Real Problems.
Concrete Results.

The Problem

A mid-size bank was paying a team of 12 analysts 40+ hours/week to review and classify 3,000 loan applications monthly. Error rate was 6.2%. GPT-4 trials failed compliance review — data couldn't leave the perimeter.

The SLM Solution

We trained a 3B parameter SLM on 8 years of the bank's historical loan documents. Deployed on-premise inside their existing VMware cluster. Model classifies, flags risk, and routes — with a human review layer for edge cases.

12-Month Result

Processing time dropped from 48 min to 3.4 min per application. Error rate fell to 0.8%. The team of 12 was redeployed to relationship banking — zero redundancies.

The Problem

Physicians at a 6-hospital network were spending 2.1 hrs/day on documentation. Consumer AI tools were banned by the CISO. An internal Epic plugin attempt stalled for 14 months in compliance review.

The SLM Solution

A 1.3B clinical SLM trained on de-identified notes from the network's own EHR. Deployed air-gapped within each hospital's server room. Integration via Epic SMART-on-FHIR with a fully offline inference endpoint.

12-Month Result

Physicians reclaimed 97 minutes/day on average. Patient throughput increased 22%. The model passed HIPAA audit on first attempt with zero findings. Rolled out to all 6 hospitals in month 9.

The Problem

A third-party logistics firm was absorbing $3.4M/year in penalty charges from delayed shipments. Delay prediction was manual — dispatchers relied on gut feel across 14,000 monthly shipments.

The SLM Solution

An ML pipeline combining a time-series forecasting model with a fine-tuned SLM trained on historical shipment data, carrier performance records, and weather event logs. Deployed as a real-time API inside their TMS.

12-Month Result

Delay prediction accuracy reached 89.3%. Penalty charges dropped to $1.3M — a $2.1M annual saving. The model now processes 14,000 shipments daily at 72ms average latency.

The Problem

A 200-partner firm was turning away contract review work during peak periods. Associates averaged 4.5 hours per commercial contract. Cloud-based legal AI tools failed Bar ethics rules on confidentiality.

The SLM Solution

A 4B parameter SLM trained on the firm's own contract archive and anonymised precedent library. Deployed within their private cloud VPC. Integrated into their document management system via a secure REST API.

12-Month Result

Average review time fell from 4.5 hrs to 47 minutes. Associates now handle 5× more contracts per week. The firm won two new enterprise retainers citing faster turnaround as the deciding factor.