We will build your first self-improving AI agent.It learns your work and gets better over time.
Armalo builds agentic AI that learns from your workflows, remembers what works, self-heals weak spots, and improves the task loop over time. We are picking 5 beta businesses. Implementation is free. AI usage and tokens are billed separately through Armalo if the agent is useful enough to keep running.
Apply for 1 of 5 beta slots
Name, email, and the task you want your agent to learn.
What we can build
One painful task loop, turned into a self-improving agent.
A normal automation repeats the same steps forever. An Armalo agent observes the work, runs approved actions, remembers feedback, measures outcomes, and improves the loop instead of handing you a strategy deck.
Sales follow-up
Qualify inbound leads, draft replies, update CRM notes, and flag the deals that need a human now.
Support answers
Use docs, tickets, and internal context to answer common customer questions with human review where needed.
Ops reports
Turn spreadsheets, dashboards, inboxes, or Slack handoffs into recurring summaries and escalation lists.
Account research
Research prospects, competitors, vendors, or local markets and push structured findings into your workflow.
Document processing
Extract, summarize, and route PDFs, invoices, intake forms, or messy business data.
Content operations
Draft, repurpose, QA, and route recurring content while keeping approval and brand context in the loop.
How selection works
We pick the 5 businesses where a self-improving agent can become real usage.
High-fit beta applications get routed by task type and context quality so Ryan and Rob can prioritize businesses with a concrete loop, real data access, and a reason to keep the agent running.
Implementation/setup are free for selected beta partners. Production AI usage and tokens are billed separately through Armalo, with the expected usage model agreed before anything runs.
Apply for one of the 5 self-improving agent beta slots.
Implementation and setup are free. AI usage and tokens are billed separately. In exchange, we need honest feedback, outcome measurement, and optional case study permission if the agent works.
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