Agentic AI for GTM
Give AI a useful job inside your GTM engine
AI works best when it has a defined task, trusted inputs, evidence to inspect, and a clear boundary on what it may change. We help B2B teams add AI-assisted research and decision support to Clay, HubSpot, and their workflows so people spend less time collecting context and more time making sound commercial decisions.
Start with one repeatable task and a human approval point. Expand only when the output proves useful.
The business problem
Autonomy needs an operating model
A prompt that writes a company summary is easy to demo. A production workflow must also know which account it is describing, where the facts came from, whether the output is complete, when to stop, and what to do if the source is wrong or unavailable.
We design bounded AI steps around the underlying GTM process. Clay can support research and structured enrichment; HubSpot can hold the record and the next team action. Depending on the use case, a person reviews the result before a CRM update, campaign brief, or customer-facing message moves forward.
What we can build
Services for a dependable GTM operation
Account and market research
Extract specific facts from approved sources, capture evidence, and give sellers a concise brief that can be checked.
ICP fit and buying-role classification
Apply explicit criteria to company and person data; return a reason and route uncertain records for review.
CRM data quality assistance
Flag conflicting fields, missing context, or likely duplicates without letting an AI overwrite trusted values by default.
Signal interpretation
Summarize why a monitored company event may matter, distinguish evidence from inference, and suggest a next research step.
Campaign and outreach preparation
Draft research-backed angles or briefs for a segment while keeping claim checks, consent, and final approval with the team.
Evaluation and governance
Build a small test set, define acceptable errors, log outputs, set review thresholds, and monitor performance and usage cost.
Our approach
Build AI research that a seller can verify
We start with a narrow decision such as whether an account fits the ideal customer profile. The AI step reads approved sources, extracts relevant facts with source links, applies agreed criteria, and produces a short research brief. Low-confidence or conflicting findings are flagged for review; a person decides whether to update HubSpot or act on the account.
The deliverable includes the structured prompt and output schema, source rules, a test set covering clear and ambiguous records, approval thresholds, and an operating log. We evaluate factual corrections, review acceptance, time spent on useful briefs, and cost per accepted output before expanding the workflow.
How we work
Start focused, test, then expand
Pick one decision
Choose a task with repeatable inputs and an outcome the team can judge.
Define evidence and boundaries
Specify allowed sources, structured fields, prohibited actions, and human review.
Evaluate on test cases
Test known good, bad, ambiguous, and missing-data cases before deployment.
Operate and improve
Track errors, corrections, cost, and whether the step helps the actual workflow.
Measures worth watching
factual correction rate
time saved on qualified tasks
cost per useful output
Common questions
What to know before you start
Do we need fully autonomous AI agents?
Usually not at the start. A bounded research or classification step with clear review often creates a better foundation than a broad agent with unclear permissions.
Can AI update HubSpot automatically?
It can be connected to downstream actions where the data, permissions, and safeguards support that choice. We define which fields may be suggested, reviewed, or written automatically.
How do you prevent invented facts?
We require source evidence for factual claims, test ambiguous cases, and keep human approval before consequential CRM or customer-facing actions.
Let’s make the next step concrete
Tell us where your GTM process slows down
Share the tools you use, the handoff or data issue you want to solve, and what a useful outcome would look like. We can scope a focused consulting or development starting point.