AI agent
AI agents
Bespoke agents built for your exact workflow. We scope, build, and deploy.
the problem
Off-the-shelf AI tools don't fit your business. You need something built specifically for how your team works, connected to your tools, handling your exact processes.
The gap between a generic chatbot and an agent that runs part of your business is huge. Generic tools follow rules. Agents make decisions. When your workflow has nuance, conditions, and judgement calls, you need something that can reason, not just respond. That doesn't exist off the shelf for your specific business.
Building it yourself is a rabbit hole. Learning the APIs, the orchestration, the prompt engineering, the testing, the deployment. By the time you've figured it out, you've spent three months and built a worse version of what someone could have built for you in two weeks.
The other trap is the demo that never ships. Plenty of teams have seen an impressive agent prototype that fell over the first time it met real data. The distance between a demo and a production agent is testing, error handling, and monitoring. That last 20% is where most DIY builds die.
what we build
We build custom AI agents scoped to your workflow. They connect to your existing tools, follow your business rules, and handle the work your team shouldn't be doing.
We scope tightly. One agent, one job, one measurable outcome. We don't build science projects. Every agent we deploy has a clear definition of done and a way to measure whether it's working. You know what it does, what it costs, and what it saves.
Agents we've built handle things like: qualifying inbound leads end to end, monitoring competitor pricing weekly, drafting and sending invoice follow-ups, building monthly client reports from scratch, triaging support tickets across multiple channels. If it's repetitive, conditional, and rule-bound but currently done by a human, an agent can probably do it.
Every agent ships with a human approval point wherever it touches the outside world. Drafts queue for sign-off before anything sends. Actions log to a dashboard your team can read. The agent earns autonomy gradually, as the logs prove it makes the same call your best person would.
the stack we typically use
- Claude
- Claude Code
- n8n
- HubSpot
- Notion
- Slack
- Gmail
what changes
- A working AI agent live in 2-4 weeks
- Connected to your existing tools and data sources
- Measurable outcomes (time saved, leads qualified, errors caught)
- Monitoring dashboard showing what the agent did and what it cost
- A partner who maintains and improves the agent over time
who this is for
Businesses with complex workflows that don't fit a simple template. Teams that have tried automation tools (Zapier, Make) and hit the wall of what rules-based logic can do. Founders who want a custom solution but not a custom development project.
how it works
Three steps
01
We scope the agent around your specific workflow and business rules
02
We build, test, and connect it to your existing tools
03
We deploy it into your workflow and iterate based on real results
common questions
Questions we get about ai agents
How long does it take to build an agent?
A tightly scoped agent goes live in two to four weeks. The scoping session sets one job and one measurable outcome, then we build, test against your real data, and deploy with monitoring. Agents that touch more systems take longer, which is exactly why we scope one job at a time.
How is an agent priced?
Per agent, fixed after scoping, so you know the number before we write a line. The scope doc states what the agent does, what it connects to, and how success is measured. Ongoing maintenance and improvement is a separate, smaller monthly figure, and you can drop it once the agent is stable.
Isn't this just a ChatGPT wrapper?
No. ChatGPT answers questions when you ask them. An agent works without being asked: it watches your inbox or CRM, makes decisions against your business rules, takes actions in your tools, and logs everything it did. The model is one component. The connections, guardrails, and monitoring around it are the build.
What stops an agent doing something it shouldn't?
Guardrails and approval gates. Anything outbound, an email, an invoice chase, a CRM change, queues for human sign-off until the logs prove the agent gets it right. Its permissions are scoped to the minimum it needs, every action is logged, and you can pause it with one switch.
Who owns the agent and its data?
You do. The agent runs on your accounts and your API keys, its code and prompts are documented and handed over, and the data it touches never leaves your systems. If we part ways, the agent keeps running and any competent developer can maintain it from the documentation.
Ready to get started?
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