AI-powered data entry & processing workflows that save hours of manual work every week.
AI monitors Drake Tax and the client portal, auto-classifies incoming documents, flags what's missing against the prior-year return, and sends personalized nudges on a cadence — reducing document-gathering time by 65%.
AI monitors ONESOURCE and the client portal, auto-classifies incoming documents, flags what's missing against the prior-year return, and sends personalized nudges on a cadence — reducing document-gathering time by 65%.
AI watches calendar, email, and Lacerte activity, drafts timesheet entries per matter, and asks only for ambiguous slices — realization rate climbs 4–8 points.
AI monitors Lacerte and the client portal, auto-classifies incoming documents, flags what's missing against the prior-year return, and sends personalized nudges on a cadence — reducing document-gathering time by 65%.
AI ingests bank feeds, statements, and QuickBooks Online data, rebuilds a clean transaction timeline, and flags suspicious patterns — investigators start at analysis, not data entry.
AI ingests bank feeds, statements, and QuickBooks Online data, rebuilds a clean transaction timeline, and flags suspicious patterns — investigators start at analysis, not data entry.
AI watches calendar, email, and Workday activity, drafts timesheet entries per matter, and asks only for ambiguous slices — realization rate climbs 4–8 points.
AI monitors Workday and the client portal, auto-classifies incoming documents, flags what's missing against the prior-year return, and sends personalized nudges on a cadence — reducing document-gathering time by 65%.
AI ingests bank feeds, statements, and QuickBooks Online data, rebuilds a clean transaction timeline, and flags suspicious patterns — investigators start at analysis, not data entry.
AI watches calendar, email, and QuickBooks Online activity, drafts timesheet entries per matter, and asks only for ambiguous slices — realization rate climbs 4–8 points.
AI ingests bank feeds, statements, and IDEA data, rebuilds a clean transaction timeline, and flags suspicious patterns — investigators start at analysis, not data entry.
AI watches calendar, email, and IDEA activity, drafts timesheet entries per matter, and asks only for ambiguous slices — realization rate climbs 4–8 points.
AI ingests bank feeds, statements, and Bill.com data, rebuilds a clean transaction timeline, and flags suspicious patterns — investigators start at analysis, not data entry.
AI ingests bank feeds, statements, and HighRadius data, rebuilds a clean transaction timeline, and flags suspicious patterns — investigators start at analysis, not data entry.
AI watches calendar, email, and Canopy activity, drafts timesheet entries per matter, and asks only for ambiguous slices — realization rate climbs 4–8 points.
AI monitors Canopy and the client portal, auto-classifies incoming documents, flags what's missing against the prior-year return, and sends personalized nudges on a cadence — reducing document-gathering time by 65%.
AI ingests the transcript, produces a page-line summary with topic tags and exhibit cross-references, and flags contradictions across witnesses — associate produces a defensible summary in 30 minutes.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in Relativity, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI requests records via HITECH, ingests them into Relativity, builds a chronological medical timeline with visit-level detail, and flags gaps — demand package goes out in days, not weeks.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in Relativity, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI ingests the beneficiary's evidence into Relativity, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in Relativity, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI ingests client documents into Relativity, auto-populates schedules, runs the means test, flags inconsistencies, and drafts the creditor matrix — petition filed in days.
AI ingests the transcript, produces a page-line summary with topic tags and exhibit cross-references, and flags contradictions across witnesses — associate produces a defensible summary in 30 minutes.
AI ingests the beneficiary's evidence into INSZoom, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI ingests the transcript, produces a page-line summary with topic tags and exhibit cross-references, and flags contradictions across witnesses — associate produces a defensible summary in 30 minutes.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in INSZoom, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI ingests the beneficiary's evidence into INSZoom, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI ingests the questionnaire in any language, translates and normalizes it, validates dates against supporting docs, flags inconsistencies, and loads structured data into INSZoom.
AI ingests the transcript, produces a page-line summary with topic tags and exhibit cross-references, and flags contradictions across witnesses — associate produces a defensible summary in 30 minutes.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in ipManager, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI ingests client documents into CompuMark, auto-populates schedules, runs the means test, flags inconsistencies, and drafts the creditor matrix — petition filed in days.
AI ingests the transcript, produces a page-line summary with topic tags and exhibit cross-references, and flags contradictions across witnesses — associate produces a defensible summary in 30 minutes.
AI ingests the questionnaire in any language, translates and normalizes it, validates dates against supporting docs, flags inconsistencies, and loads structured data into MyCase.
AI requests records via HITECH, ingests them into MyCase, builds a chronological medical timeline with visit-level detail, and flags gaps — demand package goes out in days, not weeks.
AI ingests the beneficiary's evidence into WealthCounsel, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in EstateExec, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI ingests the beneficiary's evidence into EstateExec, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI ingests the beneficiary's evidence into iManage, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI ingests the questionnaire in any language, translates and normalizes it, validates dates against supporting docs, flags inconsistencies, and loads structured data into iManage.
AI ingests the questionnaire in any language, translates and normalizes it, validates dates against supporting docs, flags inconsistencies, and loads structured data into Workiva.
AI requests records via HITECH, ingests them into Workiva, builds a chronological medical timeline with visit-level detail, and flags gaps — demand package goes out in days, not weeks.
AI requests records via HITECH, ingests them into Litify, builds a chronological medical timeline with visit-level detail, and flags gaps — demand package goes out in days, not weeks.
AI ingests the beneficiary's evidence into Litify, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI ingests the questionnaire in any language, translates and normalizes it, validates dates against supporting docs, flags inconsistencies, and loads structured data into Litify.
AI requests records via HITECH, ingests them into Litify, builds a chronological medical timeline with visit-level detail, and flags gaps — demand package goes out in days, not weeks.
AI ingests client documents into Litify, auto-populates schedules, runs the means test, flags inconsistencies, and drafts the creditor matrix — petition filed in days.
AI ingests the transcript, produces a page-line summary with topic tags and exhibit cross-references, and flags contradictions across witnesses — associate produces a defensible summary in 30 minutes.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in Litify, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI ingests the beneficiary's evidence into MyCase, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI ingests client documents into Qualia, auto-populates schedules, runs the means test, flags inconsistencies, and drafts the creditor matrix — petition filed in days.
AI ingests the transcript, produces a page-line summary with topic tags and exhibit cross-references, and flags contradictions across witnesses — associate produces a defensible summary in 30 minutes.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in Qualia, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in Clio Manage, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI ingests the beneficiary's evidence into Clio Manage, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI ingests the transcript, produces a page-line summary with topic tags and exhibit cross-references, and flags contradictions across witnesses — associate produces a defensible summary in 30 minutes.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in Canopy, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI ingests client documents into Best Case, auto-populates schedules, runs the means test, flags inconsistencies, and drafts the creditor matrix — petition filed in days.
AI ingests the transcript, produces a page-line summary with topic tags and exhibit cross-references, and flags contradictions across witnesses — associate produces a defensible summary in 30 minutes.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in Best Case, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI pre-classifies documents by responsiveness, privilege, and hot-doc signal in Litify, leaving associates to review only the 15% that actually need judgment — review cost drops 60%.
AI requests records via HITECH, ingests them into Litify, builds a chronological medical timeline with visit-level detail, and flags gaps — demand package goes out in days, not weeks.
AI ingests the beneficiary's evidence into Aderant, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI ingests the questionnaire in any language, translates and normalizes it, validates dates against supporting docs, flags inconsistencies, and loads structured data into Aderant.
AI requests records via HITECH, ingests them into Elite 3E, builds a chronological medical timeline with visit-level detail, and flags gaps — demand package goes out in days, not weeks.
AI ingests client documents into Elite 3E, auto-populates schedules, runs the means test, flags inconsistencies, and drafts the creditor matrix — petition filed in days.
AI ingests the beneficiary's evidence into Ironclad, auto-builds the exhibit index, drafts forms, cross-checks consistency across the package, and flags missing items — petition prep time drops 70%.
AI ingests the questionnaire in any language, translates and normalizes it, validates dates against supporting docs, flags inconsistencies, and loads structured data into Ironclad.
AI requests records via HITECH, ingests them into Ironclad, builds a chronological medical timeline with visit-level detail, and flags gaps — demand package goes out in days, not weeks.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI drafts the encounter note from the visit audio and prior chart, populates orders and billing codes, and queues it for provider sign-off — charts close same-day, every day.
AI runs eligibility 72 hours before every appointment, flags inactive or changed coverage, and triggers patient outreach for new info — write-offs from bad eligibility drop 85%.
AI ingests each document into Orion, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI ingests each document into Orion, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI ingests each document into Encompass, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI ingests each document into Salesforce, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI ingests each document into Applied Epic, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI ingests each document into Applied Epic, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI ingests each document into Duck Creek, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI ingests each document into Qualia, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI ingests each document into PitchBook, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI ingests each document into Bloomberg AIM, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI ingests each document into Relius, classifies it, checks for completeness and consistency, chases missing items, and builds the underwriter-ready package — clear-to-close times drop.
AI extracts lease terms from PDFs into CoStar, flags non-standard clauses (exclusives, kickouts, renewal options), and produces a reviewer-ready abstract — weeks of work done overnight.
AI assembles the package from CoStar, normalizes financials to lender format, and pre-fills each lender's specific submission form — lenders respond to complete packages, not chased ones.
AI extracts lease terms from PDFs into CoStar, flags non-standard clauses (exclusives, kickouts, renewal options), and produces a reviewer-ready abstract — weeks of work done overnight.
AI extracts lease terms from PDFs into ARGUS Enterprise, flags non-standard clauses (exclusives, kickouts, renewal options), and produces a reviewer-ready abstract — weeks of work done overnight.
AI assembles the package from Procore, normalizes financials to lender format, and pre-fills each lender's specific submission form — lenders respond to complete packages, not chased ones.
AI extracts lease terms from PDFs into Procore, flags non-standard clauses (exclusives, kickouts, renewal options), and produces a reviewer-ready abstract — weeks of work done overnight.
AI extracts lease terms from PDFs into Yardi Voyager, flags non-standard clauses (exclusives, kickouts, renewal options), and produces a reviewer-ready abstract — weeks of work done overnight.
AI assembles the package from Yardi Voyager, normalizes financials to lender format, and pre-fills each lender's specific submission form — lenders respond to complete packages, not chased ones.
AI assembles the package from Yardi Voyager, normalizes financials to lender format, and pre-fills each lender's specific submission form — lenders respond to complete packages, not chased ones.
AI extracts lease terms from PDFs into MLS, flags non-standard clauses (exclusives, kickouts, renewal options), and produces a reviewer-ready abstract — weeks of work done overnight.
AI assembles the package from MLS, normalizes financials to lender format, and pre-fills each lender's specific submission form — lenders respond to complete packages, not chased ones.
AI assembles the package from Juniper Square, normalizes financials to lender format, and pre-fills each lender's specific submission form — lenders respond to complete packages, not chased ones.
AI extracts lease terms from PDFs into Juniper Square, flags non-standard clauses (exclusives, kickouts, renewal options), and produces a reviewer-ready abstract — weeks of work done overnight.
AI ingests property data, MLS comps, and inspection notes into Follow Up Boss, drafts the report narrative, places photos, and produces a reviewer-ready output — appraisal capacity doubles per appraiser.
AI generates listing copy from the property data, prepares photo tags, populates MLS fields in Skyslope, and drafts the launch package — listings go live same-day, not same-week.
AI ingests property data, MLS comps, and inspection notes into DealMachine, drafts the report narrative, places photos, and produces a reviewer-ready output — appraisal capacity doubles per appraiser.
AI generates listing copy from the property data, prepares photo tags, populates MLS fields in Hostfully, and drafts the launch package — listings go live same-day, not same-week.
AI ingests property data, MLS comps, and inspection notes into AppFolio, drafts the report narrative, places photos, and produces a reviewer-ready output — appraisal capacity doubles per appraiser.
AI generates listing copy from the property data, prepares photo tags, populates MLS fields in AppFolio, and drafts the launch package — listings go live same-day, not same-week.
AI ingests property data, MLS comps, and inspection notes into TOTAL by a la mode, drafts the report narrative, places photos, and produces a reviewer-ready output — appraisal capacity doubles per appraiser.
AI generates the design and permit package from site survey data in JobNimbus, runs structural and shade analysis, and submits to AHJ — design throughput per engineer doubles.
AI generates the design and permit package from site survey data in Aurora, runs structural and shade analysis, and submits to AHJ — design throughput per engineer doubles.
AI generates the design and permit package from site survey data in Pool Supply Pro, runs structural and shade analysis, and submits to AHJ — design throughput per engineer doubles.
AI generates the design and permit package from site survey data in Aspire, runs structural and shade analysis, and submits to AHJ — design throughput per engineer doubles.
AI ingests target company docs into DealStream, organizes them per standard data-room structure, redacts sensitive content, and drafts the diligence Q&A index — data room ready in days, not weeks.
AI ingests target company docs into DealCloud, organizes them per standard data-room structure, redacts sensitive content, and drafts the diligence Q&A index — data room ready in days, not weeks.
AI ingests target company docs into Hostfully, organizes them per standard data-room structure, redacts sensitive content, and drafts the diligence Q&A index — data room ready in days, not weeks.
AI assembles the trip sheet in Avinode, books FBO/catering/ground per client preferences, and produces the crew briefing pack — dispatcher confirms vs. assembles.
AI assembles the trip sheet in Axus Travel, books FBO/catering/ground per client preferences, and produces the crew briefing pack — dispatcher confirms vs. assembles.
