human was going to do.
The AI Layer Wasn't the Plan
I want to be honest about this. The AI layer was not in the original design. The plan was a well-structured, searchable, filterable signal feed — better than anything else built for this domain. That was the product.
The AI became the product when we started living with the signal library.
When you have 1,000+ curated, high-quality signals with consistent structure and metadata, you have the raw material for something more powerful than a feed: you have the substrate for real synthesis. The question "what is happening with HBM supply chain right now" becomes answerable not just with a filtered list of signals, but with a coherent, sourced narrative — if you have an AI layer that can synthesize across dozens of signals, weight by recency and severity, and produce something readable.
That realization drove everything that followed: signal summaries with "so what" analysis and VC/corporate angle breakdowns, flash alert briefs that auto-detect correlated signal clusters, weekly vertical digests, on-demand AI reports generated by a multi-agent pipeline in minutes.
The signal feed is the foundation. The AI layer is what makes it usable at the speed the market moves.
Where We're Going
We're still early. The signal library now covers over 1,100 datapoints across Semiconductors, EV/ADAS, and AI Applications, all with AI analysis, embeddings, and quality scoring. The on-demand report pipeline generates quality-scored intelligence briefs — competitor analyses, supply chain assessments, market trend reports — in minutes rather than weeks. The War Room Copilot lets teams ask natural-language questions against the live signal library and get sourced, structured answers.
This is what we built because we needed it and it didn't exist. If you're working in strategy, BD, product, or corporate development in deep-tech — this is the tool we wish we'd had six years ago.