Acsenix — Selected Work

Proof,
not promises.

Five engagements where AI replaced manual operations and the numbers moved. Real systems, real data, real production deployments — each one owned outright by the client at handoff.

Scroll through the work
01/ 05
Financial Services

Meridian Capital Group

14d → 4hinvestor onboarding cycle

Onboarding that took two weeks now closes before lunch.

1The Challenge

Investor onboarding stalled for days — manual KYC checks, document chasing, and signature follow-ups across three disconnected systems.

2What We Built

An onboarding agent that ingests documents, runs KYC validation, captures 200+ compliance variables, and escalates to a human only when an exception actually exists.

3The Outcome

Onboarding compressed from 14 days to 4 hours, with a complete audit trail on every automated action — and $180k in operational waste surfaced during discovery.

200+

compliance variables captured automatically

$180k

annual waste found in discovery

Workflow AutomationDocument AICompliance Routing

Laila Ostrovsky

Head of Operations, Meridian Capital Group

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02/ 05
Healthcare & Life Sciences

Northpoint Clinical Group

5d → same-dayprior authorization turnaround

Prior auth used to delay care by days. Now it clears the same morning.

1The Challenge

Prior authorization added 3–5 days to every patient's treatment timeline, and clinicians lost two hours a day to documentation that wrote itself badly.

2What We Built

A prior-auth automation layer on top of the existing EHR — assembling payer packets, drafting clinical notes for physician sign-off, and tracking every claim to resolution.

3The Outcome

Authorization turnaround dropped from five days to same-day, physicians reclaimed two hours daily, and first-pass approval rates climbed to 87%.

2 hrs/day

of physician time returned

87%

first-pass approval rate

Prior Auth AutomationAmbient DocumentationClaims AI

Dr. Elena Vasquez

Chief Medical Officer, Northpoint Clinical Group

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03/ 05
E-Commerce & Retail

Volta Retail

$2.3Min annual stockouts eliminated

They were losing revenue they couldn't even see coming.

1The Challenge

Demand forecasting lived in spreadsheets. Stockouts and overstock were discovered after the fact — and a growing support queue was outpacing headcount.

2What We Built

Autonomous reorder agents that monitor sell-through velocity, supplier lead times, and warehouse capacity — triggering purchase orders for standard SKUs with zero human approval.

3The Outcome

$2.3M in annual stockouts eliminated, cart abandonment down 43%, and a replenishment engine that reorders before the team would have noticed the gap.

43%

reduction in cart abandonment

0

manual reorders for standard SKUs

AI AgentsDemand ForecastingProcurement Automation

Marcus Delacroix

VP of Supply Chain, Volta Retail

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04/ 05
Logistics & Supply Chain

Cardinal Freight Systems

31%fewer late deliveries

Exceptions used to cascade before anyone could respond.

1The Challenge

Carrier exceptions were handled by hand — delays rippled across the network before an operator ever saw them, and supplier onboarding dragged past three weeks.

2What We Built

An exception-management system that watches every shipment in real time, predicts at-risk deliveries, and acts on them automatically — plus AI document review that compresses supplier onboarding.

3The Outcome

Late delivery incidents fell 31%, four hours were cut from the average fulfillment cycle, and eight manual handoffs were removed from the workflow entirely.

4 hrs

cut from average fulfillment cycle

8

manual handoffs eliminated

Exception ManagementRoute OptimizationDocument AI

Tomas Reynard

Director of Operations, Cardinal Freight Systems

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05/ 05
SaaS & Technology

Pylon Software

9d → 3donboarding time-to-value

Slow onboarding was quietly churning their best customers.

1The Challenge

New customers needed days of manual setup before seeing value, and support volume grew faster than the team could hire — eroding margin with every new account.

2What We Built

An AI-guided setup flow that configures itself from the customer's data, plus a support copilot that resolves common tickets and routes the rest with full context.

3The Outcome

Time-to-value dropped from nine days to three, month-one churn fell 38%, and onboarding ran 3.1× faster without adding a single headcount.

38%

lower month-one churn

3.1×

faster onboarding to value

AI CopilotOnboarding IntelligenceSupport Automation

Aria Chen

VP of Product, Pylon Software

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