The challenge
Most teams are drowning in repetitive work that could be automated yesterday.
Your team spends hours every week on tasks that follow the same pattern: pull data from one system, transform it, push it to another, send a notification, repeat.
You've tried Zapier. You've built a few HubSpot workflows. But the moment the inputs change or an edge case hits, things break — and the team falls back to doing it manually.
Meanwhile, AI agents that can read, reason, and act are now genuinely usable in production. Most companies haven't figured out where to deploy them or how to keep them reliable.
- Repetitive multi-step tasks — research, data enrichment, follow-up sequences — consume hours of team time every week
- Manual handoffs between tools create delays, errors, and work that falls through the cracks
- Your team is making the same decisions over and over — decisions that could be encoded as rules
- You've tried Zapier or basic automations but they break when the inputs change
- AI tools are being used ad-hoc, not systematically — results vary by person
The root cause
Most automations fail because they're built for happy paths, not real conditions
A working automation handles the 80% case. A production-grade automation handles the 20% of weird inputs, missing data, API failures, and exceptions that show up the moment it goes live.
The difference between the two is design discipline — mapping every input, defining every failure mode, and building monitoring so you find out about issues before your customers do.
AI agents raise the bar further. They can handle ambiguity, but only if you give them the right guardrails, the right context, and the right escalation paths to humans when they're out of their depth.
Automations that break under real conditions aren't automations. They're technical debt.
The solution
We design and build automations that survive contact with reality
We start by identifying the workflows where automation creates the most leverage — the multi-step, high-frequency, rule-based work that's currently eating your team's time.
Then we build on the right platform for the job: HubSpot workflows for CRM-driven logic, Make or n8n for multi-app orchestration, and custom LLM agents when reasoning is required. We build error handling and monitoring into every automation from day one.
The result is automation that runs reliably, escalates intelligently, and is documented so your team can extend it without us.
Deliverables
What's included
- Automation audit — map current manual workflows and identify agent opportunities
- Agent architecture design — define triggers, decision logic, and tool integrations
- HubSpot workflow automations — enroll, update, notify, create tasks based on CRM events
- Make / n8n multi-step flows — connect apps, transform data, and handle exceptions
- LLM-powered agents — AI that reads, researches, drafts, and decides within defined guardrails
- Error handling and monitoring — alerts when automations fail, logs for debugging
- Documentation — runbooks so your team can maintain and extend automations
- 30-day monitoring — watch live performance and tune edge cases
Our Process
How an AI Agents & Automation engagement works
- 01
Map
We document the manual workflows targeted for automation — inputs, outputs, decision points, exceptions, and the tools involved at each step.
- 02
Design
We design the agent architecture: what triggers it, what it does at each step, how it handles exceptions, and where humans stay in the loop.
- 03
Build
We build the automation on the right platform for the job — HubSpot workflows, Make, n8n, or custom LLM agents — and connect it to your live systems.
- 04
Monitor
We watch the automation run in production for 30 days, fix edge cases as they appear, and hand off with full documentation.
Outcomes
What well-built automation produces
Typical time recovered per team member when multi-step manual workflows are automated
Average response time for automated lead follow-up vs. hours with manual processes
Data stays clean — automations enrich, deduplicate, and structure CRM records on entry
Handoffs between teams become instant and traceable — no more dropped balls
Your team's time shifts from execution to judgment — the work that actually requires them
FAQ
Common questions
What platforms do you build automations on?
Primarily HubSpot workflows, Make (formerly Integromat), and n8n. For AI agents that require language model reasoning, we build on the OpenAI or Anthropic APIs and connect them to your existing tools via webhook or direct integration.
How are AI agents different from regular automations?
Standard automations follow fixed rules: if X happens, do Y. AI agents can read unstructured content, make judgment calls, draft responses, and adapt their behavior based on context. They're better for tasks that have variability — like research, content drafting, or complex qualification logic.
What if an automation breaks?
We build error handling and monitoring into every automation. You get alerts when something fails, logs that show what happened, and a runbook that documents how to fix the most common issues. For the first 30 days, we're on the hook for any failures.
Can you automate things that require our internal data?
Yes. We connect automations to your CRM, your internal databases, your document storage, and any API your tools expose. If a system doesn't have an API, we evaluate whether scraping or other data extraction methods are viable.
Will my team be able to maintain the automations after you're done?
Yes — that's a requirement, not a nice-to-have. Every automation we deliver includes documentation and, for HubSpot-based work, training so your team can modify and extend it without us.