AI agents for startups that are short on people, not ideas

The startup problem is attention

Founders do support on Monday, sales research on Tuesday and investor emails on Wednesday. None of those jobs is hard. All of them are interruptions. An agent earns its place when it takes one interruption off the calendar for good.

It is also easy to waste money here. A fast-moving product changes every fortnight, and an agent built around last month's flow breaks. So we favour small agents that read live data over large ones that memorise a process.

Which agent first, by stage

Pick the row that matches the most painful hour of your week, not the most impressive demo.

What we build and what we do not

• One agent per task, with a written scope that lists the tools it may call and the actions it may never take.
• A test set built from your past tickets, calls or CRM notes, so you see its error rate before it touches a customer.
• A review screen. In the first weeks every output waits for a click from a founder or teammate.
• A cost log: model spend per task, so you can set a ceiling that fits your runway.
• Handover documents and the code in your own repository.

We do not build agents that spend money, change production data or email investors without approval. That rule can be loosened later, with evidence, using the stages in our AI agent security checklist.

Agent, chatbot or plain workflow

Many startup tasks do not need an agent. If the steps never change, an n8n or Make workflow with one model call is cheaper and easier to explain to a future auditor or a due-diligence team. We say so when that is the right answer. Our AI agent vs chatbot page shows where each fits, and AI automation covers the workflow route.

Investors and enterprise buyers increasingly ask how you use AI with customer data. A short written description of what the agent reads, what it sends to a model provider and who approves its actions answers most of those questions. We write it for you as part of handover.

How we work with a small team

1. Pick the taskA 30-minute call to choose the single task and the way you measure success, for example minutes saved per week.
2. Two-week prototypeA working agent on test data, shown in a demo so you can still change the scope.
3. Supervised pilotIt runs on live data with a person approving each action. Weekly notes show tasks handled and edits made.
4. HandoverYou get the repository, the prompts, the test set and a one-page runbook. Maintenance is optional.

We work from Pune with a lead who stays on your project, and we book calls inside your working hours, whether that is San Francisco, London or Sydney.

What it costs a startup

Projects start at $1,500 and our rate is $30 an hour. A single-task agent on one or two tools usually sits near the low end. The price rises with the number of connected systems, how messy the source data is and how strict the approval rules are. You get a fixed quote within one working day of the call, so a build does not drift into open-ended hours.

Running costs are model usage, hosting and any tool subscriptions, listed apart from the build. Our AI agent cost guide explains what drives them, and the parent AI agent development page describes the permission levels we use.