Build or buy6 posts
When is it worth building a system of your own?
Building your own system is worth it when a workflow is specific to how your company runs, repeats every week, and costs more in staff hours than the build and its upkeep would. When the work is generic, buy a product. When the product forces your team to change how it works, the product is the expensive option.
3 min
No. 25: AI demo vs production: what week six brings that the boardroom never saw
The gap in AI demo vs production is mostly the cases left out: a demo runs on clean examples somebody picked, and production gets whatever the operation produces that week, including the days you would never choose to show. Before signing, ask to see it run on a bad week of your own records.
3 min
No. 18: How much does an AI system of your own cost, and what it costs to change later
How much a custom AI system costs comes down to two sums: the build price, and what running and changing it costs after launch. Weigh both against what the work costs your team today, in hours at your own rate. OCTYN quotes its price after a mapping call, fixed and in writing.
5 min
No. 12: Who owns the code when an agency builds your system, and what to get in writing
Who owns the code when an agency builds your system should be settled in writing, and the answer is you: your data, specs, configuration and system code. Get where it will run on paper before anything is built, and ask for documentation good enough that someone other than the builder could take it over.
5 min
No. 08: AI agent vs workflow automation for small business: most of the work wants rules
For a small business weighing an AI agent against workflow automation, most of the work belongs to workflow automation: rules with tests beside them, so the same input decides the same way every time. Keep the model to the narrow parts that need judgement, like reading a website or drafting a first email, and check what it returns.
5 min
No. 05: How to scope an AI project so it ships: write down what it will never do
To scope an AI project so it ships, tie it to one piece of work your team repeats every week, with your weekly hours on it, and then write down what the first version will never do. Feature lists grow with every meeting. The stated limits keep it small enough to finish and safe enough to leave running.
4 min
No. 03: Build vs buy AI tools for operations: look for the side spreadsheet
On build vs buy AI tools for operations, buy wherever your workflow looks like everyone else's and build only the narrow part that is specific to how you work. The deciding cost arrives later, every day, in the workarounds a team invents when a bought product cannot hold one of its steps.
The systems and arguments this topic points at.
The rest of the writing.
- Client contextThe work of reassembling who a client is before every next touchpoint, and how to stop doing it.
- Automation that holds upSilent failures, gates, rollbacks and the checks that keep a running system honest.
- Founder operationsCounting the hours that never needed a human, and deciding what to hand to a system first.
- Growth systemsContent and outbound that keep running when the founder is busy, and still sound like the company.
- Operations systemsIntake, handoffs and status carried by one system, so the team keeps the judgement calls.
- Product systemsA model inside a product: speed, fallbacks, keys, and what running it costs after launch.
Bring the version of this that is about your company.
The first conversation is with the people who would build it. If there is a fit, the Consult comes next.