How we BootStrapped a Profitable Agentic AI Startup in Security Space in 6 Months.

Bootstrapping is a startup is hard, security space is crowded, LLM agents are finicky, AI is expensive.

Yet, its been relatively easy for us at Transilience AI to get paying customers, delightful users which keeping our costs minimal. To put in perspective, we have 1 frontend engineer, Muzaffar Hossain , one backend engineer, Alessio Mauro , one designer, Garima Sadhnani who worked on the product full time starting in June.

Our AI stack costs are minimal, enough to be covered by our revenues.

If we can do it, anyone can do it. No need to raise millions of seed money. Some lessons learnt so fellow entrepreneurs can jump in into starting their own companies

Hone in on the use case and know your architecture.

We can boil the ocean with LLMs, but you should not try to LLMs on all use cases. As you are talking to customers, see if the pain point and use case that customer is expressing is only solved by LLMs.

For example - tell me if this vendor has a workaround for a given CVE in their advisory. It cannot be solved programmatically with out LLMs.

Know your architecture leverage.

There are core capabilities of LLM and agentic AI architecture, if you get them right, you can solve several use cases using the same architecture components.

For example - structured output extraction from several different formats of information. If you get that component right, you can parse out threat intel advisories from CISA or exploit code from metasploit. RAG , if you get RAG right, you can solve compliance use cases or vendor documentation parse out use cases.

Pick the right team

AI engineering expertise can be expensive, but can be worked around.

For example - You only need one LLM expert (who is the most expensive). The rest can be done by good python and react engineers. As the team adjusts to LLMs , smart engineers would develop the taste for LLMs. Our front end team joined with out much LLM expertise but now they are well versed with AI UX patterns.

Pick the right stack and right configuration

AI stack can be expensive but can be worked around provided you know the exact use case you are trying to solve.

For example - Time and again we were able to solve the use case with just the lowest priced model , say 4o-mini. Yes it required a lot of prompt engineering, but thats less expensive. Same with RAG, probably a different post on tech stack.

Use AI for All of your Business

We use AI not just for product but for dev (for example we use cursor heavily and all of us are pro chatgpt, claude and gemini users), we use them heavily to draft documentation or customer emails.

So entrepreneurs sitting on the side lines, this is the best opportunity to build a company that can take on established companies and legacy workflows, with out raising a lot of money and have fun building it.