AI consulting in Des Moines, IA

Build a test set before you build anything else. We pull a sample of past files with known outcomes, have the model produce summaries, and ask experienced underwriters to score them against a simple rubric covering missing facts, wrong facts and useful order. That gives a baseline error rate and shows which document types the model handles poorly. The pilot then runs with underwriters checking every summary, and we track corrections over time. Expansion to new product lines happens only when the correction rate is acceptable to your underwriting leadership.

It can help reviewers, not replace them. A practical use is checking applications for missing or inconsistent information, such as income and liquid assets that don't line up with the product chosen, and routing them to a person with a note. The decision and documentation stay with your suitability team. We log what the AI flagged and what the reviewer decided, which gives compliance a record. Any change to the review process should go through your compliance and legal teams before rollout.

Usually not yet, and that's normal. Producer records in carrier systems often have duplicates, outdated contact details and inconsistent hierarchy, and AI built on that will give confident wrong answers. The readiness assessment measures how far off the data is and what matters most for the use case you have in mind. Sometimes a few weeks of cleanup and a rule for who owns producer data is enough. Other times it makes sense to fix the CRM first and start AI on a document task that doesn't depend on producer data.