Reps who know the buyer, the market and why you win

Competitor moves, new objections and updated battle cards, posted to the channel your reps read. Training and quizzes built from the current version, not last quarter's deck.

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Works inand any MCP client

Question: Build a 45-minute session on selling to payments companies, so every rep can run a first call there by the end.

Used Calven MCP

Module 1, the buyer. The Head of Data at a payments company is measured on pipeline uptime and audit readiness. In their words: "Finance finds the broken report before we do."

Module 2, why they buy. Audit trails come first in 6 of 8 won fintech deals. Tell the Kestrel Payments story: fixed in six weeks.

Module 3, objections. "We already use Northwind" came up in 7 calls; answer with lineage depth from the battle card. "Does it work with dbt Cloud?" has no approved answer yet, so I left it out.

Selling to payments, 45 min.pptxPowerPoint deck

Here's the session deck, with speaker notes and the source behind every claim.

Sources: Head of Data persona · Fintech messaging · 8 won deals · Kestrel win story · Northwind battle card

Example workspace. Names and numbers are illustrative.

What can sales enablement teams use Calven for?

Digests, coaching notes, training and quizzes that start from the approved story and what buyers actually say on calls. Calven is the approved context your AI workflows need to scale across the team and still be trusted.

Use cases

Example prompts for weekly field updates

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Collect everything that changed for the field in the last [7 days], ordered by impact on the pitch.
Turn these changes into instructions for reps, with the asset behind each: [paste the changes]
Write this week's enablement digest for [#sales] from these items: [paste the items]
Check this digest against what Calven recorded before it goes out: [paste the draft]
Rank this week's competitor moves and product changes by the open pipeline each one touches, and tell reps only the top three.
Treat our win rate against [competitor] as a prior and update it with this week's evidence, so I know whether to escalate.
Did reps actually start saying what last month's digest told them to? Compare their call lines before and after.
Any new objections on calls this week? How many calls each?
What did we release in the last 30 days that reps might not know about?
Give me the three things a rep should do differently this week, with sources.

Open the full prompts on GitHub

Example prompts for talk track audit

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Audit our talk track against what reps said and buyers raised in [last quarter].
Which talk track lines show up in won deals and which in lost ones, over [last two quarters]?
Flag every claim in this talk track that is wrong, stale or unsupported: [paste the talk track]
Turn this audit into a retraining plan, sessions in priority order: [paste the audit]
Replay our lost deals as if the rep had used the approved talk track. Which losses would it have saved, and which never?
Hold a debate on whether [pillar] stays in the talk track, with prosecution, defence and a judge working only from evidence.
Plot every talk track line by how often reps say it and how often it shows up in wins, then tell me what to stop saying.
Which pillar do reps say least on calls?
What do reps say instead of our approved line on "[objection]"?
Which talk track section has no customer evidence behind it?

Open the full prompts on GitHub

Example prompts for call coaching

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Pull what buyers and [rep name] said on calls in [the last month].
Check [rep name]'s lines against our messaging and product brief: [paste the lines]
Write a one-page coaching note for [rep name] from this evidence: [paste the evidence]
Show me which objections, pillars and claims the team needs coaching on this quarter.
List the objections reps answered on their own in [last quarter], with no approved line.
Which things reps say on calls actually go with won deals? Run a driver analysis across every rep line and deal outcome.
Have my managers score the same calls, measure how much they disagree, and fix the scorecard until they agree.
Take the objection a rep fumbled on a real call and drill it with them five times, harder each round, scored every time.
What objections did [rep name] hear on calls this month?
Did anyone over-promise on [capability] in the last 30 days?

Open the full prompts on GitHub

Example prompts for battle card updates

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Brief the reps on what changed in the [competitor] card, in under 150 words.
Check the [competitor] card against what buyers said in deals [last quarter].
Find the thin sections and stale claims in the [competitor] card before I push it to reps.
Rank our battle cards by how much they need an update, worst first.
War-game [competitor] over three rounds of move and counter-move, then pre-write the battlecard changes for the moves they're most likely to make.
Test every "we win when" and "we lose when" line on the [competitor] card against last year's closed deals. Which lines actually predict the outcome?
Let [competitor]'s best AE read our battlecard and tell me how they'd beat every line on it in a live deal.
What changed in the [competitor] card?
What is our win rate against [competitor] this year, and the top loss reason?
Which landmine should reps set against [competitor]?

Open the full prompts on GitHub

Example prompts for objection libraries

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Rank every objection buyers raised in [the last two quarters] by the deals it touched.
Write the approved answer and proof for each objection in this list: [paste the list]
Check this objection library for over-claims, then read it as our personas: [paste the library]
Tell me what to add to and retire from our objection library: [paste the list]
Price every objection in lost and stalled pipeline, then show the Pareto: which few objections cost us most of the money.
Pit three answers to our hardest objection against each other, judged by our real personas, and crown a winner.
Classify every objection as push, pull, anxiety or habit, and tell me which force each answer should really be fighting.
What are the five objections we hear most this quarter?
What persuaded buyers who raised "[objection]" and still bought?
Did any objection appear for the first time this month?

Open the full prompts on GitHub

Example prompts for launch readiness

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Write the one-page field launch brief for [capability].
Write the week-one field FAQ for [capability], plus what reps must not claim.
Write the [capability] talk track and how it changes our position against [competitors].
Write the [capability] certification and list the rep assets this release makes stale.
Find every deal we lost for lack of [capability] and model how much pipeline the launch could realistically win back.
List every way the field could get this launch wrong on day one, score each by severity, likelihood and how hard it is to spot, and fix the worst.
Run each rep through five buyer conversations about the launch, harder each time, scored against the brief, before they're cleared to pitch it.
What is the approved one-liner for the [capability] launch?
What do we not support with [capability]?
Which battlecards need updating because of [capability]?

Open the full prompts on GitHub

Example prompts for onboarding plans

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Build the week-one reading pack for a new [AE] starting Monday.
Build the persona, competitive and practice modules for days 8 to 30.
Write the 30-day certification for a new rep, with the source for every answer.
Tell me what went stale in this plan since the cohort that started [date]: [paste the plan]
Plot how long past hires took to close their first deal, and find what the fast ramps had in common.
Turn our ICP, personas, objections and battlecards into a 90-day spaced repetition schedule weighted by what actually comes up in deals.
Have a sceptical VP Sales tear apart my 30-60-90 plan using our real sales cycle and loss data, then fix what breaks.
Summarise our ICP in five bullets for a new rep.
Which objections should a new rep expect in their first ten calls?
What proof points can a new rep use in week one?

Open the full prompts on GitHub

Example prompts for training sessions

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Tell me what the next three enablement sessions should cover, based on the evidence.
Build a 45-minute session on [topic] so every rep can [behaviour] by the end.
Build the 30-minute "what changed this quarter" module for the [Q1] kickoff.
Score my candidate training topics by the pipeline each one touches and the win-rate lift it could plausibly buy, then pick the quarter's sessions.
Rebuild one real lost deal as a business-school case with decision points, so reps argue what they'd do before learning what the buyer said.
Which objection should this week's session cover?
Give me two buyer quotes about [topic] to open the session with.
Which pillar has the lowest pull-through?
Which objection did reps handle worse this quarter than last, judging by lost-deal drivers?
Which pillar do our top-closing reps say on calls that the rest of the team doesn't?

Open the full prompts on GitHub

Example prompts for certification quizzes

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Write a [20]-question certification bank on [competitor], with the source for each answer.
Write five scenario questions from real objections, with model answers and grading points.
Check this question bank from [date] against the current documents: [paste the bank]
Run item analysis on the last cohort's quiz results to find questions that are too easy, too hard, or reward the wrong reps.
Build an interactive quiz that gets harder when the rep is right and easier when they're wrong, so it finds their real level in 12 questions.
Write ten multiple-choice questions on our ICP with answers.
Write a scenario where [persona] says "[objection]" and the model answer.
Write three questions on what we do not do.
Which facts in our product brief changed since last quarter's certification was written?
Which competitor appears in the most deals but has the thinnest battlecard to quiz on?

Open the full prompts on GitHub

Example prompts for win stories

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Find the wins from [the last two quarters] that make the strongest stories.
Write the win story for [deal], with three lines a rep can quote on a call.
Find the win story that fits [open deal] and the lines the rep should use.
Before reps tell this win story everywhere, check whether its reason for winning holds across our other deals or was a one-off.
Simulate the story being retold rep to rep to buyer, see what gets lost or exaggerated, and rebuild it so the truth survives.
Why did we win [deal], in the buyer's words?
Which win story fits a [persona] evaluating us against [competitor]?
What decided our wins in [segment] this year?
Which won deal had the most competitors in it, and what tipped it?
Which buyer in a won deal almost chose someone else, and what nearly cost us the deal?

Open the full prompts on GitHub

Example prompts for competitive drills

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Play [persona] at a [segment] company evaluating us against [competitor]. I am the rep.
Score this call against the [competitor] battle card: [paste the transcript]
Tell me which four competitor and persona drills to build this quarter, where we lose most.
Let the AI play [competitor]'s rep pitching the same buyer right before me, then have the buyer pick a winner and explain why.
Build a five-level drill ladder against [competitor] from real lost-deal moments, with a mastery bar before a rep moves up.
Play [persona] evaluating us against [competitor] and push back on price.
Where do we lose to [competitor], and what should I avoid saying?
What is our proof point against [competitor]'s time-to-value claim?
Which competitor beats us most often in deals with a technical buyer?
What's the question [competitor]'s buyers ask us that reps have never had a good answer to?

Open the full prompts on GitHub

Example prompts for persona cheat sheets

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Write the one-page cheat sheet a rep reads before a call with [persona].
Check this [persona] cheat sheet against the approved canvas: [paste the sheet]
Which personas need a cheat sheet most? We have these: [paste the list]
Measure how much each persona's presence on a deal really changes win rate and deal size, and rewrite the cheat sheets around who matters.
Simulate a working week as [persona], hour by hour, to find the moment our pain hits and the moment they'd actually take a call.
What is [persona] measured on?
s top three pains, with a quote?",
Which persona is listed as a blocker most often on our lost deals?
What does [persona] say about vendors in general, before we even pitch?
Which persona's objections changed most between last year and this year?

Open the full prompts on GitHub

Example prompts for one-pagers

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Draft a [buyer-facing] one-pager for [persona], every claim sourced.
Fact-check this one-pager and review it as [persona]: [paste the draft]
Which one-pagers are we missing by persona and competitor? [paste our list]
Show my one-pager to each persona for five seconds, then sixty, and tell me what they remember and whether they'd forward it.
Run a conjoint-style test with our personas to find which mix of headline, proof and call to action my one-pager should lead with.
What proof point should a [segment] one-pager lead with?
Is "[claim]" in the product brief?
Which one-pagers did the last release make stale?
Which value pillar has the strongest quantified customer quote for a one-pager?
What's the one sentence [persona] would use to forward a one-pager about us?

Open the full prompts on GitHub

Example prompts for field FAQ

Ask any of these in Claude, ChatGPT or any AI tool connected to Calven.

Answer this month's rep questions from our approved documents: [paste the questions]
Check this FAQ for answers that changed since [date]: [paste the FAQ]
List the questions buyers asked on calls that our FAQ misses: [paste the FAQ]
Cluster a year of Slack questions from reps, find which ones the docs answer but nobody can find, and which the docs don't answer at all.
Look at what reps and buyers asked after our past releases and predict the questions the next one will trigger, with answers ready on launch day.
Do we integrate with [system]?
What is in the [plan] tier, and what is not?
Where is the claim "[claim]" supported?
Which questions do buyers ask on calls that our FAQ doesn't answer?
Where do two of our documents give different answers to the same question?

Open the full prompts on GitHub

No new tool to learn

Calven works where your team already does: in Slack, and in Claude, ChatGPT or any AI tool through Calven MCP. Nobody opens another app to get the approved story.

In Slack

Mention @Calven in a thread or send it a DM. It answers from the approved version, with sources.

CalvenAgent9:00 AM

Weekly update from Competitive Intelligence

Published: Battle card: Northwind
Published: Battle card: Vantage
New competitive signal approved: Northwind cuts mid-tier price 15%

Jess Moreno9:12 AM

@Calven Brief the reps on what changed in the Northwind card, in under 150 words.

CalvenAgent9:12 AM

Price: Northwind cut its mid tier 15%. Drop "half Northwind's price" from every email and call.

Lineage: they bundle it now, so don't say it costs extra. Say: "Northwind shows lineage. We show which report breaks and who it hits."

Unchanged: on-premises is still their edge with banks. Raise hosting early.

Sources: Northwind battle card

Example workspace. Names and numbers are illustrative.

In your AI tool

Add Calven to the AI workflows you already run. Every step reads from the approved version, with sources.

Question: Audit the Northwind section of our talk track against what reps said and buyers raised last quarter.

Talk track, Northwind sectionGoogle Doc

Used Calven MCP

Holds: the lineage-depth answer. Buyers repeated it back in 5 of 7 deals we won against Northwind.

No longer holds: "Northwind charges extra for lineage". They bundle it now, after this month's price change. Rewrite: "Northwind shows lineage. We show which report breaks and who it hits."

Missing: "Does it work with dbt Cloud?" came up on 4 calls and has no approved answer. Get one from product marketing before reps improvise it.

Sources: Northwind battle card · 7 won deals · Northwind price change · Last quarter's calls

Example workspace. Names and numbers are illustrative.

What changes when sales enablement's AI tools read from Calven?

Enablement teams using AI without a shared source get fast training on last quarter's story. Connected to Calven, every digest, coaching note, quiz and session starts from the same approved go-to-market context.

Pain point	Without Calven	With Calven
Field updates	Battle cards updated quarterly, if someone remembers to tell the field	What changed posted to the reps' channel every week, with the source
Coaching	Coaching from the calls a manager happened to hear	Coaching from the objections reps heard most, checked against the talk track
Training	Onboarding and quizzes rebuilt by hand after every launch	Sessions, onboarding and quizzes built from the approved version, answers sourced
Talk track	A talk track that drifts from the story the week after training	An audit of what reps say on calls, with the sections that no longer hold
Objections	An objection library brainstormed two years ago	Objections ranked by the deals they touched, each with an approved answer and proof
Accuracy	Reps pitch claims the last release made stale	Every claim in a card, FAQ or one-pager checked against the approved product brief

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A day is all it takes to see what the agents can do for you.
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Questions, answered

How does Calven help sales enablement teams?

It keeps the material enablement depends on current: battle cards, positioning, messaging, personas and win/loss evidence. Enablement gets a weekly summary of what changed, coaching notes from real objections, and training built from the approved version.

Can Calven post battle card updates to Slack?

Yes. The Calven Slack app posts competitor moves, new objections and battle card changes to the channel your reps read, and reps can ask follow-up questions in the thread.

Does Calven replace our enablement platform?

No. Your LMS and content library stay where they are. Calven is the source the content is built from, so what you publish there matches the current story.

Do sales enablement teams need to log into Calven?

No. Product marketing runs Calven. Everyone else reaches the same approved context from the AI tool they already use over Calven MCP, or in Slack. Professional and Enterprise include unlimited seats, so each person signs in as themselves.

Which AI tools work with Calven?

Any client that supports remote MCP: Claude, ChatGPT, Cursor, VS Code with GitHub Copilot, Claude Code, Codex and others. Paste https://api.calven.ai/mcp, choose OAuth and sign in.