Announcing Deepsolve for small businesses

Deepsolve for Small Business is built on a simple idea: small businesses do important work, and they should be able to grow without needing an enterprise-sized budget. What usually stands in the way is a set of bottlenecks — points where a business struggles to serve more, or larger, customers. This post is about how we think about those bottlenecks, and a few of the things we have learned working on them.

Why small businesses?

After working across many industries, we have noticed that small businesses struggle to attract the same investment as enterprises. Talent tends to flow toward the companies with the most capital — big tech firms like Google, Amazon, and Atlassian, or trading firms like Optiver, IMC, and Jane Street. There is a good argument that those areas are already over-invested, while small businesses do vital work serving mum-and-pop stores and ordinary people. That work deserves the same quality of engineering the big players take for granted.

Engineering is problem-solving, not writing code

There is a common misconception that engineers just write code. Code is only a tool — the real aim is to solve business problems. Often that means helping a business serve its customers directly, for example by answering questions or taking orders. It can also mean working one step removed, by improving the efficiency of the team that serves those customers. In a consulting firm, raising the productivity and quality of consultants’ work flows straight through to clients.

In software, this idea is called developer productivity, and it can be measured with concrete signals like pull requests merged and deployments shipped per day. The interesting part is how these gains compound: a small, steady improvement in how well a team uses its time adds up to a real difference in what the business can take on.

A worked example: indexing in law

One of the clearest examples of a hidden bottleneck comes from law. Lawyers spend a great deal of time indexing documents, and it turns out to be a close cousin of a concept programmers know well.

An index in law works much like a database index in programming. Each document is given a unique reference and a short summary, so it can be found quickly among thousands of others. That speed matters most in a courtroom, where the bundle may be a physical pile of paper ordered by its index, and where finding the right document at the right moment can shape an argument.

Done by hand, indexing is slow and error-prone. It is exactly the kind of manual, repetitive work that people should not have to do — their time is better spent on the judgment-heavy, creative side of the job. This is the reasoning behind Deepsolve Legal, which we built and made free for law firms. Legal software has a reputation for being expensive and hard to use, and we wanted to show it does not have to be that way. With modern tooling, including AI, indexing that once took days can take minutes.

This is the lens we bring to any business: look for the repetitive, low-value work that quietly slows a team down, and remove it so people can focus on what only they can do. If any of this is useful or you would like to compare notes, you can reach us at hello@deepsolve.xyz.