Automated data collection for the deadlines that never move.
We build AI agents that pull your external data — surveys, filings, portals, public sources — on schedule, normalize it, and QC it into one structured store, so your analysts start at the analysis.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
The analysis was the job. The collecting ate it.
From the consulting and research firms we have sat with: analysts whose week is a calendar of sources — surveys, filings, client portals, public data — each one pulled by hand before the real work can start.
Every source, pulled by hand
Surveys, filings, portals, public databases — each with its own login, format, and schedule. An analyst’s week is a route of websites and spreadsheets, walked on deadline.
The same pull, every cycle
The same sources are re-collected every quarter, every month, every study. Nothing about the repetition makes it faster — it just keeps coming back.
Collection eats the analysis window
By the time the data is gathered, cleaned, and joined, most of the deadline is gone. The thinking the client pays for gets the hours that are left.
The AI tool that didn’t know your sources
The last automation attempt fetched pages fine but knew nothing about your source list, your QC rules, or what clean means here — so the team learned to collect around it.
The process lives in one person’s head
Which portal, which login, which spreadsheet tab — the collection runbook is tribal knowledge. When that person is out, the deadline isn’t.
Collecting the data costs more than the hours.
What the manual way looks like at a professional-services firm whose analysts pull surveys, filings, and portal data by hand. Your numbers will differ — tracing one collection cycle puts figures on yours before anything gets built with AI.
From an anonymized engagement — an executive-compensation consultancy whose analysts collected source data by hand
Data collection automation that runs on your calendar.
No new system for your team to learn. The collection run your best analyst would execute on every source becomes the system — AI agents run it on schedule, and people handle the exceptions.
- 01
Trace one collection cycle
We follow one deadline end to end — which sources, which logins, which formats, where the cleaning and QC actually happen.
- 02
Encode the collection rules
Source lists, schedules, normalization rules, QC checks — documented with your analysts and encoded as rules you own.
- 03
AI agents run the calendar
Every source is pulled on schedule, normalized, and QC’d into one structured store; a failed pull or a QC flag lands with a person, the reason attached.
Tools fetch pages. AI agents deliver the data.
The scraping tools, APIs, and CRM add-ons are genuinely good at what they are built for: fetching pages, parsing markup, filling a field. What they cannot know is which sources matter to your study, what clean means for your data, or what to do when a portal changes its login.
We advise on the collection strategy, then build and run the layer itself — scheduled AI agents in your cloud, on rules encoded from how your analysts actually work, landing QC’d data in one store you own. You do not buy a tool and staff it; you get the data.
If it takes you ten minutes to do when it should just be a button to click, let me know.
Led by Peter Enestrom.
Asked by consulting and research leaders.
The straight answers, before you book anything.
Automated data collection is using software — increasingly AI agents — to gather external data on a schedule instead of by hand: pulling from surveys, filings, portals, and public sources, normalizing the formats, checking quality, and landing it in one structured store. Done well, an analyst starts at the analysis; done generically, it fetches pages nobody can use.
Tools fetch pages; they do not know your source list, your normalization rules, or what QC means for your data — so the collecting gets faster and the cleaning stays manual. We build AI agents on rules encoded from your analysts and run them in your cloud, so what lands in the store is already checked against how your firm actually works.
Same pattern, different data type. Publisher survey files and public-company filing reading are specific collection problems, each covered on its own page; this page is the general layer — any external source your firm collects from, on any schedule. All three run the same way: encode your rules, let AI agents apply them, route only exceptions to people.
That is the normal case, not the exception. Where a portal or a legacy system offers no API, AI agents work through it the way a person does — browser sessions and scheduled exports — so collection runs without waiting on an integration project. Legacy systems slow the work down; they do not block it.
QC is encoded, not eyeballed. Every pull is checked against your rules — expected ranges, required fields, source citations — and anything that fails lands in an exception queue with the reason attached, so a person reviews a short list of real problems instead of re-checking every record.
You do. The agents run in your cloud, the structured store is yours, and the encoded rules — source lists, schedules, QC checks — are documented IP you keep. We advise, build, and run the machine; nothing about it is rented back to you.
Start with one workflow.
Tell us where your team loses hours. We will come back with a straight answer on whether AI can help, what it would take, and what it would pay.