Tech stack read for <company.com> with the gaps we fill

The problem this solves

A prospect's stack tells you how the conversation will go before it starts. A CRM first seen six years ago is entrenched; a marketing platform added last spring is still being rolled out; two analytics tools running side by side usually means one is on the way out. Reps get this read today by viewing page source, asking a friend who used to work there, or guessing from the job posts. The guess is wrong often enough that the first call is spent discovering what a lookup would have shown.

The half of the read that matters is the second half: where your product fits. A list of forty detected technologies is trivia until someone marks which ones you complement, which ones you replace and which ones you must integrate with to be considered at all. That marking is the part that never gets written down, so every rep on the account does it again from scratch.

How the mission runs

1. Detect the technologies by function. The Company Intelligence Agent runs technographics against <company.com> and its related properties, then sorts the detections into the five groups in your prompt: CRM, marketing, data, analytics and infrastructure. Detections outside those groups are kept in an appendix rather than dropped, since a payment provider or a support desk sometimes turns out to matter.
2. Date each detection. Every technology carries its first-seen and last-seen dates. The agent reads the pattern rather than just listing it: tools first seen in the last six months are marked as recent additions, tools whose last sighting is months old are marked as possibly retired, and pairs of tools in the same category are called out as overlap.
3. Mark where your product complements or replaces something. Using the description of your product from the prompt or your workspace profile, the agent annotates each relevant row: complements, meaning your product sits beside it and needs the integration; replaces, meaning your product covers the same job; or neutral. Each annotation carries a one-line reason, and rows with no clear relation stay unmarked instead of being forced into a category.
4. Write the read to a Google Doc. The Google Doc opens with a short summary - the three or four facts about the stack that shape your approach - followed by the five sections as tables with technology, category, first seen, last seen, status and your annotation. The evidence for each detection is linked so a skeptical rep can check it.

The prompt

This is the exact objective the agent receives. Swap the obvious placeholders for your own domain, segment or channel and run it as-is from the console, Slack, or the API.

What comes back

A Google Doc for <company.com> with a summary of what the stack means for your deal, five tables covering CRM, marketing, data, analytics and infrastructure with first-seen and last-seen dates, overlap and recent-addition flags, and a complements-or-replaces mark with its reason on every row that relates to your product. An appendix lists the other detections, and every row links to its evidence.

Make it yours

• Run it on the ten companies in list <name> and ask for one doc with a section per company, so a territory's stack patterns show in one read.
• Narrow it to one category - data and analytics only - when your product lives there and the rest is noise.
• Add a column for the competitor tools you most often displace, and the read doubles as a displacement list.
• Send the summary to a Slack deal channel with the doc linked, so the rep and the solutions engineer read the same page before the call.

Frequently asked questions

Go deeper

• Technographic and hiring-signal prospecting: the complete guide
• Company and person data for AI agents: the complete guide
• Outbound personalization that references evidence