Real Results for
Real Enterprises

The Challenge

A Tier 1 automotive parts manufacturer with 2,400 employees across three facilities was experiencing significant inefficiency in quality inspection, production scheduling, and supplier communication. Manual processes consumed an estimated 18,000 staff hours per month, and error rates in incoming goods inspection were running at 3.2% — well above industry benchmarks.

The operations team had piloted several AI tools independently, but without a cohesive strategy, the tools created data silos and inconsistent outcomes. Leadership wanted a unified AI workflow strategy before committing to further technology investment.

Our Approach

ALMINO AI conducted a four-week workflow audit across all three facilities, mapping every process step that consumed more than 100 staff hours monthly. We identified seven high-impact automation opportunities and prioritized three for Phase 1 implementation.

Phase 1 targeted incoming goods inspection (vision AI integration), production scheduling optimization (LLM-assisted parameter tuning), and supplier document processing (open-source LLM with RAG over specification documents). We advised on vendor selection, provided architecture review for the engineering team, and ran monthly strategy sessions throughout a 9-month retainer.

Outcomes

The production scheduling system reduced changeover planning time from 4 hours to under 35 minutes. The supplier document AI eliminated a 3-person manual review team, redeployed to higher-value QA functions. Phase 2 roadmap is currently in planning.

The Challenge

A mid-size regional bank with $18 billion in assets under management was processing approximately 9,000 loan application documents per month. The extraction and review process required a team of 14 junior analysts working in rotating shifts, with a typical review cycle of 3–5 business days per application.

The bank's compliance and IT security teams had rejected proposals to use proprietary cloud LLMs (GPT-4, Claude) due to strict data residency and regulatory requirements. The requirement was explicit: all processing must occur on-premises, with no data leaving the internal network perimeter.

Our Approach

ALMINO AI led a 12-week open-source LLM evaluation specifically filtered for the bank's constraints: on-premise deployment, document extraction accuracy, and inference cost within budget. We evaluated Llama 3.1 70B, Qwen2.5-72B, and Mistral Large against a benchmark set of 500 historical loan documents (anonymized).

Llama 3.1 70B was selected for its strong English document comprehension and extraction accuracy. We designed a structured extraction pipeline with a confidence scoring system — high-confidence documents routed directly to senior review, low-confidence flagged for junior analyst attention. We also advised on the GPU infrastructure spec and ran the model serving architecture review with the bank's internal engineering team.

Outcomes

Average review cycle dropped from 4.1 days to 1.1 days. The 14-person analyst team was restructured: 8 redeployed to complex case review, 6 transitioned to a newly created AI quality oversight function. Annual infrastructure ROI breakeven at 14 months.

The Challenge

A nationwide retail chain operating 340 stores across the US was handling 85,000 customer service inquiries per month through a combination of phone, email, and web chat. Contact center staffing costs had grown 22% year-over-year as inquiry volume increased, and first-contact resolution rates had fallen to 61% — well below the 75% target set by the customer experience team.

A previous chatbot implementation (rule-based, deployed 2021) had been effectively abandoned by customers due to its inability to handle anything beyond the simplest FAQ queries. Leadership was skeptical of AI investment but under significant pressure to reduce operating costs without degrading the customer experience.

Our Approach

We began with a 3-week inquiry analysis — categorizing all contact center tickets over a 6-month period by type, resolution complexity, and handling time. This revealed that 67% of inquiries fell into 12 repeatable categories that a well-designed AI agent could resolve autonomously with high confidence.

We designed a tiered AI agent architecture: Tier 1 (fully autonomous for routine inquiries — order status, return eligibility, store hours), Tier 2 (AI-assisted with human in the loop for exceptions and escalations), and Tier 3 (direct human handling for complaints and high-value customers). We advised on agent tool design (connecting to order management, inventory, and CRM APIs), knowledge base structure, and the escalation protocol that preserved customer trust through seamless handoffs.

Outcomes

Contact center headcount reduced by 18% through natural attrition over 12 months, with no forced redundancies. Customer satisfaction (CSAT) score improved from 3.8 to 4.3/5.0. The AI agent now handles 49,300 inquiries per month autonomously — 24 hours a day, 7 days a week.