AI agents that automate real work across marketing, sales, support, and operations. Built for teams that need automation which survives contact with production data, with deep CRM-native experience as our specialty.
Three failure patterns account for most of it, and all three are operational rather than technical.
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Agentic AI splits into three layers with very different running costs. Knowing which one your problem needs is the difference between a $7,500 project and a $20,000 one.
The conversational layer that drafts content, summarizes records, and answers questions from your existing systems. Free on many platforms and consumes no credits.
Ready-made agents that handle multi-step work on their own, resolving support conversations or sourcing prospects. Runs in the platform's agent console.
When no pre-built agent fits your process, we design your own around your prompts, your knowledge, and your data.
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Campaign content and asset generation
Lead nurturing sequences
Segmentation and list logic
Prospect research and account summaries
Outreach drafting and follow-up
Deal prioritization and forecasting inputs
Customer agent resolving inbound conversations
Ticket summaries and suggested replies
Routing and escalation logic
Duplicate detection and record standardization
Cross-object workflow automation
Data sync monitoring between systems
Handing an agent access to your CRM means handing it access to your customer data. We treat that access the way we'd want it treated if it were ours.
Every engagement opens with a signed NDA, and everything we build, workflows, prompts, code, documentation, transfers to you as IP when we deliver. Nothing stays locked to us after the project ends.
Only the team members assigned to your project get into your systems, and only with the permissions that project requires. Access is granted per engagement and revoked when it closes, not left open by default.
Any client data our agents touch, records, credentials, conversation logs, moves over encrypted connections and sits encrypted in storage. This holds regardless of which model or platform a project runs on.
Separate from the cost caps under Guardrails above, every action an agent takes, what it reads, what it writes, what it sends, is logged and reviewable. If something goes wrong, you can see exactly what happened and when.
AI Inside Your CRM
|
AI Bolted On From Outside | |
|---|---|---|
| What the agent reads | The live record | A synced copy, as current as the last sync |
| What it can change | Updates deals, tickets, and contacts directly | Read-only, or writes back through an integration |
| Reporting | Activity logs on the record timeline | Sits outside your attribution model |
| Permissions | Inherits the roles you already maintain | A second set of users to provision and audit |
| Cost | Credits on your existing platform bill | A separate subscription per tool |
| Adding a system later | The agent already sees the new data | Another integration to build and maintain |
| When something goes wrong | One audit trail, one place to look | Logs split across systems |
Our CRM-native work is built on HubSpot, where agent tooling requires Professional or Enterprise tiers and assistant features are included on every tier including the free CRM.
Data structure and quality reviewed against agent requirements
Existing workflows and integrations mapped
A realistic picture of what agents can act on today
Use cases prioritized by business impact against build complexity
Success defined in numbers before anything is built
A build plan agreed before work starts
Agents configured in platform tooling, or built custom
Knowledge vault assembled from your approved content
Grounded in your actual data, not a generic model
Systems integrated for the agent to read from or write to
ERP, data warehouse, or internal application connections built
API integrations tested before go-live
Teams trained to work alongside the agent
Escalation rules documented
Permissions set to govern what the agent can change
Performance reviewed against the baseline
Prompts and routing logic refined
Scope expanded once the first use case holds up
Most agentic projects fail on data quality rather than model quality, which is why we will not quote a build without looking at your systems first. Two to three weeks, fixed fee, credited back against whatever you build next.
Two things sink agentic projects after launch: an agent that says something it should not, and a bill nobody forecast. Both are preventable at build time.
Approval steps sit in front of anything customer-facing. A support agent answers only from your knowledge base and cannot take custom instructions, so a thin knowledge base produces confident wrong answers rather than no answer.
Agents operate inside the access model you already maintain. Platform admins have to enable AI access before an agent can reach customer data, and agent-building permission is granted deliberately.
Credits do not roll over month to month, and platforms can auto-upgrade your tier if you exceed the limit. We set caps and alerts before an agent handles its first conversation.
Every organization adopts AI for different reasons. The following examples show how agentic AI can be applied to real business challenges.
HubXpert has helped teams put agents into production that actually hold up under real volume, not just a demo.
Across support, sales, and internal operations, each one built for a specific workflow rather than a generic chatbot wrapper.
Measured across live deployments handling real customer and internal traffic, not test environments.
Tickets and inquiries resolved by the agent without a human handoff, measured against pre-deployment volume for the same team.
Start With the
Readiness Audit
Two to three weeks, $2,500, credited back against whatever you build next. You get a clear answer on whether your data can support agents today, which use cases are worth the spend, and what it will cost to run them.
Takes 30 minutes
Delivered by a certified AI specialist
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