Jump the Hurdles, Win the Race: Precedent Analysis as the Starting Line

Anyone who’s ever dug through ADAMS knows the feeling: ten browser tabs open, each with a 400-page PDF, and that nagging suspicion you missed the one RAI that mattered. NRC search has improved, but you still end up buried in documents with no synthesis.

Serious licensing work doesn’t start with drafting. It starts with precedent analysis—understanding what’s been done, what went wrong, and what the regulator already knows by heart. Everything else builds on that.

Find the best and worst of the past

When we approach precedent, it’s not just so we can make you a fillable template. It’s about seeing the range of what’s been tried—the solid, the sloppy, and the painful.

Our AI tools reconstruct entire histories of license amendments and applications. Think of it like a track race. The finish line is the amendment, and every supplement or RAI cycle is a hurdle in between. The system traces them all and shows where each applicant stumbled or cleared the bar.

Within each case, you can see what worked. Across dozens of them, you can see patterns that explain why.

Decide your philosophy before you write

Precedent analysis also forces a choice about who you want to be as an applicant.

Some teams give the regulator everything—every calculation, every attachment—hoping to minimize questions. (Or maybe to drown them in paperwork and confuse them? We weren’t always sure at the NRC.)

Others strip it down to first principles and try to explain their logic.

Most end up somewhere in between.

Like the old “speed, cost, quality—pick two” rule, each approach has trade-offs. Too much detail and you over-commit yourself. Too little and you look evasive. If you’re doing something new, you need to show why it’s different. If you’re doing something that’s been done 97 times, you need a very good reason to deviate.

Don't waste time on manual digging

Until recently, precedent research was pure endurance. A power uprate meant pulling dozens of amendment packages, hundreds of RAIs, thousands of pages. A new reactor license meant tracking every design certification, early site permit, and prior license approval. Hundreds of hours, with no guarantee you found what mattered.

Some teams still repeat this exercise from scratch every time. Smart teams work with us to automate it.

Everstar’s precedent analysis links every document in the chain—application, RAI, supplement, approval—and organizes them into a timeline. It shows which issues drove contention, how complex the review became, and what the applicant did right or wrong. Once the framework is set, AI delivers in hours what once took months.

It’s the difference between running a race in the dark and flipping on the stadium lights.

Learn from history—especially the parts people skip

Patterns emerge fast: missing data that triggered new RAIs, vague language that wasted months, communication gaps that soured reviews. The same system shows what worked: clear logic, traceable claims, consistent framing.

But don’t take precedent at face value. Garbage in, garbage out still applies.

If you only look for RAIs, you’ll miss the bigger story—especially as the NRC shifts toward faster cycles and more audits.

And don’t just study what got approved. Look at what happened after. In license renewal, some commitments are now being rolled back because they couldn’t be met. In the AP1000 builds, plenty of paper assumptions fell apart once construction started. Proper research tells the full story and lets you plan.

Start smarter, finish faster

Precedent analysis isn’t busywork. It’s the entry condition for credible, efficient licensing.

Before you draft, understand the field. Know where others tripped, and why. Then write confidently.

If your next license renewal, power uprate, or new reactor application deserves that level of rigor, reach out to Everstar. We’ve already done the homework.