INDEPENDENT CREATIVE DEVELOPMENT / 03
AI development & automation
Beyond the obvious.
AI is useful when it improves a real task. KESHET develops custom AI tools, intelligent interfaces and connected workflows, starting with what you need to accomplish rather than a technology in search of a purpose.
The right project may help people find information, work with documents, prepare a draft or move between systems with less manual effort. The important questions are what the tool should do, what information it can use and how someone will know whether the result is good enough.
Start with a specific job
A focused use case is easier to evaluate than a broad promise to transform everything. Identify the input, the expected output and the point where someone currently spends time or loses context. Sometimes a conventional integration or a simpler interface solves the problem better than adding a model.
Design for review and control
Generated output can be incomplete or wrong. A useful interface makes uncertainty visible, gives people a way to check results and keeps consequential actions under appropriate control. Where a workflow uses source material, showing the relevant evidence can help a person judge the response.
Connect carefully to the workflow
An AI feature needs more than a model call. The surrounding product determines which data is available, how requests are handled and what happens when the service fails. Data access, model-provider choices and the amount of automation should follow the needs and constraints of the project.
Experiment first. Evaluate what matters.
Choose a representative task and a small set of realistic examples. A prototype can reveal where the approach helps, where it breaks down and what a person still needs to review. Quality, latency and operating cost all affect whether the idea belongs in a production workflow. The next step should follow that evidence.
A few useful starting points
Does every automation need AI?
No. Repetitive tasks with clear rules can often use ordinary software or an integration. AI becomes relevant when the task involves language, varied source material or inputs that are difficult to handle with fixed rules.
What makes an AI prototype ready for the next step?
It should perform usefully on representative examples, make its limitations clear and fit the workflow around it. A convincing demonstration is a starting point; repeatable evaluation and a plan for errors matter before expanding its use.
LET’S MAKE SOMETHING USEFUL.