Solution
For equity research, investment banking, and market intelligence teams: one governed company profile assembled from every web source you track, refreshed on a schedule and validated by your analysts.
The problem
Every web source needs its own extraction script, and every site redesign breaks one. The engineering backlog grows with the coverage universe.
Analysts stitch data points from dozens of tabs into one company view. That time comes straight out of model-building and coverage.
A profile assembled manually is current for a day. Decisions made three weeks later run on data nobody re-checked.
The product, not a promise
How it works
AI-based web data extraction pulls data points from the defined sources across the web.
Aggregation rules decide which values to keep and how sources reconcile when they disagree.
Hundreds of data points merge into one singular company profile.
Analysts review the scheduled profile and sign off before it feeds a decision.
Who it's for
Research analyst
Head of research
Data & IT lead
A large investment banking company builds its investment decisions on company profiles — and those profiles are only as good as the data feeding them. Analysts needed live data points from varied web sources, plus alternate datasets to strengthen their financial models. Getting there meant custom scripts per source and manual consolidation to stitch everything into a single company view. Every new source added maintenance; every refresh added toil.
The Botminds solution replaces per-source scripts with AI-based web data extraction governed by a rulebook: a declarative definition of which sources to scan, which data points to pull, and how to reconcile them when sources disagree. The platform scans the sources, extracts the defined data points, and consolidates them automatically into one company profile.
Because extraction is AI-driven, a source changing its page layout still reads correctly — the platform understands the page the way an analyst would. Adding a new source is a configuration change an analyst can make in an afternoon.
Profiles run on a weekly schedule, so analysts start each cycle with a current consolidated view instead of building one. Their role shifts to validation: reviewing the assembled profile, checking the data points that matter for the model at hand, and approving what feeds the investment decision. Each data point traces to the source it was extracted from, which makes validation quick and makes the profile defensible when a decision built on it is questioned.
The measured effect: time to create a company note dropped from 20 minutes to 6. Across a research desk and a coverage universe, the same team covers meaningfully more companies with fresher data. The pattern — many live sources, one governed consolidation, human validation before use — fits any research operation whose inputs live scattered across the web.
Objections, answered
Every data point traces to the source it was extracted from, and conflicting values are flagged instead of silently merged. Your analyst reviews and signs off on each scheduled profile before it feeds a decision — nothing reaches a model unvalidated.
Yes — the rulebook is yours. You define which sources to scan, which data points to pull, and which value wins when sources disagree. The platform applies your rules; it does not impose a vendor's view of a company.
Extraction is anchored to the meaning of the page rather than its markup, so layout changes rarely break it. When a page genuinely stops yielding a data point, the gap is flagged for review instead of silently dropping out of the profile.
The first sources and rulebook are configured in days, since adding a source is configuration rather than parser development. Most teams run their first scheduled refresh within the first weeks and expand the coverage universe from there.
Watch a live consolidated profile assemble from your real sources — with every data point cited and one conflict routed for your analyst's call.
Request a demo