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 MEDDIC Qualification With AI Agents

MEDDIC is the best sales qualification framework most teams have. It’s also the one with the worst data in the CRM. Not because reps don’t understand it - because filling in six structured fields after every call is 15 minutes of admin that competes with sending the follow-up email, prepping for the next call, and everything else that feels more urgent.

The fix isn’t training. It’s not enforcement. It’s an agent for each MEDDIC field that reads the call transcript and populates the data automatically. The rep reviews in 30 seconds instead of writing for 15 minutes. The data goes from 20% complete to 90% complete overnight.

Why is MEDDIC data always incomplete?

MEDDIC - Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, Champion - requires reps to translate an unstructured 45-minute conversation into six structured fields. After every call. For every deal.

The information exists. The prospect said their current process costs them $400K per quarter - that’s Metrics. They mentioned their VP of Finance needs to approve anything over $50K - that’s Economic Buyer. They said they need SOC 2 compliance and Salesforce integration - that’s Decision Criteria.

All of it was said on the call. All of it is in the transcript. None of it ends up in the CRM because nobody has time to go back through a transcript, find the relevant moments, extract the data points, and type them into HubSpot fields.

So the fields stay empty. And every downstream system that depends on MEDDIC data - deal scoring, forecast accuracy, pipeline reviews, coaching - operates on incomplete information.

The problem was never the framework. It was the data entry.

How does an agent swarm handle MEDDIC extraction?

Instead of one agent trying to extract everything, you build a specialized agent for each MEDDIC field. Each agent reads the same transcript but looks for different signals.

Metrics agent. Scans every transcript for quantified business impact. Dollar amounts, percentages, time lost, headcount affected, revenue at risk. “We’re losing about $400K a quarter to manual processing” - extracted, structured, written to the Metrics field in HubSpot. If no metrics were mentioned, the agent flags the gap so the rep knows to ask on the next call.

Economic Buyer agent. Listens for power signals. Who approves the budget? Whose name comes up when the rep asks “who else needs to sign off?” Who does the prospect defer to when discussing pricing or timeline? The agent maps these signals to contact records and flags whether the economic buyer has been directly engaged.

Decision Criteria agent. Extracts the specific requirements the prospect named - not the rep’s interpretation, the actual words. “We need it to integrate with Salesforce.” “Has to be SOC 2 compliant.” “Needs to handle 10,000 contacts minimum.” Each criterion captured verbatim and written to the field.

Decision Process agent. Looks for process signals. How many stages in their evaluation? Who’s involved at each stage? What’s the timeline? Is there a procurement review? A security audit? A legal review? The agent maps the buying process as the prospect describes it across multiple calls.

Identify Pain agent. Extracts the stated pain points - not features the prospect liked, but problems they described. “Our reps spend 4 hours a week on data entry.” “We have no visibility into pipeline health.” “We lost three deals last quarter because nobody noticed they went dark.” Pain in the prospect’s own words.

Champion agent. Identifies which contact is actively selling internally on your behalf. Who’s scheduling meetings with other stakeholders? Who’s sharing materials? Who’s pushing for a faster timeline? Champion identification is harder to automate than the other fields - the agent flags likely champions based on engagement patterns rather than making a definitive call.

Each agent runs in parallel the moment a transcript is available. By the time the rep opens HubSpot after the call, the MEDDIC fields are populated with draft data. They review, correct if needed, and move on. Thirty seconds instead of fifteen minutes.

What changes when MEDDIC data is actually complete?

Deal scoring becomes real. A deal risk model that checks MEDDIC completeness can distinguish between “this deal is early” and “this deal is missing critical qualification data at a late stage.” When the fields are actually populated, the model can assess deal health based on what’s known - not just what stage it’s in.

Pipeline reviews get specific. Instead of “where are we on Acme?” the manager can say “the Economic Buyer hasn’t been engaged and we’re in Proposal stage - that’s the risk.” MEDDIC data turns vague pipeline reviews into focused strategy conversations.

Forecasting improves. Deals with complete MEDDIC data close at higher rates and with more predictable timelines. When your MEDDIC fields are populated across the pipeline, your AI sales forecast stops being a guess and starts being a model.

Coaching has something to work with. A manager can look at a rep’s MEDDIC data across their deals and see patterns. This rep consistently misses Metrics. This one never identifies the Economic Buyer early enough. That’s coachable. Empty fields aren’t coachable.

What if your team barely uses MEDDIC fields today?

That’s most teams. The fields exist in HubSpot. They were set up with good intentions. They’re 15-25% populated across the pipeline, and the data that is there is often vague or outdated.

Don’t start with a mandate to fill them in. Start with the agent swarm. Connect your call transcription tool (Gong, Fathom, Fireflies) to an extraction pipeline. Let the agents populate the fields from transcripts for two weeks without telling anyone. Then show your team the data.

When a rep sees their MEDDIC fields filled in accurately - with the prospect’s actual words, from the actual call - the conversation shifts. They’re not being asked to do data entry. They’re being asked to review data that’s already there.

Adoption follows accuracy. Build the agent, prove the data quality, and the team will trust it.

MEDDIC extraction is one part of a broader shift toward autonomous GTM agents that execute without being asked. Complete MEDDIC data also powers the pre-call brief agent and feeds into AI deal risk detection that catches dying deals early.

How to build the extraction pipeline

The technical stack for MEDDIC extraction is simpler than it sounds.

Transcript source: Gong, Fathom, or Fireflies. All three have APIs that return transcript text when a call completes. Gong has the most mature API and the best search capabilities across your full call library. Fathom is the best option if you’re starting fresh on a tight budget.

Trigger: A webhook from your transcription tool fires when a new transcript is available. This is usually a “call completed” or “transcript ready” event. n8n handles this natively - you configure the webhook endpoint and n8n receives the event automatically.

Extraction step: n8n passes the transcript text to an AI model via HTTP request. The prompt is specific per agent: the Metrics agent prompt instructs the model to “read this call transcript and extract every quantified business impact the prospect mentioned - dollars, percentages, time, or headcount. Return a structured list with the exact quote, what it measures, and the magnitude.” Each MEDDIC field has its own prompt, its own extraction logic, and its own output format.

Write-back step: The extraction output (structured JSON) gets mapped to HubSpot custom properties via the HubSpot API. One API call per MEDDIC field, updating only the fields where new information was found in this transcript.

Notification step: A Slack DM to the rep with a summary of what was extracted: “Processed call with Acme Corp. Extracted: Metrics (updated), Decision Criteria (2 new criteria added), Economic Buyer (identified Sarah Chen as budget approver). Review and confirm.”

Build time for a six-agent MEDDIC extraction pipeline: 3-4 days.

How to handle calls where nothing useful is extracted

Not every call yields MEDDIC data. A 10-minute check-in call won’t have budget discussion or decision process clarity. Handle this gracefully.

Configure each agent to return an explicit “nothing found” signal rather than leaving the field blank or returning a generic response. The Metrics agent should say “No quantified metrics mentioned in this call” rather than outputting nothing. That signal tells you the call happened but the data point wasn’t raised - which is different from the call not being processed at all.

For short calls (under 10 minutes), consider filtering at the trigger level. If the transcript is under 1,000 words, skip the extraction pipeline and log that the call was processed with no MEDDIC content. Saves API calls and keeps your MEDDIC fields clean.

For calls where extraction confidence is low - the transcript has poor audio quality, overlapping speakers, or highly technical tangents - add a confidence threshold. If the model isn’t confident in an extraction, flag it for rep review rather than writing it automatically to the CRM. A wrongly extracted Metric is worse than no Metric.

Your sales methodology is only as good as the data behind it. MEDDIC agents make the data real - not by changing rep behavior, but by removing reps from the data capture loop entirely.