What AI tools do you use?

We build on top of the tools you already have - HubSpot, Salesforce, n8n, Make - and layer in AI from OpenAI, Anthropic, and others where it makes sense. No proprietary platform, no new vendor to manage.

Is this actual AI or just rule-based automation?

Both, depending on the job. Simple routing and alerts are rule-based - that's the right tool for that. Scoring, summarization, intent detection, and research use real LLMs. We match the approach to the problem.

Do I need to replace my CRM?

No. We build on top of what you have. Whether that's HubSpot, Salesforce, or something else, we connect to it - we don't replace it.

Do I need a technical team to manage this?

No. The automations run on their own. We build it, hand it off, and your team uses the outputs - alerts, scores, summaries - without touching the infrastructure.

How do you handle our data?

Your data stays in your stack. We don't store it, sell it, or route it through proprietary systems. The pipelines we build connect your existing tools directly.

What does onboarding actually look like?

A 30-minute discovery call, then we scope the work and send a proposal. Once you're in, we go heads-down - no lengthy kickoffs or status meetings. You see working software fast.

What if the AI gets it wrong?

It will sometimes, especially early. That's why we build human review steps where it matters and tune based on your data. Accuracy improves over time - it's not set-and-forget.

How is this different from HubSpot's or Salesforce's native AI?

Native AI works within the walls of that one tool. We connect your entire stack and build logic that runs across it - enrichment, scoring, alerts, and summaries that pull from multiple sources and trigger actions anywhere.

How to Automate Sales QBRs Using Call Transcripts and AI

Most sales QBR prep is the same work every quarter: pull up the accounts, dig through call recordings you half-remember, try to reconstruct what the champion said about budget three months ago, and build a deck that answers questions you’re guessing the executive will ask. It takes hours. Most of it is wrong by the time you’re in the room.

The fix is not a new QBR tool. It’s wiring your sales call transcripts - discovery calls, check-ins, renewal conversations - into a workflow that does the prep for you. With AI, specifically Claude, you can automate QBR prep so that every account brief is generated from the actual words your customers and prospects said, not from what a rep vaguely remembers.

This is how to build that workflow.

Why QBR prep fails without transcript data

The standard QBR prep process relies on two sources: CRM fields that reps filled in when they had time, and memory. Neither is reliable.

CRM data is structured but shallow. You know the deal stage, the close date, the ARR. You don’t know that the VP of Sales mentioned during the discovery call that their current tool is getting cut in Q3 because it failed a security audit. That detail lives in a recording somewhere, if it was recorded at all.

Sales discovery calls are where the real intelligence lives - pain points, political dynamics, buying timeline, what the economic buyer actually cares about. But that intelligence is almost never systematically captured. It sits in a Gong recording that nobody re-watches, or in a rep’s notes that are half a sentence and a placeholder.

When QBR season hits, you’re rebuilding context from scratch. Automating sales QBRs means automating that context rebuilding - and the only way to do it reliably is from transcripts.

Step one - get your transcripts into a usable format

If you use Gong, Chorus, Fathom, or any other call recording tool, you already have transcripts. The problem is they’re siloed in a separate system and nobody has connected them to QBR prep.

For each account you’re reviewing, pull the transcripts from the last 2-3 most recent calls: the original discovery call if it’s available, any check-in calls, and the most recent conversation. Export them as text. Most tools let you do this natively, or you can use their API to pull transcripts automatically on a schedule.

If you don’t have a call recording tool, this is the forcing function to get one. Even basic transcription from Zoom or Google Meet works for this workflow.

Step two - extract deal intelligence from discovery call transcripts with Claude

Paste the transcripts into Claude with a structured extraction prompt. The goal is to pull out the specific intelligence that makes a QBR useful - not a summary, but structured data about the account.

A prompt that works:

“You are analyzing sales call transcripts for account [Name]. Extract the following and format as structured output: stated pain points with direct quotes, current tools and process they’re replacing, budget signals and any dollar amounts mentioned, buying timeline and any hard deadlines, identified stakeholders and their roles, political dynamics or blockers mentioned, success metrics - what good looks like to them, and any risk signals including objections that weren’t resolved.”

Run this against each account’s transcript set and you get a structured brief for every account, built from what was actually said, not reconstructed from memory. This is the core of automating QBR prep from sales call transcripts - the brief writes itself from the data that already exists.

Step three - score MEDDIC automatically from transcript data

If your team runs MEDDIC or MEDDICC, you can use the same transcript data to populate qualification scores automatically. This is one of the most requested pieces of QBR automation because MEDDIC scoring is typically either skipped entirely or done inconsistently by reps.

Add a second prompt pass after extraction:

“Using the extracted deal intelligence above, score this deal against MEDDIC criteria. For each letter - Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, Champion - note whether it is confirmed, partially confirmed, missing, or contradicted by the transcript data. Flag any MEDDIC gaps as risks for the QBR.”

You get a MEDDIC scorecard per account, grounded in transcript evidence. Not a rep’s self-assessment. Actual data from the discovery call and subsequent conversations.

This changes the QBR conversation from “how confident are you in this deal” to “here are the three MEDDIC gaps we found in the transcript from February - what’s changed since then.”

Step four - generate the QBR brief

With extraction and MEDDIC scoring complete, generating the actual QBR account brief is one more prompt:

“Using the structured deal intelligence and MEDDIC scores above, write a QBR brief for account [Name]. Include: account context and where they are in the buying process, key stakeholders and their positions, deal health summary based on MEDDIC gaps, top 3 risks and what signals they came from, recommended talking points for the executive conversation, and suggested next steps.”

The output is a structured brief per account that a RevOps leader or AE can review in five minutes before the QBR, correct if anything is stale, and bring into the meeting. The prep that used to take 45 minutes per account takes 5.

Automating the full workflow

Running this manually account by account is better than the status quo. But the real version of this is automated.

A scheduled workflow - running the night before each QBR cycle - pulls transcripts via API from Gong or Chorus, sends each account’s transcripts through Claude with the extraction, MEDDIC scoring, and brief generation prompts, and writes the output to a shared doc or Notion page. By the time the QBR prep call starts, every account brief is already there.

The tools to build this are n8n or Make for orchestration, Gong or Chorus API for transcript retrieval, Claude API for the analysis, and Notion or Google Docs for output. None of this requires engineering support if you have a RevOps person who can set up a workflow tool.

The total build time for a team of 20-30 accounts is a few hours. The time savings per QBR cycle is measured in days.

What this doesn’t solve

Automated QBR prep from call transcripts only works if calls are being recorded and transcribed consistently. If your reps are taking calls off the record, running demos on personal Zoom accounts, or just not using the call recording tool, the transcripts won’t exist.

Before building this workflow, audit your call recording coverage for the last quarter. If you’re missing transcripts for more than 20% of discovery calls, close the coverage gap first. A QBR automation workflow built on incomplete transcript data will produce incomplete briefs - and incomplete is sometimes worse than nothing because it creates false confidence.

The discipline of recording and transcribing every discovery call is what makes this workflow compounding. Each quarter you run it, the transcript library gets deeper, the briefs get better, and the QBR conversations get sharper.

The difference between a QBR that goes well and one that doesn’t is usually preparation. Automating sales QBRs from call transcripts doesn’t make the conversation easier - it makes the preparation stop being the bottleneck.