AI That Moves
Business
Forward.

A 12,000-employee logistics operator was processing freight documentation manually across 9 regional hubs, creating bottlenecks that delayed shipments and inflated overhead costs.

Stratum AI conducted a full operational audit, identified high-volume repetitive workflows, and designed an intelligent document processing pipeline using fine-tuned OCR and classification models integrated into their existing TMS.

End-to-end document handling time dropped by 60%. The client reallocated 34 FTE hours per week to higher-value operations, and exception rates fell from 18% to under 3% within the first quarter post-deployment.

A mid-market financial services firm needed to scale their credit analysis function without proportionally growing headcount. Analysts were spending 70% of their time on data aggregation rather than insight generation.

Stratum AI designed and deployed a retrieval-augmented LLM layer that automated the extraction and summarisation of financial filings, news feeds, and internal CRM data, surfacing analyst-ready briefings on demand.

Analyst throughput tripled within 14 weeks of go-live. The team moved from producing 8 reports per analyst per week to 24, enabling the firm to expand coverage of mid-cap opportunities without adding headcount.

A pan-European healthcare network was expanding AI-assisted diagnostics tooling but lacked a governance framework capable of satisfying GDPR, MDR, and local health data regulations across six countries simultaneously.

Stratum AI delivered a comprehensive AI governance programme: risk classification taxonomy, model audit trails, data lineage documentation, and a cross-functional review board structure aligned to EU AI Act requirements.

Full regulatory sign-off was achieved across all six jurisdictions within five months. The framework became the internal standard for all future AI deployments, reducing per-project compliance overhead by an estimated 45%.

A national retail chain with 200+ locations had received competing internal proposals for AI investment across demand forecasting, loss prevention, and personalisation — but lacked the technical clarity to evaluate or prioritise them.

Stratum AI ran a structured discovery sprint across merchandising, supply chain, and digital teams. Each proposed use case was assessed against feasibility, data readiness, and projected ROI using a proprietary prioritisation matrix.

Four high-confidence use cases were identified with a combined annual saving potential of $2.4M. The client avoided $900K in premature vendor spend on low-ROI initiatives and proceeded with a sequenced 18-month AI roadmap.

A European venture-backed SaaS company had accumulated contracts with four overlapping AI platform vendors following a rapid growth phase, resulting in duplicated capabilities, integration friction, and significant unplanned spend.

Stratum AI performed a full vendor landscape audit, mapping capability overlap and actual utilisation against contract value. A consolidated architecture was designed around two core platforms, with a migration and sunset plan for the remainder.

Annual AI platform expenditure was reduced by 40% — a saving of approximately €380K — while technical capability improved through deeper integration with the retained vendors. The consolidation completed in under three months.

A heavy manufacturing group faced internal resistance to an AI transformation programme due to low AI literacy across the workforce. Department heads lacked the vocabulary to engage with proposed AI projects, stalling executive buy-in.

Stratum AI designed and delivered a tiered AI literacy curriculum — from executive briefings to role-specific practitioner tracks — delivered through a blend of live workshops, asynchronous modules, and hands-on simulation exercises.

Over 800 employees completed AI certification across all seniority levels within 90 days. Internal AI project proposals from business units increased by 220% in the six months following the programme, accelerating the company's transformation pipeline.