Case studies
Each study documents full AI automation—not single tools: multi-agent systems, cross-department workflows, technical architecture, and measurable outcomes.
Construction – Multi-Location GC (Southeast U.S.): Estimating-to-Field Dispatch AI System
ConstructionTimeline: Build phase: weeks 1–10 (integrations, agents, pilot branch). Deployment: weeks 11–13 (read-only shadow, then cutover). Optimization: weeks 14–21 (dispatch model tuning, RFI quality).
PrimeAxiom replaced fragmented manual coordination with a single AI-orchestrated pipeline from lead packet to dispatched crew, cutting errors and calendar slack across multiple Southeastern states.
Core problem
Manual processes: coordinators triaged inbound leads from five channels, re-keyed scope into templates, and chased photos and site notes in chat. Bottlenecks appeared at estimating handoff (missing measurements), dispatch (who was actually free), and payroll (hours didn’t match dispatched tasks). At scale, duplicate bids, missed callbacks, and Friday overtime spikes broke margin assumptions.
Full system
PrimeAxiom built a unified automation fabric—not a chatbot. Inbound triggers create a canonical Job Intent Record (JIR) with extracted scope, geo, and risk flags. Estimating receives structured packets plus AI-summarized site constraints. Dispatch uses an Operations Agent that reads crew skills, drive-time, and contract SLAs, proposing slots; humans approve exceptions only. Field techs confirm via mobile; hours and tasks sync to Procore and payroll staging. Reporting rolls up margin risk and backlog daily.
Results
Efficiency: 62% reduction in manual coordinator steps per active job. Conversions: win rate on qualified bids +18% (faster response). Manual work: 380+ hours/month saved org-wide. Revenue: margin on rush overtime down 2.1 points; recognized revenue timing improved with fewer stalled starts.
Timeline: Build: 12 weeks (compliance gates). Deploy: 4 weeks phased by site. Optimization: ongoing denial taxonomy.
PrimeAxiom automated the full revenue-cycle chain from schedule trigger to paid claim, using AI agents for auth and appeals—not single-point bots.
Core problem
Manual re-keying, unclear ownership of auth status, and denials discovered late. Bottlenecks at auth submission and COB verification. Scale broke when new providers joined with different payer mixes.
Full system
Orchestrated pipeline: appointment → eligibility bot → auth workflow with document AI → claim generation checks → denial classification → appeal drafting with human sign-off.
Results
Denials down 27%; prior auth turnaround improved 44%; FTE-equivalent 2.4 roles redeployed to patient-facing work; cash velocity improved 9% in 90 days.
Timeline: Build: 11 weeks. Deploy: phased by case type. Optimization: 12 weeks on demand templates.
PrimeAxiom automated the full intake-to-production pipeline so AI agents handled repetitive legal operations while attorneys supervised strategy.
Core problem
Manual intake scripts, lost web leads after hours, records chaos, copy-paste demands. Bottlenecks at paralegal assignment and insurer comms. Revenue impact when statute deadlines loomed.
Full system
Unified matter creation from omnichannel intake, AI scoring for case viability, automated conflicts and retainer flows, records orchestration with status agents, demand workspace with RAG over case documents.
Results
Qualified intakes +31% without headcount; records cycle time −40%; demand first-draft time −55%; attorney review hours reallocated to court appearances.
Timeline: Build: 7 weeks. Deploy: 2 weeks training. Optimization: 8 weeks milestone tuning.
PrimeAxiom replaced manual thread-chasing with an AI-orchestrated transaction system spanning people, documents, and calendars.
Core problem
Email silos, manual checklist tracking, no single timeline truth. Bottlenecks at doc collection and title. Revenue risk when deals stalled silently.
Full system
Canonical transaction object; AI reads contracts and amendments; milestone engine; comms agents with brand voice; dashboard for leadership.
Results
Missed contingency deadlines −88%; coordinator touches per file −52%; agent NPS +11; time-to-close variance −19%.
Financial Services – Mid-Size RIA ($4B AUM): Client Onboarding & Compliance Automation
Financial ServicesTimeline: Build: 13 weeks with CCO oversight. Deploy: 3 weeks pilot team. Optimization: continuous rule library.
PrimeAxiom automated the full client lifecycle handshake from data capture to custodian funding with AI doing document intelligence—not checkbox macros.
Core problem
Manual form chasing, inconsistent suitability files, duplicate custodian entry. Regulatory scale risk.
Full system
Orchestrated onboarding graph: intake → verify → suitability AI → custodian packets → surveillance hooks for changes.
Results
Onboarding cycle −46%; exception rework −63%; exam prep binder time −55%; advisors gained ~6 hrs/week each of administrative recovery.
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Construction – Heavy Civil Subcontractor (Sun Belt): Change-Order & Lien-Exposure Automation
ConstructionTimeline: Build: 8 weeks. Deploy: 2 weeks. Optimization: 6 weeks for GC-specific templates.
End-to-end AI automation tied field reality to legal calendars and GC systems so change orders became traceable, timely, and financially controlled instead of chat-thread chaos.
Core problem
Manual transcription from radios and texts, inconsistent CO numbering, and no single source of truth for what was approved vs. in dispute. Bottlenecks at PM review and legal sign-off. At scale, one missed lien window erased margin on entire jobs.
Full system
Event-driven automation with AI document understanding for CO PDFs from GCs, voice-to-structure for field reports, exposure scoring (cost + calendar), and agent-driven notifications to GCs and internal counsel prep packs.
Results
Missed notice incidents fell 91%; average change-order approval cycle shortened 35%; finance recovered 22 hours/week of paralegal-adjacent tracking; DSO on disputed extras improved 12%.
Timeline: Build: 9 weeks. Deploy: 3 weeks per wave. Optimization: 10 weeks protocol alignment.
A full-stack AI automation linked intake language to clinical workflows and structured follow-up, increasing throughput and care quality simultaneously.
Core problem
Fragmented intake, triage overload, inconsistent discharge. Manual bottlenecks at peak flu season. Scale stress broke phone queues.
Full system
Intake Agent structures complaints and history; Triage Assist suggests ESI-aligned pathways (non-diagnostic); orders staged for provider approval; Discharge Agent builds education and PCP follow-up.
Results
Door-to-provider time −23%; duplicate data entry −68%; post-visit ED bounce-backs −15% in pilot sites; nursing admin time −120 hrs/week network-wide.
Timeline: Build: 14 weeks. Deploy: matter-by-matter. Optimization: model refresh per practice group.
Full AI automation across discovery operations replaced ad-hoc contract attorney armies with repeatable, auditable agent workflows.
Core problem
Manual linear review, inconsistent privilege, slow narrative prep. Scale issues on large custodian sets.
Full system
Ingest pipelines, AI coding with confidence bands, privilege separation workflows, production export checks, deposition notebooks auto-built.
Results
Review throughput +2.1x; privilege QC incidents −76%; partner prep time −34%; outside review spend −28% on comparable matters.
Timeline: Build: 10 weeks. Deploy: portfolio waves. Optimization: 9 weeks vendor model.
Full AI automation across maintenance and reporting replaced spreadsheet storytelling with live, agent-generated operational intelligence.
Core problem
Manual categorization, vendor phone tag, owner narrative built last-minute. Scale broke during hurricane season surges.
Full system
WO ingestion from portals/email, AI categorization and urgency, vendor Agent with performance history, capex summarization, owner briefing Agent.
Results
Work-order resolution time −29%; duplicate WOs −81%; owner report prep −17 hrs/week per portfolio manager; renewal revenue forecasting error −22%.
Financial Services – Regional Credit Union (8 Branches): Loan Origination & Decision-Support AI
Financial ServicesTimeline: Build: 11 weeks. Deploy: branch pilot then all. Optimization: 10 weeks credit policy alignment.
PrimeAxiom replaced lending assembly lines with AI-orchestrated origination where agents handle documents, communications, and structured underwriting prep.
Core problem
Fragmented apps, manual stip tracking, underwriter queue starvation. Member drop-off at document upload.
Full system
Application Agent, Doc Classify Agent, Stipulation Chaser Agent, Underwriting Summary Agent, Member Comms Agent with fair-lending guardrails.
Results
Pull-through +24%; cycle time −33%; processor manual minutes per loan −61%; member satisfaction +18 points.
Construction – Design-Build Firm (Mountain West): Preconstruction & Subcontractor Bid AI
ConstructionTimeline: Build: 9 weeks. Deploy: 2 weeks. Optimization: 7 weeks bid-leveling models.
PrimeAxiom automated the entire preconstruction funnel from RFP drop to leadership decision, replacing email-thread bidding with agent-orchestrated data.
Core problem
Manual RFP reading, inconsistent sub quotes, hidden scope gaps. Bottleneck at bid leveling and risk surfacing.
Full system
RFP Intelligence pipeline, Sub Outreach Agent, Bid Normalization Agent, Risk Scoring Agent, Executive Brief Agent.
Results
Bid preparation hours −47%; subcontractor response rate +29%; estimate error variance −18%; executive review time −63%.
Timeline: Build: 10 weeks. Deploy: OR-by-OR. Optimization: 11 weeks specialty tuning.
PrimeAxiom fused clinical scheduling with supply and payer automation so AI agents operated the ASC like a synchronized system, not isolated spreadsheets.
Core problem
Manual block management, paper implant logs, faxed auths. Revenue lost to idle rooms and last-minute cancels.
Full system
Block Optimizer Agent, Implant Forecast Agent, Auth Watch Agent, Vendor Sync Agent.
Results
OR utilization +8.4%; canceled cases for admin reasons −41%; implant reconciliation lag −72%; materials waste −19%.
Timeline: Build: 8 weeks. Deploy: 2 weeks. Optimization: 9 weeks rule expansion.
PrimeAxiom automated multi-state employment litigation operations with AI agents enforcing rules and communications end-to-end.
Core problem
Manual routing, calendar risk, repetitive client updates. Scale limited new office openings.
Full system
Intake AI, Jurisdiction Graph, Deadline Engine, Task Factory, Client Pulse Agent.
Results
Mis-filed matters −86%; deadline incidents −94%; paralegal reassignment time −57%; client satisfaction +21 NPS.
Timeline: Build: 10 weeks. Deploy: deal-team pilot. Optimization: 8 weeks sector models.
PrimeAxiom replaced manual OM-to-model work with an AI-orchestrated diligence system spanning documents, numbers, and investment committee narrative.
Core problem
Manual spreadsheet builds, inconsistent lease interpretation, memo bottlenecks.
Full system
OM Extract Agent, Rent Roll Recon Agent, Credit Narrative Agent, IC Memo Agent.
Results
Analyst model build time −61%; reconciliation errors −78%; IC memo turnaround −54%; bid participation +22%.
Financial Services – Boutique Broker-Dealer: Supervision & Communications Surveillance AI
Financial ServicesTimeline: Build: 12 weeks. Deploy: supervised rollout. Optimization: model calibration quarterly.
PrimeAxiom automated supervisory workflows end-to-end so AI agents triaged, summarized, and packaged compliance cases—not keyword alerts alone.
Core problem
Linear review, alert fatigue, exam scramble. Regulatory scale risk.
Full system
Surveillance Ingest Agent, Risk Rank Agent, Review Brief Agent, Case Binder Agent.
Results
Reviewer throughput +2.4x; false-positive burden −51%; exam prep cycles −48%; repeat violations −33%.
Timeline: Build: 8 weeks. Deploy: tier-1 customers first. Optimization: 10 weeks health models.
PrimeAxiom automated the full post-sale lifecycle with AI agents owning telemetry interpretation and revenue motions—not static playbooks.
Core problem
Manual onboarding plans, late churn detection, ad hoc expansion. Scale broke as ARR grew.
Full system
Onboarding Factory Agent, Health Agent, QBR Agent, Expansion Router Agent.
Results
Time-to-value −38%; gross churn −27%; expansion pipeline +33%; CSM admin time −41%.
Timeline: Build: 7 weeks. Deploy: global. Optimization: 6 weeks questionnaire library.
PrimeAxiom connected PLG telemetry to enterprise closing with full AI automation across security, legal, and technical sales prep.
Core problem
Manual qualification, questionnaire hell, duplicate SE work.
Full system
PQL Agent, Security RAG Agent, Redline Assist Agent, SE Brief Agent.
Results
PQL-to-opp conversion +44%; security review cycle −52%; SE prep hours −68%; enterprise ASP +19%.
Timeline: Build: 9 weeks. Deploy: support shadowing. Optimization: 8 weeks tax season.
PrimeAxiom automated employer payroll operations with AI agents spanning ticketing, payroll data, and compliance workflows.
Core problem
Manual ticket handling, hidden registration gaps, reactive support.
Full system
Ticket Triage Agent, Payroll Trace Agent, Employer Comms Agent, Registration Agent.
Results
MTTR on payroll tickets −46%; employer-visible errors −39%; support cost per employer −28%; NRR +6 pts.
Timeline: Build: 11 weeks. Deploy: category pilot. Optimization: 12 weeks seasonality.
PrimeAxiom unified merchandising, supply chain, and customer recovery in one AI-orchestrated operating system.
Core problem
Manual forecasting, slow PO cycles, CX disconnected from inventory.
Full system
Demand Agent, PO Draft Agent, 3PL Router Agent, CX Resolution Agent.
Results
Stockout rate −31%; margin on promotions +2.3 pts; CX resolution time −44%; planner manual hours −52%.
Timeline: Build: 10 weeks. Deploy: seller tiers. Optimization: 9 weeks risk models.
PrimeAxiom automated trust, matching, and fulfillment exceptions across a B2B marketplace with multi-agent coordination.
Core problem
Manual catalog review, email RFQs, chaotic logistics.
Full system
Catalog Agent, Risk Agent, RFQ Agent, Logistics Exception Agent.
Results
Catalog approval time −58%; dispute rate −37%; RFQ-to-order conversion +26%; ops headcount per GMV −19%.
Construction – Specialty Electrical Contractor (Gulf Coast): Workforce & Certification AI System
ConstructionTimeline: Build: 8 weeks. Deploy: craft trade-by-trade. Optimization: 6 weeks union rule packs.
PrimeAxiom automated the full human-capital loop for field electrical work—matching, compliance, safety, and pay—in one AI-governed system.
Core problem
Manual roster checks, paper safety, payroll fights. Bottleneck at dispatch desk during storm response.
Full system
Cert Graph Agent, Match Agent, Safety Brief Agent, Hours Recon Agent.
Results
Dispatch errors −72%; certification lapses −89%; payroll disputes −54%; job readiness time −41%.
Timeline: Build: 11 weeks. Deploy: trauma centers first. Optimization: 10 weeks network routing.
PrimeAxiom automated hospital-to-hospital blood logistics with AI agents coordinating inventory, approvals, and transport—not phone trees.
Core problem
Manual inventory calls, slow transfers, expiry waste. Patient risk at peak demand.
Full system
Inventory Agent, Priority Agent, Approval Agent, Courier Agent.
Results
Turnaround for rare units −56%; blood waste −23%; transfer approval time −48%; clinician phone burden −62%.
Timeline: Build: 7 weeks. Deploy: practice group pilot. Optimization: 8 weeks carrier packs.
PrimeAxiom automated insurance-defense revenue operations so AI agents enforced structure before carriers ever saw a line item.
Core problem
Manual LEDES pain, rejections, slow cash. Scale limited new carrier panels.
Full system
Time Capture Agent, Narrative Agent, Rule Validator Agent, Resubmission Agent.
Results
Invoice rejection rate −69%; days-to-pay −22%; write-offs from billing errors −81%; billing clerk hours −47%.
Timeline: Build: 9 weeks. Deploy: property cluster pilot. Optimization: 9 weeks academic calendar.
PrimeAxiom automated the full student housing cycle from prospect to move-in readiness with interconnected AI agents.
Core problem
Manual bed tracking, guarantor chase, turn chaos.
Full system
Tour Agent, Guarantor Agent, Risk Agent, Turn Project Agent.
Results
Lease-up velocity +17%; guarantor completion time −44%; turn delays −31%; leasing labor per bed −28%.
Timeline: Build: 13 weeks. Deploy: shadow mode first. Optimization: continuous graph features.
PrimeAxiom replaced linear fraud queues with AI-orchestrated investigation systems spanning data, narrative, and compliance output.
Core problem
Alert fatigue, slow investigations, inconsistent SAR quality.
Full system
Graph Agent, Case Brief Agent, Evidence Agent, SAR Draft Agent.
Results
Alert precision +37%; analyst cases/day +2.1x; time-to-SAR −58%; fraud loss rate −24%.
Timeline: Build: 6 weeks. Deploy: all tiers. Optimization: 5 weeks doc embeddings.
PrimeAxiom automated the entire API customer lifecycle with AI agents grounded in live docs and telemetry—not generic chat.
Core problem
Doc drift, noisy alerts, manual incident comms.
Full system
RAG Support Agent, Anomaly Agent, Incident Timeline Agent, Comms Agent.
Results
Ticket deflection +51%; MTTR −43%; API abuse detection lead time +3.2x faster; CSAT for developers +14.
Timeline: Build: 10 weeks. Deploy: hub-by-hub. Optimization: 11 weeks demand spikes.
PrimeAxiom automated dark-store operations end-to-end—warehouse, last mile, and customer recovery—in one AI stack.
Core problem
Manual picking, bad subs, rider chaos at peak.
Full system
Pick Agent, Substitution Agent, Rider Agent, Recovery Agent.
Results
Units per labor hour +19%; substitution acceptance +27%; on-time delivery +16%; support contacts per order −35%.
Timeline: Build: 9 weeks. Deploy: category rollout. Optimization: 10 weeks model refresh.
PrimeAxiom automated marketplace trust and money movement with AI agents spanning vision, policy, and payments.
Core problem
Manual auth queue, pricing chaos, payout delays.
Full system
Vision Agent, Risk Agent, Pricing Agent, Dispute Agent, Payout Agent.
Results
Authentication throughput +2.3x; fake listing catch rate +41%; dispute resolution time −52%; seller NPS +18.
Timeline: Build: 12 weeks. Deploy: line-by-line. Optimization: 10 weeks vision retrain cadence.
PrimeAxiom fused vision AI with manufacturing execution so defects triggered a full automated operational response across plants and suppliers.
Core problem
Paper trails, slow andon, duplicate ERP entry.
Full system
Vision Agent, Andon Agent, Containment Agent, Rework Agent.
Results
False escape rate −46%; andon response time −38%; rework data entry −91%; supplier chargeback recovery +29%.
Timeline: Build: 10 weeks. Deploy: facility pairs. Optimization: 8 weeks SKU library.
PrimeAxiom automated recipe-to-floor compliance so AI agents governed what workers executed and what labs verified.
Core problem
Email recipe drift, manual COA checks, audit scramble.
Full system
Recipe Graph Agent, Allergen Agent, Batch Sheet Agent, COA Agent.
Results
Wrong-batch incidents −83%; allergen review time −67%; COA mismatch catch +2.1x faster; audit prep −54% hours.
Manufacturing – Aerospace Machining Shop (Southwest): Precision Scheduling & NADCAP Trace AI
ManufacturingTimeline: Build: 11 weeks. Deploy: cell-by-cell. Optimization: 9 weeks capacity models.
PrimeAxiom automated machining operations from schedule to certification package with AI agents enforcing aerospace traceability.
Core problem
Manual scheduling, paper travelers, audit panic.
Full system
Schedule Agent, Traveler Agent, Metrology Agent, Audit Binder Agent.
Results
On-time delivery +21%; setup time waste −34%; audit prep hours −76%; scrap rate −18%.
Timeline: Build: 13 weeks. Deploy: dual shift. Optimization: 12 weeks OEM spec updates.
PrimeAxiom fused vision, telemetry, and serialization into one AI-driven manufacturing system for EV battery packs.
Core problem
Siloed data, late defect discovery, serialization risk.
Full system
Vision Agent, Telemetry Agent, Serialization Agent, Containment Agent.
Results
Customer-reported defects −62%; line stoppage diagnosis time −57%; serialization errors −94%; rework hours −48%.
Local Services – Multi-Location HVAC & Plumbing (Metro): Dispatch & Parts Intelligence AI
Local Service BusinessesTimeline: Build: 7 weeks. Deploy: branch rollout. Optimization: 8 weeks parts model.
PrimeAxiom automated the entire HVAC/plumbing service loop from emergency call to cash—with AI agents coordinating people, trucks, and billing.
Core problem
Manual dispatch, parts guesswork, billing delay.
Full system
Triage Agent, Route Agent, Parts Agent, Billing Agent.
Results
First-time fix rate +18%; average drive time −22%; invoice lag −64%; revenue per tech +12%.
Local Services – Regional Pest Control: Route Density & Treatment Protocol AI
Local Service BusinessesTimeline: Build: 8 weeks. Deploy: district by district. Optimization: 7 weeks seasonality.
PrimeAxiom automated pest control operations from routing to compliance logs with AI agents tied to regulatory reality.
Core problem
Manual routes, log gaps, churn.
Full system
Route Agent, Protocol Agent, Compliance Agent, Renewal Agent.
Results
Stops per day +14%; chemical overuse incidents −88%; renewal rate +9%; dispatcher hours −41%.
Local Services – Multi-Site Funeral & Cremation Provider: Family Care & Regulatory AI
Local Service BusinessesTimeline: Build: 9 weeks. Deploy: site-by-site. Optimization: 10 weeks jurisdictional packs.
PrimeAxiom automated sensitive funeral operations with AI agents that assembled compliant workflows while preserving human care.
Core problem
Manual forms, scheduling clashes, staff burnout.
Full system
Intake Agent, Document Agent, Scheduler Agent, Vendor Agent.
Results
Family-facing data entry −59%; paperwork error rate −81%; scheduling conflicts −73%; coordinator emotional bandwidth improved (survey +27%).
Local Services – Commercial Cleaning Franchise Network: QA & Compliance Auditing AI
Local Service BusinessesTimeline: Build: 7 weeks. Deploy: regional pilots. Optimization: 8 weeks franchise benchmarks.
PrimeAxiom automated franchise quality operations with AI agents assessing visual evidence and driving coaching loops.
Core problem
Manual QA, subjective scoring, late client visibility.
Full system
Vision QA Agent, Compliance Agent, Coaching Agent, Client Report Agent.
Results
Audit cycle time −68%; client churn risk accounts flagged 3x earlier; franchisee compliance +22%; HQ labor on QA −52%.
Timeline: Build: 8 weeks. Deploy: pod by pod. Optimization: 9 weeks vertical playbooks.
PrimeAxiom automated the full performance marketing lifecycle—brief, creative, spend, and proof—with AI agents embedded in agency workflows.
Core problem
Manual reporting, Slack chaos, overnight spend risk.
Full system
Brief Agent, Creative Agent, Pacing Agent, Report Agent.
Results
Reporting hours −63%; pacing incident rate −76%; creative cycle time −41%; client retention +14%.
Timeline: Build: 9 weeks. Deploy: show-by-show. Optimization: 7 weeks client templates.
PrimeAxiom automated the production pipeline from creative intent to delivery-ready packages with AI agents coordinating assets and approvals.
Core problem
Manual feedback, rights risk, delivery errors.
Full system
Script Agent, Review Agent, Rights Agent, Delivery Agent.
Results
Producer admin time −54%; revision rounds −23%; rights clearance time −48%; delivery rejections −71%.
Timeline: Build: 6 weeks. Deploy: account clusters. Optimization: 8 weeks journalist models.
PrimeAxiom automated earned media workflows with AI agents researching, drafting, and alerting—not generic blasts.
Core problem
Generic outreach, slow news response, reporting burden.
Full system
Graph Agent, Pitch Agent, Newsjack Agent, Coverage Agent.
Results
Reply rate +31%; story placement lead time −42%; analyst briefing prep −58%; account team research hours −49%.
Timeline: Build: 7 weeks. Deploy: studio-wide. Optimization: 6 weeks skill ontology.
PrimeAxiom automated agency operations planning—forecasting, assignment, and margin protection—with AI agents tied to real pipeline and talent data.
Core problem
Manual staffing, margin blindness, burnout.
Full system
Forecast Agent, Match Agent, Margin Guard Agent.
Results
Utilization +12 pts without headcount; margin on projects +4.1 pts; resourcing meeting time −71%; designer overtime −38%.
Timeline: Build: 12 weeks. Deploy: DC-by-DC. Optimization: 11 weeks seasonality.
PrimeAxiom automated warehouse operations with AI agents optimizing space, labor, and waves as a single system—not static slotting rules.
Core problem
Manual slotting, wave inefficiency, labor mismatch.
Full system
Slotting Agent, Wave Agent, Labor Agent, SLA Agent.
Results
Pick path efficiency +17%; labor overtime −29%; SLA breach incidents −44%; inventory accuracy +0.3 pts (high-value SKUs).
Timeline: Build: 9 weeks. Deploy: lane-by-lane. Optimization: 10 weeks market models.
PrimeAxiom automated freight brokerage with AI agents matching carriers, pricing, and claims—end-to-end.
Core problem
Manual matching, compliance gaps, claims drag.
Full system
Load Agent, Match Agent, Price Agent, Claims Agent.
Results
Cover time −35%; fall-through rate −22%; claims cycle time −48%; gross margin per load +1.9 pts.
Timeline: Build: 10 weeks. Deploy: lane pilot. Optimization: 9 weeks IoT calibration.
PrimeAxiom automated pharmaceutical cold-chain delivery with AI agents monitoring temperature, predicting failure, and communicating proactively.
Core problem
Reactive temp handling, manual patient comms.
Full system
Telemetry Agent, Predict Agent, Patient Agent, Returns Agent.
Results
Excursion losses −57%; redelivery rate −41%; compliance audit prep −69%; patient satisfaction +23.
Timeline: Build: 11 weeks. Deploy: shift-by-shift. Optimization: 8 weeks peak profiles.
PrimeAxiom automated hub sortation with AI agents predicting failures and orchestrating labor and maintenance together.
Core problem
Late mis-sort detection, jam cascades, reactive maintenance.
Full system
Vision Agent, Predict Agent, Maintenance Agent, Labor Agent.
Results
Mis-sort rate −63%; unplanned downtime −38%; maintenance response −52%; throughput per hour +11%.
Timeline: Build: 14 weeks. Deploy: line-of-business waves. Optimization: continuous fraud model.
PrimeAxiom automated first notice of loss through triage and routing with AI agents—not IVR trees.
Core problem
Manual FNOL, slow triage, vendor latency.
Full system
FNOL Agent, Fraud Agent, Routing Agent, Vendor Agent.
Results
FNOL-to-adjuster assignment time −61%; straight-through processing +19%; fraud referral precision +33%; customer NPS +17.
Timeline: Build: 8 weeks. Deploy: employer cohorts. Optimization: 9 weeks carrier maps.
PrimeAxiom automated group benefits administration with AI agents handling data, eligibility, and carrier file integrity.
Core problem
Manual census, QLE backlog, EDI errors.
Full system
Census Agent, Eligibility Agent, QLE Agent, EDI Agent.
Results
Enrollment error rate −74%; QLE turnaround −52%; carrier rejection rate −68%; broker support tickets −44%.
Timeline: Build: 10 weeks. Deploy: specialty line pilot. Optimization: 8 weeks pricing graph.
PrimeAxiom automated specialty underwriting operations with AI agents assembling evidence and pricing context—not spreadsheets in isolation.
Core problem
Manual reading, slow quotes, inconsistent pricing.
Full system
Submission Agent, Loss Run Agent, Pricing Agent, Referral Agent.
Results
Quote cycle time −43%; underwriter capacity +31%; referral acceptance +18%; leakage from mispricing −27%.
Timeline: Build: 9 weeks. Deploy: tower phase. Optimization: 10 weeks personalization.
PrimeAxiom automated the full resort guest journey with AI agents coordinating revenue, operations, and recovery as one system.
Core problem
Generic offers, siloed prefs, late recovery.
Full system
Concierge Agent, Upsell Agent, Booking Agent, Recovery Agent.
Results
RevPAR uplift +8.1%; ancillary spend per guest +19%; service recovery incidents resolved pre-checkout +37%; GSS +0.6.
Timeline: Build: 8 weeks. Deploy: property clusters. Optimization: 7 weeks menu libraries.
PrimeAxiom automated group sales and event execution from RFP to final invoice with AI agents bridging sales and ops.
Core problem
Manual RFP, BEO chaos, billing leakage.
Full system
RFP Agent, Space Agent, Catering Agent, BEO Agent, Billing Agent.
Results
RFP response time −46%; BEO error rate −82%; banquet revenue leakage −29%; sales coordinator hours −53%.
Timeline: Build: 7 weeks. Deploy: outlet-by-outlet. Optimization: 6 weeks flight models.
PrimeAxiom connected aviation signals to kitchen and labor automation so AI agents ran F&B like a real-time operations system.
Core problem
Static prep, labor mismatch, waste.
Full system
Flight Agent, Demand Agent, Prep Agent, Labor Agent, Waste Agent.
Results
Food waste −24%; labor cost as % of F&B revenue −1.9 pts; stockouts during spikes −56%; guest satisfaction on F&B +22.