Code that evolves
with your business.

Problem

First Notice of Loss calls were averaging 14 minutes. Customers waited on hold after accidents while agents typed into three systems at once. After-hours volume routed straight to voicemail.

Approach

Trained a voice agent on five years of FNOL transcripts and policy data. Wired it directly into the dispatch, rental, and inspection systems so it could take action — not just collect information and hand off.

Solution

A 24/7 voice agent that handles standard claims end-to-end. Captures the loss details, verifies coverage, dispatches tow and rental, and books a photo inspection. Hands off complex losses to a human with full context attached.

Result

Average handled-call time down to 5 minutes. 62% of FNOL volume now fully automated. After-hours customer satisfaction scores up 31 points.

Problem

Internal helpdesk was drowning in 8,000 weekly tickets — password resets, software access, VPN failures. Median resolution was 4 hours. The 4-hour SLA was missed on 38% of P3s.

Approach

Built an agent that doesn't just answer questions — it acts. Integrated with the identity provider, ticketing system, and software catalog so it can resolve, not just deflect.

Solution

A Slack-integrated agent that resolves tickets it has authority to handle (provisions access, resets credentials, opens VPN exceptions) and escalates ambiguous cases with full diagnostic context already attached.

Result

64% of tickets resolved without human touch. Median resolution time down to 12 minutes. Helpdesk team reassigned to higher-tier security and platform work.

Problem

Underwriters were spending 6+ hours per loan manually pulling fields from PDFs — paystubs, tax returns, bank statements, W-2s. Throughput capped at 8 loans per underwriter per week. Missed inconsistencies were the leading cause of post-close repurchase risk.

Approach

Built a domain-tuned extraction pipeline that handles both structured forms (1040, W-2) and messy unstructured documents (bank statements with mixed deposit types). Cross-validates every extracted field against the application data.

Solution

A submission portal that auto-extracts the entire loan package, flags discrepancies with the supporting evidence inline, and presents the underwriter with a pre-validated case file ready for decisioning.

Result

Per-loan underwriter time down 78%. Throughput up 3x without adding headcount. Repurchase risk from missed inconsistencies down 41% in the first year.