AI in HubSpot, done with context

What we do with AI

Native where it fits, custom where it doesn't.

HubSpot Breeze, set up with context

Breeze Assistant, Copilot, agents and Breeze Intelligence configured on your data and your process - not switched on and left generic. The AI that ships with HubSpot, made useful.

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Agents with something to work with

Prospecting and customer agents wired to real CRM history. The honest catch: without context, an agent writes generic noise. So we migrate the context first, then let the agent run.

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Custom AI beyond the native toolset

When native AI stops, we build: LLM assistants, custom and multi-agent systems, context engineering, and integrations to your own models. Real engineering, scoped to a concrete use case.

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AI woven into how we deliver

We run our own AI content and research stack, so we know first-hand where AI helps and where it is theatre. You get a straight read on what to automate now and what to leave alone.

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How we run an AI engagement

Use case to Scope to Build to Stay.

Use case

What is worth automating

We look at your process and your data, and pick the use cases where AI actually moves a number - not the ones that demo well. Data readiness is part of the honest answer.

Scope

Fixed-bid quote

Concrete scope, deliverables, timeline, fixed price. We carry the execution risk - the price in the doc is the price.

Build

Configure or engineer

Native Breeze configuration where it fits, custom assistants, agents and context engineering where it does not. Weekly checkpoints, full Slack access.

Stay

Enablement + 30 days

A working session for the team, admin handover, and 30 days of post-launch support. Optional retainer for ongoing tuning and monitoring.

Sample deliverables

Examples of what an AI engagement includes.

Readiness

Honest starting point

- AI use-case prioritisation
- Data readiness assessment
- Tooling + platform review
- Governance + risk check
- Business-case framing
- Roadmap you can act on

Breeze

Native AI, configured

- Breeze Assistant + Copilot setup
- Prospecting Agent configuration
- Breeze Intelligence enrichment
- Customer Agent for service
- AI in workflows
- Guardrails + review steps

Custom

Built for your case

- Custom LLM assistants
- Custom + multi-agent systems
- Context engineering + knowledge base
- Retrieval on your data
- Integration to your own models
- Evaluation + monitoring

Content

AI in marketing

- AI content production support
- Brand-voice enforcement
- Research + drafting agents
- AEO answer-ready content
- Personalisation with AI
- Human review in the loop

Ops

Kept in check

- AI performance monitoring
- Governance + compliance
- Adoption + enablement
- Prompt + workflow maintenance
- Cost + usage tracking
- Experimentation loop

Enablement

Team can run it

- Role-based AI training
- SOPs + prompt libraries
- Where-to-trust guidance
- Review + escalation rules
- Admin handover
- 30 days of support

Common questions

What buyers ask about AI.

Is this just HubSpot Breeze, or custom AI too?

Both. We configure native Breeze properly - assistant, agents, Intelligence - and when the native toolset runs out we build custom: LLM assistants, agents, context engineering, integrations to your own models. We start with whichever gets the result for the lower cost.

Does an AI agent actually improve results?

Only when it has context. Without real history in the CRM, an agent generates generic output with no signal from the contact or account. So we sequence it: get the data and context in first, then the agent has something to work with. We will not sell you an agent that writes into a vacuum.

Can you build an assistant on our own data and models?

Yes - that is the custom AI line. Retrieval on your knowledge base, context engineering, custom and multi-agent systems, and integration to your own or third-party models. Scoped to a concrete use case with evaluation and monitoring, not a science project.

Isn't a lot of AI just hype?

A lot of it, yes. We tell you straight where it earns its keep today - drafting, research, enrichment, routing, summarisation - and where it does not yet. Honesty is cheaper than a tool nobody trusts after a month.

What does an AI engagement look like?

Use-case prioritisation and a data-readiness read first, then a fixed-scope build, enablement and 30 days of support. Optional retainer for tuning and monitoring. Fixed-bid, our risk on execution.

AI modules