Build AI around the systems your company already trusts.
The hierarchy of needs behind our name shapes how we work. We begin with your data, workflows, risk limits, and infrastructure. Once that base is sound, an AI employee has something dependable to work from.
Reduce the cost of AI adoption.
Enterprise AI projects often bundle strategy, software, and infrastructure into one opaque price. We separate those decisions. Open models can reduce licensing costs, local hardware can lower high-volume inference costs, and a well-built harness can outlast any single model. Your proposal shows the scope and price of each.
Four commitments, in writing
We run Maslow on the same system we build for you: our procedures are versioned skills, our engagements ship weekly written status, and our work product lives in your repos, not ours. The first proof of the product is the company.
Work directly with the founder.
Rakesh spent more than twenty years in enterprise technology, including CIO and CTO roles at Expedia and Aurobindo Pharma. He owned the budgets, legacy systems, and board questions that come with large technology programs. He founded Maslow after repeatedly seeing critical know-how locked in a few irreplaceable people and projects priced beyond the reach of mid-market companies. Today he builds knowledge graphs, agentic harnesses, and the AI employees they support.
You work directly with someone who has sat in your chair, on your side of the table.
For each engagement, Rakesh brings in specialist engineers from a small, trusted bench while retaining accountability for the work. Code, pipelines, skills, documentation, and status history live in your repositories from day one. That makes the engagement less dependent on any one person's availability, including his.
Rakesh serves as Chief AI Officer at Rivalista, where the same operating model runs in a second industry, and writes about AI cognition.