Most AI projects stop at the first one: cheaper emails, faster notes, quicker research. That is worth having, but your competitors get it too. We build agents and automation on HubSpot where they save real hours, look for the places where AI lets you sell, serve or price differently, and train your people to use it with judgement.
Every candidate use case gets one question first: does it make today's work cheaper, or does it let you do something new? Both are valid. They are scoped, measured and priced differently, and the second one does not happen by accident.
Before an agent qualifies a lead or resolves a ticket, we write down what qualified and resolved mean, and make HubSpot hold that definition. Otherwise AI just automates the ambiguity, faster.
HubSpot's own agents where they fit, custom agents where they do not: research, enrichment, meeting-transcript extraction and qualification, reading and writing HubSpot properties, with approval steps where money or customers are involved.
What data AI may touch, what it may write, what must be reviewed, and how every action is logged, so you can answer who approved what. Written in plain language and enforced in the tooling.
Role-based, hands-on sessions for sales, marketing, customer success and RevOps on your own HubSpot data: prompting, Breeze features, reusable skills and when not to trust the output.
We plan adoption with ADKAR, one role at a time, and measure what happened to the hours AI freed up, not just whether they were freed. Small pilots with stop criteria agreed up front, reviewed monthly.
An AI agent is treated like a new hire: a written job, a supervisor, a probation period and a review. Every action lands on a HubSpot record, so you can always answer the question most companies cannot: show me everything AI did last quarter, and who approved it.
Our agent operating model is informed by the B.R.A.I.N.S.™ framework by Mind & Metrics.
One process at a time: what starts it, what it reads, what done looks like, and which parts stay human. If the field that triggers it is not reliably filled in, we fix the data before we build.
Where it runs, what it may access, what it must never touch, and where it asks a person when it is stuck. Approval steps are built in from the first run.
Every run is logged with what the agent did, what it cost and who approved it, measured against the hours the work took before.
A standing review of results, corrections and cost. Every human fix becomes a change to the agent's instructions, and the stop criteria are checked every time.
Adoption planned with ADKAR for the people who review and receive the agent's work, with a sponsor who says this is how we work now.
The next process only after the first one shows its numbers. Governance and team capacity, not technology, usually set the pace.
Engagement shape: Two-week use-case sprint, then build and training phases of two to six weeks. Fixed fee per phase or part of a retainer.
Forecasting ran in a BI tool fed by NetSuite and HubSpot, but HubSpot company records were too dirty to roll up: nameless companies, full country names in a two-letter code field on 13,344 contacts, contacts unattached to their company.
Company-level reporting and deal roll-ups work again. Every write was verified live after the fact, and the client received review sheets for the records we deliberately did not touch.
Mostly Claude for reasoning and agents, HubSpot's own Breeze features where they fit, and n8n for orchestration. We recommend based on your stack and data location, and we say when a tool is not ready.
We define what data AI may access before anything is connected, use enterprise or API tiers with no training on your data, and log every write back to HubSpot. Your data is only ever worked on locally: it stays in your HubSpot portal or on the consultant's machine during processing, and the only external party that sees it is the AI model provider through the API call itself. Nothing is copied to third-party storage. Governance is a deliverable, not a footnote.
Yes. Training works best on your real HubSpot data and real tasks, so we prepare the sessions around your workflows, not generic prompting.
Only once the data they read is ready. An agent that scores leads against your ideal customer profile needs that profile written down and your company data filled in. We check readiness per agent before you pay for the credits. As a HubSpot partner we are rewarded when agents are activated, which is exactly why we will tell you when one should wait.
Book a 30-minute discovery call. We look at your portal together and tell you what we would fix first.
Discovery calls are free and go straight into the calendar.
