Reps were losing roughly a third of the week to manual data entry, and the pipeline itself couldn't be trusted: stage, value and next steps were typed in from memory, hours after the conversation that actually changed them.

• Agents that read Gmail and Calendar and infer deal stage, sentiment and momentum, citing the exact line behind every value, never guessing
• An approval gate on every outbound action: drafts wait for a human before anything sends
• A deal-health monitor that flags stalling and going-cold deals before they die

Every manufacturer sent paperwork in a different format, some as blurry scans or phone photos. The team spent 2+ hours a week manually reformatting it into company-branded templates, with an error rate around 10%, well above the 1% they needed.

• OCR extraction that preserves tables, forms and structure even from low-quality scans
• An AI standardization layer that maps extracted content into the company's own templates, resolving ambiguous fields
• A side-by-side editor for quick human validation before anything ships
• Automatic branding: letterhead, headers and footers applied on export

The path from a customer demand signal to a consultant on the ground ran through 70+ manual handoffs across multiple teams, taking weeks where it should take days. Every day of delay was revenue left on the table.

• Demand-signal intake that auto-triages internal availability vs. external hiring needs
• Automated service-order generation with the correct role requirements, timeline and billing codes
• AI-matched and ranked candidate shortlists with explainable scoring, not just a list
• Autonomous screening interviews, customer-interview prep, and approval-routed allocation through to purchase-order closure

Critical answers lived across SharePoint, Confluence, policies, tickets and tribal knowledge. Employees spent hours searching and still couldn't verify whether an answer was current or authorized.

• Multi-source ingestion with folder- and role-level access controls, spanning documents, tables and unstructured text
• Citations to the exact page and source object behind every answer, not a generic summary
• Feedback, evaluation and escalation loops that improve the assistant over time

Scaling expertise across 70,000+ diagnosis codes and 1,000+ agents was nearly impossible. High attrition and long training curves led to inconsistent claim handling, and unstructured agent notes broke both audits and automation.

• A PHI-safe next-best-action recommender grounded in diagnosis codes, insurance metadata and historical claim activity
• A comment-enhancement engine that structures agent notes into clean, audit-ready logs
• A vectorized claim-allocation matcher that cut comparison time from 10 seconds to 0.25 seconds
• Hallucination guardrails and queue-based throttling to hold accuracy at scale