Reading your first account brief.

▸Anatomy of a brief, at a glance

Every brief has the same 9 blocks in the same order. Some blocks are sparse for small or quiet accounts; none are ever omitted entirely. The order is deliberate — top-to-bottom matches a typical AE's read pattern of "is this real → what's happening → who do I talk to → how do I talk to them → who's next."

01The headline ("Why now")

One sentence. It answers: "What happened in the last 90 days that makes this account worth your attention?" If you only have 5 seconds for a brief, this is the 5 seconds.

Worked example

How to read it

The headline always names 2-3 concrete signals joined into a narrative. "Raised $275M" alone is a fact. "Raised $275M + hired 14 data roles + new VP Data from Stripe" is a story. The story matters because outreach that names the story "I noticed you're rebuilding the data stack" performs 4-7× better than outreach that names a single fact "congrats on the round". If the headline only names one signal, the brief is thin — try a lookalike with denser activity instead.

02Signals

The evidence behind the headline. Each signal is a card with: signal type · date · source · freshness pill · "why it matters" line.

The 8 signal types you'll see

• Funding — round closes, term sheet leaks, equity events. Deep dive →
• Hiring — JD posts, hiring spikes, specific role openings. Deep dive →
• Exec moves — VP/C-level hires, departures, internal moves. Deep dive →
• Tech changes — stack drops, adds, swaps detected from JS sniffing or DNS. Deep dive →
• Product launches — Product Hunt, blog announcements, changelog posts.
• Office moves — facility expansions or relocations.
• Job changes — your tracked people moving accounts. Deep dive →
• Custom bot detections — anything your custom crawlers were set to watch.

Signal freshness

Every signal has a freshness pill: Fresh (<7d) green · Active (7-30d) amber · Aging (30-60d) grey · Stale (60d+) grey-dashed. Fresh + Active are where your outreach should anchor. Stale signals are kept in the brief for context but shouldn't be the basis for the cold email. See the signal decay deep-dive for the math on why.

03Decision-makers

The 3-7 humans at this account who should be in your sequence. Mama pulls them from LinkedIn + the account's careers page + the team page on the website, then ranks by role tier × recent activity × thread-readiness.

Verified email pill means we ran the address through SMTP-level verification in the last 7 days — bounce risk <1%. Inferred email pill means we constructed it via the company's known pattern but couldn't verify (bounce risk ~10%). Pattern unknown means we couldn't construct it and you'd need to do enrichment.

The "thread-status" badge tells you if this person has been emailed by anyone on your team in the last 90 days — surfaced via the CRM integration. Don't double-touch threads someone else owns.

04Voice quotes

3-8 direct quotes pulled from earnings calls, blog posts, conference talks, podcast appearances, and customer interviews. These are the words the company and its leaders actually use — the vocabulary that maps to their priorities.

How to use voice quotes

Don't quote them back. Use them to match the abstraction level of your outreach. If the VP Data talks about "instrumenting the data flywheel," your email shouldn't say "we help with data warehousing." If a founder talks about "shipping iteratively to enterprise," your email shouldn't say "we accelerate digital transformation." Voice quotes are a vocabulary calibrator, not a quote library.

Each quote is sourced and dated — fresh quotes (last 90 days) carry more weight than 2-year-old conference panels. Mama orders quotes by recency by default but you can resort by speaker (e.g., "show me only VP Data's quotes").

05Suggested templates & playbooks

The matchmaker block. Mama looks at the signals + decision-maker tier and pulls 3-5 templates and 2-3 playbooks from the library that statistically perform best for this combination. One-click copy on templates; modal-open on playbooks.

How matching works

Templates are tagged with signal-type + persona + tone. When a brief's strongest signal is "funding + hiring," and the top decision-maker is a VP Data, Mama filters the 200-template library down to the ~12 templates tagged for that combo, then ranks by historical reply rate. You're not seeing all templates — you're seeing the matched ones. If you want to browse the full library, click "Browse all 200" in the suggested-templates header.

06Lookalikes — the next 5

3-5 accounts that match the same archetype as this one. Same signal pattern + similar persona density + similar firmographic shape. If this account is brief-worthy, these probably are too. Click any to generate a new brief — the lookalike inherits your current ICP scoring.

Lookalikes are how briefing scales from "1 account per day" to "10 accounts per day." Most experienced users brief one focal account, then run all 5 lookalikes. See the archetype matching deep-dive for how the ranking actually works.

07Trust markers — every fact, verifiable

Every scraped fact in a brief carries a verify pill — a small badge showing when we last confirmed the fact and its source. Hover any pill to see a "Report incorrect" action. This is the trust-marker system and it's load-bearing for the whole product.

What the pills mean

• Verified · <24h ago — green pill. Highest confidence.
• Verified · 1-7 days — green pill, dimmer. Fresh enough for outreach.
• Verified · 7-30 days — amber pill. Use but verify if stakes are high.
• Inferred — grey pill. Constructed from patterns, not observed directly. Most common for emails when the company hasn't published a pattern.
• Stale — grey-dashed pill. Last verified >30 days ago. Re-verify before outreach.

If a fact is wrong, click the pill and select "Report incorrect." Reports go into our verification queue and corrections typically propagate within 6 hours. This is how the data quality compounds.

08What to do next from a brief

The whole point of the brief is the action. Here's the if-then map most teams use.

Common reading mistakes