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Logistics & Supply Chain

Supply chains that see problems before they become delays

Demand and ETA forecasting, automated processing of shipping documents and invoices, and operations assistants that answer status questions from live data.

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Use cases

Where AI pays off in Logistics & Supply Chain

01

Forecasting

Demand, capacity and ETA models with monitoring and retraining.

02

Document automation

Bills of lading, invoices and customs paperwork extracted and validated.

03

Operations assistants

Status and exception answers for customers and teams from live systems.

Real-timeEvent pipelines for tracking and alerts
FewerManual touches per shipment
Services

How we help

Machine Learning & Data Science

Predictive models, NLP, vision and MLOps with measurable business lift.

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Data Engineering & Cloud

Pipelines, lakehouses, vector stores and cloud platforms built to scale.

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AI Agents & Automation

Multi-agent systems, copilots and workflow automation that hold up in production.

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What we solve

Problems we solve in Logistics & Supply Chain

Each maps to a reference architecture and to outcomes the industry has documented.

Document intelligence pipeline

Documents keyed in by hand

Invoices, KYC files, claims and contracts arrive as PDFs and photos. People retype them, errors slip through, and the backlog grows every month-end.

How the system works

  1. Ingest PDF / image / email
  2. Classify document type
  3. Extract to a schema
  4. Validate & score confidence
  5. Auto-post or route to review
Multimodal LLMStructured outputsReducto / LlamaParseReview queueERP / LOS integration

Outcome: Documented deployments classify documents in under a second, turn day-long claims backlogs into minutes, and reach 85% no-touch invoice processing within six months with a seven-month payback. DXC with Claude; Vic.ai

Architecture
Analytics copilot on a governed semantic layer

Every question needs an analyst

Leaders wait days for a report, dashboards disagree with each other, and the data team is a queue. Raw data sits in the warehouse without becoming a decision.

How the system works

  1. Raw sources
  2. Lakehouse with partitions & quality checks
  3. Semantic layer of metrics
  4. Text-to-SQL agent
  5. Dashboards & alerts
Iceberg / DeltadbtSnowflake / BigQuery / DatabricksCube / dbt Semantic LayerGenie / Cortex Analyst

Outcome: In dbt's own test, questions answered through a governed semantic layer reached 83% accuracy, against a 16.7% raw-SQL baseline in the data.world benchmark it replicated; vendors report time-to-insight falling by over 99% with 62% adoption within a year. dbt: the semantic layer as the data interface for LLMs

Architecture
Orchestrated multi-agent workflow

Swivel-chair work across systems

Onboarding, reconciliation, procurement and approvals require people to read one system and type into another, with rules that live in someone's head.

How the system works

  1. Trigger (email, form, event)
  2. Orchestrator plans steps
  3. Worker agents read & decide
  4. Confirm risky actions
  5. Update systems & log
LangGraph / Agents SDKMCP tool serversCRM / ERP APIsHuman checkpointsTraces & replay

Outcome: CRM-native agents at a regulated vendor deflected 72% of support cases to self-service and saved 7.5 hours per case handled with AI assistance. Smarsh on Salesforce Agentforce

Architecture

Let's build intelligent systems that drive growth

Tachyon is the engineering partner for teams that need AI in production, not in a deck. Start with a free 60-minute discovery call.

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