DEF 14A

DEF 14A filings read by encoded rules, not analysts.

We encode your study’s rules — disclosed trumps plan tables, targets never mix with actuals — extraction runs across the coverage universe, every value cited.

  • AI implementation services
  • Your study’s rules, encoded
  • Every value cited to its filing page
  • Low-confidence fields queue for review

Built with the leading AI platforms

NVIDIAAnthropic
Peter Enestrom, founder of Zaigo

Led by Peter Enestrom and the Zaigo AI & engineering team

YaleColumbia UniversityMicrosoft

The broken Friday

The proxy isn’t the problem. The reading is.

From the partners, analysts, and research directors we have sat with: boutique exec-comp advisors and executive compensation benchmarking teams covering dozens to thousands of public companies — not shopping for software, drowning in the reading.

  • 150-page proxy statements read by hand

    Board and pay data comes out of DEF 14A filings one company at a time, some running 200 pages, while the study waits on the reading.

  • 150 granular extraction rules per study

    Each pay study needs roughly 150–160 fine-grained field rules, derived by hand from a high-level requirements document before extraction can even start.

  • Disclosed figures trump plan tables

    The rulebook says non-disclosed must never default to zero — salary, STI, and LTI targets land in the workbook with per-field confidence flagged.

  • Target vs actual comp values

    Analysts need target values for salary, STI, and LTI — mixing in actuals corrupts the study, and the rebuild happens every single study.

  • Same prompt, different numbers

    AI outputs that vary run to run cannot be defended to clients — consultants need reproducible numbers with a citation behind every value.

What manual costs

One analyst, one CD&A, one company at a time.

What the manual way looks like at a consultancy whose studies span dozens to hundreds of proxies, with coverage universes up to the Russell 3000. Your numbers will differ — the calibration study puts figures on yours before anything scales.

150–200 pagesThe filings an analyst reads one company at a time to pull salary, STI, and LTI fields
~150–160Fine-grained extraction rules each pay study needs, derived by hand from the requirements doc
Russell 3000Coverage universes the studies span — the reading does not scale by hand

From anonymized engagements — exec-comp consultancies and benchmarking teams, studies spanning dozens to hundreds of proxies

How it works

We encode your study’s rules.

No new system for your team to learn. The extraction rules your senior consultants carry become how every filing gets read.

  1. 01

    Calibrate against ground truth

    We run the encoded rules against your past hand-built studies and match the analysts’ answers line by line before anyone trusts the output.

    First study
  2. 02

    Encode the study’s rules

    Disclosed figures trump plan tables, non-disclosed never defaults to zero, targets never mix with actuals — roughly 150–160 field rules, documented and yours to keep.

    Per study
  3. 03

    Exception-only review

    Extraction runs across the whole coverage universe; low-confidence fields queue for an analyst, and approved rows lock and carry into the next study.

    Every study
Not another tool

The data subscriptions sell everyone the same numbers. Your study’s rules aren’t in the package.

The data subscriptions sell everyone the same pre-packaged numbers. The dev tools hand an engineer raw filings. Neither encodes your study’s rules — disclosed over plan tables, target never mixed with actual. We encode those.

Our AI does the reading of every DEF 14A, CD&A, and 8-K in your coverage universe; your encoded rules do the judging — which fields count, which table wins a conflict, what gets flagged for an analyst.

Peter Enestrom, founder of Zaigo
Who builds it

Led by Peter Enestrom.

Co-Founder — leads AI & Engineering

Pete Enestrom

Every engagement is led personally by Pete, working with the Zaigo AI & engineering team from the two-week audit through the production handover. The person who scopes the work is the person who builds it.

Education
Yale & ColumbiaGraduate
Background
Microsoft & IntelFormer
Experience
Exited FounderVenture-Backed

Background

Questions

Asked by comp consultants and research teams.

The straight answers, before you book anything.

What’s holding your business back?

A workflow ready for automation. An AI product you want to build. A problem that has sat on the roadmap for years. Let’s talk about what it would take to solve it.

Talk to Zaigo