Neo Mind ai

Case Studies

Strategy decks don’t transform businesses — shipped systems do. These case studies show how we take AI from idea to production: the business problem we start from, the solution we build, and the kind of results this work delivers. Client details are anonymized to protect confidentiality.

If one of these problems looks like yours, let’s talk about what a first engagement would look like.

Banking & FinanceRAG · Knowledge Assistant

Compliance answers in seconds instead of hours for a regional bank

The challenge

Compliance officers and branch staff at a regional bank spent hours each week searching thousands of pages of regulations, internal policies, and product documentation. Answers were inconsistent across branches, and every ambiguous case escalated to an overloaded central compliance team.

What we built

We built a retrieval-augmented knowledge assistant over the bank’s full policy and regulatory corpus — with source citations on every answer, role-based access controls, and deployment inside the bank’s own perimeter. Staff ask questions in plain language and get grounded answers with links to the exact clause.

75%faster policy lookups
94%answer accuracy in audit sampling
2,400+employees using it weekly
  • RAG pipeline
  • Vector search
  • Source citations
  • On-premises deployment

E-commerce & RetailChatbots & Conversational AI

24/7 customer support that resolves two thirds of tickets on its own

The challenge

A fast-growing online retailer was drowning in repetitive support requests — order status, returns, sizing, delivery questions. Response times stretched past 24 hours in peak season, hurting reviews and repeat purchase rates, while agents burned out on copy-paste answers.

What we built

We deployed a conversational AI assistant integrated with the retailer’s order management and returns systems, live on web chat and WhatsApp. The bot resolves routine requests end-to-end, hands off gracefully to human agents with full context, and reports deflection and satisfaction metrics weekly.

64%of tickets resolved without an agent
<1 minmedian first response time
+21%customer satisfaction score
  • Custom LLM assistant
  • Order-system integration
  • WhatsApp & web chat
  • Human hand-off

ManufacturingMachine Learning · Forecasting

Demand forecasting that cut waste for a food producer

The challenge

A food manufacturer planned production from spreadsheet forecasts built on last year’s numbers. Short shelf lives meant over-forecasting turned directly into waste, while under-forecasting caused stockouts at key accounts — and planners had no visibility into promotions, weather, or seasonality effects.

What we built

We built time series forecasting models per product family and channel, enriched with promotion calendars, holidays, and weather data, and delivered them through a planning dashboard the team already knew. Automated retraining keeps accuracy stable as demand patterns shift.

-32%forecast error (vs. spreadsheets)
-19%production waste
98.2%service level at key accounts
  • Time series forecasting
  • Feature engineering
  • Planning dashboard
  • Automated retraining

LogisticsAgentic AI · Automation

Invoice processing on autopilot for a freight operator

The challenge

A freight operator processed tens of thousands of supplier invoices a month, arriving as PDFs and emails in every imaginable format. A back-office team keyed them manually into the ERP, matched them against contracts, and chased discrepancies — a slow, error-prone process that delayed payments and strained supplier relationships.

What we built

We built an agentic workflow that reads incoming invoices, extracts and validates line items against contracts and rate cards, posts clean invoices straight to the ERP, and routes only genuine exceptions to humans — each with a full audit trail and approval gates on high-value cases.

87%of invoices processed touchlessly
6×faster invoice-to-payment cycle
-71%processing cost per invoice
  • Agentic workflow
  • Document extraction
  • ERP integration
  • Audit trails & approvals

Field ServicesOptimization & Decision Science

Smarter routing and scheduling for a national service fleet

The challenge

A field-services company dispatched hundreds of technicians daily using regional planners and gut feel. Travel time ate productive hours, urgent jobs bumped schedules unpredictably, and SLA penalties were mounting — but every attempted “simple fix” broke some real-world constraint.

What we built

We modeled the full problem — skills, time windows, parts availability, working-hour rules — and built a route and schedule optimizer that produces daily plans in minutes, with live re-optimization when urgent jobs arrive. Planners keep override control and see the cost of every manual change.

-18%kilometers driven per job
+2.1extra jobs per technician per week
96%SLA compliance (from 81%)
  • Route optimization
  • Constraint modeling
  • Live re-optimization
  • Planner dashboard

Telecom & SubscriptionsMachine Learning · Churn Prediction

Keeping subscribers before they decide to leave

The challenge

A subscription business only discovered churn after the cancellation email arrived. Retention offers were blasted to everyone — expensive for customers who were never leaving, and too late for the ones who were. Marketing needed to know who was at risk, and why, while there was still time to act.

What we built

We built churn prediction models on usage, billing, and support-contact signals, scored the full customer base weekly, and wired the scores into the CRM so retention teams see at-risk customers with the top drivers behind each score — enabling targeted, explainable interventions instead of blanket discounts.

-26%churn in targeted segments
3.4×ROI on retention campaign spend
85%of churners flagged 30+ days early
  • Churn modeling
  • Customer scoring
  • CRM integration
  • Explainable drivers

Looking for a problem closer to yours?

These are a sample, not a boundary. The same disciplined path — discover, design, build, validate, deploy — works wherever there is data and a decision worth improving. Tell us about your challenge and we’ll show you what a first engagement would look like, with success metrics defined before any work begins.