Look, we’ve all been there. There’s a report sitting behind a paywall or locked in a subscription you don’t have, and you need to understand what’s in it. Maybe it’s a Gartner study your competitor keeps citing, or an IDC forecast that keeps coming up in sales conversations.
Shadow research is basically reverse engineering that report using everything that exists around it. You’re not copying it—you’re building your own informed picture from the fragments that are already out in the open. If you follow the right steps, you might be able to get to 80% or even 90% of what key analysts are saying.
What we’re actually doing here
A shadow report is a reconstruction. You’re pulling together press releases, blog posts, conference slides, partner white papers, and anything else that quotes, references, or summarises the original work. From those pieces, you triangulate four things:
- The scope and structure (what categories and segments are they using?)
- The main drivers they’re tracking
- Ballpark numbers and directional trends
- The likely recommendations and storyline
The toolkit
I’ve found this works well with an AI tool that can work from a curated set of sources—NotebookLM is a excellent example. The key feature is that it stays within what you’ve uploaded rather than hallucinating from its training data. Pair that with solid web research, for example, using Gemini Deep Research to gather URLs for valuable content that touches your target report.
The process
1. Be specific about what you’re after
Name the exact report—title, author, publication date. Write down what you actually need to know: the taxonomy, the market sizing approach, growth patterns, recurring themes, and the type of recommendations you’d expect.
2. Build your source collection
Gather everything: programme pages describing the research, older editions if they exist, public summaries, conference presentations where analysts discussed findings, vendor collateral that cites specific figures. Load it all into your research tool with clear filenames so you can trace where each insight comes from.
3. Extract the skeleton
Ask the tool to infer the likely segments, categories, regions, and standard drivers. Push it to show which source each element is drawn from—you want to know you’re building on real citations, not guesses.
4. Be honest about numbers
Use quoted figures to triangulate ranges, not false precision. If you can anchor a number to a public source, great. If you can’t, use qualitative language—”mid-single-digit growth” or “low tens of billions”—rather than inventing a point estimate that looks more confident than you have any right to be.
5. Reconstruct the narrative
Prompt the tool to identify recurring themes across your sources: technology shifts, regulatory changes, buyer priorities, vendor responses. Draft your own synthesis in neutral language, making it clear this is inferred (“this shadow report suggests the original is likely to argue that…”). You’re not claiming to have the original—you’re showing what a reasonable reconstruction looks like.
How accurate is this, really?
Since you can’t see the full source, accuracy is estimated against what’s observable:
- Structural accuracy tends to be high. You can check your taxonomy against public descriptions, sample pages, or tables of contents.
- Directional accuracy is usually medium to high. If your growth directions and emphasis match multiple independent references, you’re probably on track.
- Quantitative accuracy is medium at best. Cross-check against publicly available numbers for adjacent markets to see if your implied shares and growth rates are plausible.
You could score yourself across structure, themes, and numbers on a simple scale and average them. Treat the result as an indicative fit, not a claim of fidelity.
The point is to be disciplined and transparent: grounded in what you can actually see, clear about what you’re inferring, and honest about the uncertainty around your reconstruction.
