The sales brain that tells reps their next move
For CROs · VP sales · Sales enablement leaders
Your reps don't have an information problem. They have a next-move problem.
The objection handling that closed the Fortinet deal is buried in a call from three months ago. The competitive positioning that worked against Zscaler is in a Slack thread nobody can find. The product translation that made a security-skeptic CIO lean forward lives in your best rep's head — and only there.
Every company builds knowledge. Almost none of it reaches the rep at the moment it can change their behavior.
That gap is not a search problem. It is a timing and translation problem. The insight delivered after the deal dies is a retrospective. The same insight delivered one hour before the call — already filtered to this deal stage, already translated into field language, already connected to what worked in past wins — is a next move.
Zime AMA gives every rep their next move. Not search results. Not transcript snippets. The specific action that the best rep on your team would have known to take.
Tenarai (enterprise AI services, 200-person GTM) started with an average of 3.5 fatal objections per call. After Zime AMA was deployed, that dropped to 1.9.
The problem with every tool your team already has
A rep is 45 minutes before a call with a prospect who mentioned Palo Alto in the last meeting. They need to know: what objections have competitors raised against us, and how have we handled them when we win?
Here's what actually happens: they Slack a senior rep. That person is in their own meeting. They search the CRM. Nothing useful comes back. They open Gong and try to find the right call. 20 minutes later they are on the call less prepared than they were before they started.
The institutional knowledge exists. It never reached the rep at the moment it could have changed something.
Conversation intelligence (Gong, Chorus)
Summaries are accurate about what was said. They have no awareness of what mattered — the one signal a rep should act on.
"Gong is a graveyard of call recordings."
— Ryan, VP Sales, Postman
Enterprise search (Glean, Copilot)
Built for general employees. Wrong architecture for reps who need how to win this deal type, against this competitor, in this stage.
"If you want generic search, use Glean. If you want your reps to know how to win the deal, use Zime."
— Zime positioning
Call-queryable AI
Returns text snippets across hundreds of calls. The information exists. The synthesis doesn’t — and that last mile never happens.
"This imperfection hinders the ability to classify calls correctly and limits the functionality of asking questions based on the type of call."
— G2 review
The structural failure across all of them: they solve retrieval. None of them solve the last mile — translating what exists in the corpus into the specific next action a rep can take in the next conversation.
99% of sales calls are never reviewed. Every objection handled, every competitive signal, every moment of champion language — it goes into a recording that nobody watches and a transcript that nobody reads.
Related: Why your CRM can't tell you what's actually happening in deals →
Why knowledge graphs help — and where they still fall short
General-purpose knowledge graphs fail in GTM for five structural reasons:
The entity problem
Your reps say "SASE." Your CRM has "Versa Unified SASE." Your top competitor is "PA" in the field and "Palo Alto Networks" in product docs. Generic graphs treat these as different entities.
The ontology problem
Enterprise graphs model people/content/activity. GTM graphs must model stage → objection → behavior → outcome relationships.
The criticality problem
Signals that matter vary by deal type and stage. Zime configures critical signals per motion, so the answer is the right next move.
The language gap
PMM writes product language. Reps need field language. Zime translates between them so answers are immediately usable on the next call.
The outcome gap (most important)
Generic graphs are trained on what's in your data. Zime is trained on how you win — weighting patterns from won deals more heavily than patterns that didn't.
How Zime AMA works
Zime AMA is not a chat interface on top of call recordings. It is a GTM context graph built by humans who understand your sales motion, queried by an AI that understands what the rep is actually asking — and returns not a set of results, but a specific next action.
Layer 1: the FDE-built context graph
FDE interviews sales leadership, listens to wins/losses, maps product language to field language, identifies critical signals in each deal stage, and configures the graph taxonomy. Only then does indexing begin.
What gets indexed: recorded calls, CRM, battlecards, pricing docs, win-loss analysis, competitive positioning, playbooks, enablement content — all tagged to your GTM taxonomy.
Layer 2: the query engine
When a rep asks a stage + competitor + ICP question, the system queries the graph and surfaces what top reps said and what happened next — a synthesized next move, not snippets.
Technical term: query_entity_insights
Structured deal signals linked to outcomes — not unstructured text search.
What Zime AMA returns — and what others can't
| Question | Conversation intelligence | Enterprise search | Call-queryable AI | Zime AMA |
|---|---|---|---|---|
| "How do we handle the Fortinet pricing objection?" | Finds transcripts with "Fortinet" and "pricing" | Surfaces battlecard (may be outdated) | Returns 30–50 transcript snippets | Returns what top reps said in won deals against Fortinet, by stage |
| "What's blocking the three deals stuck in technical validation?" | No deal-level synthesis | No deal-level awareness | Surfaces recent call summaries | Returns deal-specific blockers linked to CRM stage |
| "What does a champion in a won SASE deal say in the first three calls?" | No outcome correlation | No outcome awareness | Can't correlate language to win/loss | Correlates champion language patterns to deal outcomes |
| "What objections is our new product getting vs. our core product?" | No product-level segmentation | No product taxonomy | Returns mixed results | Filters by product taxonomy configured in your graph |
| "What are the top three things product needs to fix based on lost deals?" | No win/loss signal | No win/loss signal | Can't synthesize across outcome | Synthesizes objections tagged to closed-lost, by quarter |
What customers said — in their own calls
Sprinto
Amit Borges, Commercial AE
"Two things I particularly liked... pipeline review gives me better visibility... and the action items... go through my last five calls, tell me what I need to do next — that is something I am going to use."
The unlock: a rep managing multiple deals can ask what needs to happen across all of them and get a synthesized action list — not transcripts.
Bureau
Anil Srinivas Tadimeti, RevOps and Sales Leader
"Where are we losing deals we should be winning? Where are we 30% off, where are we 50% off on product fit? Which competitor names come up most often?"
Not search questions. Strategic next-move questions answered from Bureau’s call corpus — segmented by product, stage, and outcome.
Tenarai (formerly Infogain)
Tarangita Gupta, VP of GTM Transformation
"AMA is only holistic when it has documents, meetings, and Salesforce. It answers in sales language, not product language."
3.5 fatal objections per call before AMA. 1.9 after. Reps started arriving with a next move ready.
MyAdvice
Chad Erickson, CEO
"Michael does a better job getting demos set... Robbie does a better job converting demo to held. I want to understand what they’re doing differently in those specific stages."
This question is answerable from the call corpus correlated to outcomes — not dashboards. It becomes next week’s coaching plan.
Versa Networks
Mickey Singh, Global Head of Sales Enablement
"In the age of AI, if you can't provide the right context to agents, your AI transformations will fail."
Reps ask stage + competitor + product questions and get next moves grounded in Versa’s motion — not transcript search.
What changes — the aha moments
The insight already existed in the corpus. What changed is when it arrived and in what form — translated into a next move.
For reps
Tenarai went from 3.5 fatal objections per call to 1.9 (46% reduction) because reps stopped walking into calls unprepared.
For managers
Ask “top objections across deals that died in technical validation” and get a coaching agenda without reviewing calls.
For product and PMM
Ask “what is the field hearing about our new product” and get verbatim quotes from calls — not anecdotes.
For leadership
Ask “where are we losing deals we should be winning and what is the common thread” and decide in the room.
Zime AMA vs. the market
| Capability | Conversation intelligence | Enterprise search | Call-queryable AI | Zime AMA |
|---|---|---|---|---|
| Searches call recordings | ✅ | ❌ | ✅ | ✅ |
| Searches documents and battlecards | Limited | ✅ | ❌ | ✅ |
| Correlates answers to deal outcomes (wins vs. losses) | ❌ | ❌ | ❌ | ✅ |
| Understands your product taxonomy and deal stages | ❌ | ❌ | ❌ | ✅ |
| Segments answers by ICP, product line, deal stage | ❌ | ❌ | ❌ | ✅ |
| Resolves entity variants (SASE = Unified SASE = "sassy") | ❌ | ❌ | ❌ | ✅ |
| FDE-configured per customer GTM motion | ❌ | ❌ | ❌ | ✅ |
| Returns a next move, not transcript snippets | ❌ | ❌ | ❌ | ✅ |
| Connects to CRM for deal-level filtering | Limited | ❌ | Limited | ✅ |
| Answers update as new calls come in | Limited | ❌ | Limited | ✅ |
Who Zime AMA is built for
50+ sales, SE, or CS users with complex product or multi-product GTM
Frequent competitive objections handled inconsistently across the team
A gap between product language and field language
Senior reps who are bottlenecks for objection handling and deal prep
Recorded calls but no systematic way to turn them into next moves
Internal shorthand, acronyms, and nicknames generic tools can’t parse
It is not the right fit if your team has fewer than 30 recorded calls, or if your GTM is simple enough that a single battlecard covers all deal situations.
The POC — prove it in three weeks
Three-week AMA POC. 15–30 users. One or two core use cases.
| Week | Focus | What you'll see |
|---|---|---|
| Week 1 | FDE configures your graph. Source data indexed. 5 golden prompts defined. | Your company's language in, next-move answers out. |
| Week 2 | Live usage with reps. AMA routed through real deal prep and objection handling. | Reps getting specific next moves, not document links. |
| Week 3 | Measure: prompts per user, reduction in Slack escalations, examples of better prep. | Clear before/after on where knowledge was bottlenecked. |
Success criteria: reps using it without being asked, managers using it in pipeline reviews, and at least one example where AMA gave a rep a next move that previously required a senior rep or manager.
Every tool solves retrieval. Zime AMA solves the last mile — the next move.
You can find the transcript. You can find the battlecard. You can surface the call where that objection came up.
What most tools can’t do is return the specific action a rep can take in the next 45 minutes to move the deal forward — in sales language, connected to outcomes, filtered to the rep’s context.
3.5 fatal objections per call down to 1.9. That is not a search result. That is execution.