We connect AI to the data, software, and review steps your team depends on. The useful result is work people can inspect, correct, and act on.
Some steps follow clear rules. Others involve a document, a question, or an exception that needs interpretation. We find where AI can help, where a straightforward integration will do, and what a person still needs to decide.
That can mean an assistant that works with internal information, extracting details from incoming files, or preparing a summary for review. The workflow connects those tasks to the systems your team uses, so the output has a place to go and a way to be checked.
We define which source holds the facts, what the AI can access or change, and how corrections and unusual cases return to the right person. Those decisions make the integration useful in daily work.
AI can prepare an answer or suggest a next step. Your team needs to know where it came from, whether it needs review, and what happens when it is wrong or incomplete.
We build the surrounding software for access, handoffs, review, and correction. A useful first release handles a complete part of the process and gives your team a clear way to work with its output.
One supports a specialized AI product. The other connects AI to our own business records.
Kolyid brings Yiddish audio, transcript cleaning, AI speech recognition, and training-data preparation into one workflow. Reviewers can compare words with the source audio and check uncertain output in production transcripts.
See the Kolyid AI case studyA scheduled ChatGPT workflow has read-only access to our bank data and read and write access to Talli, the system we built for our own operations. It compares income and expenses, updates Talli, and flags questions or discrepancies for our review.
See how we use AI with TalliTell us what your team does today, where the current tools leave gaps, and what decisions need a person in the loop.