AI Strategy. Tailored for the Mid-Market.

High call volume overwhelming support teams, leading to long wait times and inconsistent responses.

Deploy an LLM-based conversational agent trained on product documentation and past support tickets to handle tier-1 queries autonomously.

Demonstrate 60% reduction in tier-1 ticket volume and shorten average response time from 4-6 hours to under 10 minutes.

Generic email campaigns generating low open rates and poor conversion.

Build a propensity model using CRM and behavioural data to segment customers and dynamically tailor messaging.

Opportunity to improve email click-through rate by 3-4x, and ensure double-digit uplift in campaign-attributed revenue.

Chronic over-stocking of slow-moving SKUs alongside frequent stockouts on fast movers.

Train a time-series forecasting model on sales history, seasonality, and external signals to drive automated replenishment recommendations.

Drive reduction in carrying costs by 30%; and 10-20% improvement in service-level agreement compliance

Manual supplier audits missed early warning signals, resulting in unexpected disruptions.

Continuously ingest news feeds, financial data, and logistics signals through an NLP pipeline to surface supplier risk scores in real time.

Proactively identify critical supplier issues before they impact production; cut audit cycle time by half.

Unplanned downtime costing millions per year due to reactive maintenance schedules.

Deploy IoT sensor telemetry into an anomaly-detection model to predict failures 72 hours in advance and auto-schedule maintenance crews.

Unplanned downtime could potentially be reduced by nearly 45%, with maintenance cost per unit decreasing by a quarter.

Manual visual inspection on the production line was slow, inconsistent, and missing 12% of defects.

Train a computer vision model on labelled defect images and integrate it into the conveyor camera system for real-time pass/fail classification.

Dramatic improvement in defect escape rate can be achieved (Six Sigma Quality) while simultaneously increasing inspection throughput by 5x.

AP team spending 60% of their time manually keying invoice data, leading to payment delays and errors.

Implement an OCR and LLM extraction pipeline to capture, validate, and route invoices automatically against PO data.

Invoice processing time reduced from 5 days to 4 hours; error rate dropped by 85%.

Month-end close process surfacing fraud and errors too late to act on.

Build a continuous transaction monitoring model that flags statistical anomalies and routes alerts to finance controllers for same-day review.

Eliminate potential fraud; shorten close cycle by several days.

Talent acquisition team overwhelmed by application volume, with inconsistent shortlisting criteria across hiring managers.

Implement an LLM-based screening tool that evaluates CVs against structured job criteria and produces ranked candidate summaries for hiring managers.

Cut time-to-shortlist by half; improve hiring manager satisfaction scores significantly.

High attrition in key roles with no early warning system — exits only discovered at resignation.

Aggregate engagement survey data, pulse feedback, and anonymised HR signals into a retention-risk model that surfaces at-risk employees to people managers.

Shift HR team from reactive to proactive intervention; reduce voluntary attrition within 6 months by 10%-15%.