Databricks Logistics Global

Altana

Document Processing · Enterprise Knowledge Search · Risk Management

Altana needed a better way to support generative AI trade classification, legal reasoning, and supply chain knowledge graph enrichment because the company needed to deploy many customer-specific models quickly without wasting time on boilerplate infrastructure, vendor lock-in, or fragmented evaluation tooling.

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20x solving global trade challenges across the supply chain

20–50% faster deployment of genai models into production

Deploying GenAI models 20x faster with up to 50% improved performance

Reported impact

Business outcome

Altana improved generative AI trade classification, legal reasoning, and supply chain knowledge graph enrichment with measurable results including 20x solving global trade challenges across the supply chain, 20–50% faster deployment of genai models into production, and Deploying GenAI models 20x faster with up to 50% improved performance, while giving teams a faster path from governed data to production decisions.

Solution

Altana uses Databricks Data + AI Platform to support generative AI trade classification, legal reasoning, and supply chain knowledge graph enrichment. The implementation replaced a legacy state where the company needed to deploy many customer-specific models quickly without wasting time on boilerplate infrastructure, vendor lock-in, or fragmented evaluation tooling. Global trade data and customer supply chain data move through production data pipelines and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog governs access, lineage, and compliance across sensitive datasets. MLflow, Model Serving, and Agent Bricks operationalize models, agents, and analytics that deliver tariff classifications and legal write-ups.

Data flow

Global trade data and customer supply chain data land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver tariff classifications and legal write-ups.

Solution architecture

6 components · 4 layers

Compute
- Databricks Data + AI Platform Databricks Data + AI Platform unifies engineering, analytics, governance, and AI workloads for generative AI trade classification, legal reasoning, and supply chain knowledge graph enrichment.
- MLflow MLflow tracks experiments, versions, and release workflows for models that support generative AI trade classification, legal reasoning, and supply chain knowledge graph enrichment.
Storage
- Delta Lake Delta Lake stores curated global trade data and customer supply chain data so every team works from one trusted data foundation.
Serving
- Model Serving Model Serving exposes real-time predictions and AI outputs to tariff classifications and legal write-ups.
- Agent Bricks Agent Bricks packages domain-specific agents that automate parts of generative AI trade classification, legal reasoning, and supply chain knowledge graph enrichment.
Governance
- Unity Catalog Unity Catalog applies centralized access controls, lineage, and auditability across sensitive datasets.

Architecture clues

20x solving global trade challenges across the supply chain
20–50% faster deployment of genai models into production
Centralized governance, lineage, and access controls are enforced through Unity Catalog.
Delta Lake provides the shared storage layer for curated lakehouse datasets.
Operational AI workflows are deployed directly beside governed data and analytics assets.

Evidence from the source

A key improvement for us was the model and LLM evaluation tools in MLflow, combined with Model Serving. These have allowed our ML teams to rapidly train, test and deploy their own models for customers.

The ‘batteries included’ nature of Databricks allows us to do it all in one place and collaborate across our data ingest, data engineering, ML and software teams,

These models generate what is the right tax and tariff classification of this good and then auto-generates an entire legal write-up that a customer can use for justification purposes,

We’re using Databricks to keep track of all the user input/output decisions so that we can identify which steps and models in the workflow need fine-tuning for cost, performance or latency reasons.

Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
3 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence89%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
4 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Databricks
Altana customer story

First-party
https://www.databricks.com/customers/altana-ai

Source type
Vendor-published customer story

Published
Aug 7, 2024
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date

Date evidence

Published on source page Aug 7, 2024 · Databricks · First-party
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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