Service 05
AI Agents for GTM
We build AI agents that do specific, measurable GTM work and run in production. Call transcripts become structured CRM records. Assignments get proposed in Slack for one-click human approval. Each agent solves one named problem, and a person confirms anything that touches your data.
Most AI in go-to-market is a demo that never survives contact with real data. We only build agents where the task is narrow enough to verify and valuable enough to be worth verifying, which rules out most of what gets pitched. The design constraint we hold to is human-in-the-loop: the agent drafts, proposes, and structures, and a person approves before anything is written or sent. That is what makes the output trustworthy enough to leave running.
What does a production GTM agent actually look like?
One we built assigns customer success managers to new campaigns. A fundraising platform runs hundreds of them, and each needs the right CSM quickly. The agent reads the campaign context, picks the CSM, and posts the recommendation to Slack. A human confirms with one click, and only then does anything reach the CRM.
The Slack step is the whole design. It makes the agent useful without making it unsupervised, and it means a wrong recommendation costs one click rather than a data cleanup.
How do call transcripts become CRM data?
Through a pipeline rather than a plugin. The recording lands, a webhook picks it up, Claude analyzes the transcript for a summary and the specific fields that matter, and the structured result goes to HubSpot. Meeting types are standardized first, so a discovery call and a renewal conversation get the analysis each one needs.
The output is manager summaries, call quality review, and qualification signals that used to depend on whether a rep remembered to write notes.
- Transcript analysis for summaries and call quality review
- Qualification signals extracted against your criteria, not generic ones
- Competitor mentions surfaced from real conversations
- CRM fields populated from what was said rather than from recall
Why insist on a human approval step?
Because an unreviewed AI write into your CRM is a data quality problem that compounds silently. By the time anyone notices, the bad records are already feeding scoring, routing, and reporting.
Approval in Slack costs a second and makes the whole system safe to leave running. No AI message leaves the building unreviewed.
Do you actually run on this yourselves?
Yes, which is the honest test of whether we believe it. Avero runs internal agents for its own operations, including one built from 890 completed tasks that turns meeting notes into assigned work across 19 recurring task types.
We would rather show you something running in production than talk about what is possible. If a workflow does not have a measurable problem attached, we will tell you it is not worth building yet.
The stack we build this on
References
Client work behind this service
Projects that make this concrete
Common questions
Is this real or is it a pilot?
Real and running. We can show you several agents in production right now, including the CSM assignment agent and the transcript analysis pipeline. We agree that most AI talk is hype, which is exactly why we only build against specific measurable problems.
Which models do you build on?
Mostly Claude, with ChatGPT and Gemini where they fit better. The model matters less than the surrounding system: the trigger, the structured output, the approval step, and where the result is written.
Do we need our CRM in order first?
Broadly yes. An agent writing into a broken data model produces confident nonsense faster than a human would. If the foundation is not there we will usually recommend a HubSpot audit before building agents on top.
Want this built for your team?
You will talk to the person who architects it, not an SDR.