Capabilities
Two things, and they are related
The first is finding data that is public but nobody has bothered to assemble. The second is a way of working that makes assembling it affordable. Neither is impressive on its own.
Part one
Finding data with agents
Two of the projects on this site are databases built from records that were always public. The work was not getting hold of them. The work was collecting them without quietly poisoning the result.
Four rules do most of that, and every one of them costs something in the short term.
Work out where the answer lives before searching for it
Which free sources can actually answer the question, and which are paywalled dead ends dressed up as leads. Doing this first routinely halves the time everything else takes.
Permission is a gate, not an afterthought
Collection is gated on robots.txt. A source that
disallows it is never touched — not throttled, not worked around.
Of the first 91 chamber directories reviewed for Boomer, 43 publish
a blanket disallow and have never been scraped.
That is roughly half the obvious sources given up on purpose. It is the right trade and it is worth knowing that is how we work before you hire us.
Never guess a value
If a field is not found, it stays empty. A wrong phone number is worse than a missing one — the missing one costs you a minute, the wrong one costs you a call, a bad impression, and the belief that the rest of the file is fine.
Provenance is stored alongside the data, not remembered. In
AdvisorSearch, data_lineage and
processing_log mean any single value can be traced back
to the filing it came from, months later.
A human keeps the irreversible decisions
When two records look like the same business, the system proposes the merge and stops. It never merges on its own.
This one is deliberate rather than unfinished. A wrong merge fails silently — a business vanishes from the pipeline and nobody ever notices it is gone, because there is no gap to see. Automating that step would save minutes and cost trust.
What we can source
These are sources already used in live work, not a wish list.
Federal filings
SEC Investment Adviser records — the full firm feed, the monthly ADV filing data, the individual adviser feed, and ADV Part 2 brochures parsed for succession language.
State records
Business registration records and professional licence files — primary sources, which is what makes a list verified rather than merely long.
Directories
Chamber of commerce and trade directories, limited to those whose terms permit it.
Whatever your market publishes
Most regulated industries file something publicly. If yours does, it can usually be assembled.
What we won't do
- Scrape a source that says not to. Not slower, not through a proxy, not at all.
- Fill a gap with a plausible guess. Empty fields ship as empty fields.
- Merge records automatically. The one manual click is the point.
- Resell data whose licence has not been checked. Collecting something and having the right to sell it on are two different questions, and they get answered separately.
If a project needs any of the above to work, it is not a project we can take.
Part two
Agent workflows
Software like this used to need a team. It now gets built by two crews of AI agents, each with a defined job, run against one written plan.
Nineteen roles across the two crews: two leads and seventeen specialists. Research establishes what is true and worth solving, then hands off to build. You never talk to any of them — that is the point.
The same nineteen roles, as text:
- research-lead — entry point, research crew
- source-librarian
- research-analyst
- primary-researcher
- quant-analyst
- data-curator
- fact-checker
- methodology-reviewer
- opportunity-analyst
- research-editor
- delivery-lead — entry point, build crew
- solution-architect
- business-analyst
- ui-ux-designer
- backend-developer
- frontend-developer
- fullstack-developer
- qa-automation
- devops-engineer
The parts that actually matter
Verification is structurally separate
The fact-checker re-verifies findings from scratch, without the original researcher's sources or notes, and is never the agent that produced the work. Self-auditing cannot give you that. It is a structural check, not a diligent one.
Two different questions get asked
The fact-checker asks whether a fact is true. The methodology reviewer asks whether the evidence justifies the claim being made from it. The second failure is more common and more damaging, and almost nobody checks for it.
QA loops against something written down
Not until it looks finished — until it meets a definition of done that existed before the build started. Every requested feature works, or the exception is stated in plain English.
Cost is a design constraint
Cheaper models do the grunt work, the expensive one is saved for planning and hard problems, and everyone works from a shared plan instead of re-reading the whole project. That is why a build like this is affordable at all.
How this site was built
By that crew, in about a day.
A written spec and a content file listing every fact that was allowed on the page came first, with every number queried live from the real databases so nothing could drift. A designer role built the design system. Four builders wrote the eight interactive demos in parallel, two more wrote the case studies, and everything was checked against a blacklist of claims — no invented metrics, no fabricated testimonials, no client data — before it went anywhere.
Two real bugs were caught in review and sent back to the agents that wrote them: a theme control whose label was inverted against the system setting, and a set of tab buttons that were submitting a form instead of switching views. Both were fixed by their authors, which is how the loop is supposed to work.
Everything you are reading is the output of the process it describes. That is the cheapest proof available, and it is the honest one.