Claude on the platforms you already run
Claude is available inside Amazon Bedrock, Google Cloud Vertex AI, Snowflake Cortex AI, Databricks, and Microsoft Foundry. Bristlecone builds governed, evaluated agents on the platform you already use, grounded in your supply chain data and business definitions.
You need agents that understand your business, stay within agreed limits, and prove their value before they're trusted with more. That's how we build them.
Most agent pilots fail on data, cost, or control, not on the model
We ground agents in a supply chain data model, release nothing that hasn't passed a test set drawn from your own business, cap spend, and let autonomy grow only as results earn it.
Claude, inside the platform you already govern
Claude runs inside the data platforms and clouds many companies already use, so your data doesn't have to move to a new environment. We build on whichever one you run:
- Amazon Bedrock: Claude models through Bedrock.
- Google Cloud Vertex AI: Claude in Model Garden.
- Snowflake Cortex AI: Claude models callable from SQL, and Claude powers Snowflake Intelligence.
- Databricks: Claude through SQL and model endpoints, governed by Unity Catalog.
- Microsoft Foundry: Claude through serverless deployment, and in Microsoft 365 Copilot.
How we put Claude to work
Our agent architecture is model-neutral, and Claude is one of the models we deploy. The work covers six areas:
Find the right use cases
We score AI use cases on P&L impact and feasibility before anything is built, drawing on a supply chain use case catalog across planning, procurement, manufacturing, and logistics.
Ground agents in your data
We use a supply chain common data model and semantic layer, with retrieval over documents and graphs protected by row- and column-level access control.
Build and evaluate agents
Every release is gated on test sets from your own business. Agents run on the platform you already use, and connect through REST, MCP servers, and agent-to-agent protocols.
Govern autonomy
We use a staged approach from advisory to write-back, with financial transactions held at the most cautious levels, and govern agent data access through the platform's own catalog.
Control cost
We set hard consumption ceilings at the gateway and use smaller, distilled models for repeatable traffic where they're cheaper to serve.
Speed up delivery with AI-assisted engineering
Our engineering teams use AI coding assistants, Claude among them, with MCP-based context to speed up migration, modernization, and quality assurance work.
Where governed agents pay back first
Start with high-volume, lower-risk decisions:
- Plan: explaining forecast and inventory changes to planners.
- Source: supplier summaries and market sensing for buyers.
- Make: shift and quality report summaries.
- Deliver: shipment exception triage.
- Finance and administration: invoice and document processing.
Governed by design, capped by default
Agents read through the platform's own catalog and access controls, with row- and column-level security on retrieval. Every release passes an evaluation gate, and autonomy is granted in stages.
Spend is capped at the gateway and reported per outcome. Repeatable traffic can move to smaller models that cost less to serve.
Four steps from data to a governed agent
The same sequence applies on every platform:
- Ground: supply chain data and definitions in your platform's governed tables.
- Govern: semantic layer, access rules, and test sets agreed with the business.
- Activate: a Claude-based agent built on your platform, released only after it passes evaluation.
- Decide: answers and actions inside BI, Teams, or the planning tool, with autonomy granted in stages.
What leaders ask about Claude on the platforms you already run
Practical answers to the questions that often shape the first conversation.
Do we need a separate contract with Anthropic?+
Usually not. Claude is available through Amazon Bedrock, Google Cloud Vertex AI, Snowflake, Databricks, and Microsoft Foundry, so it can often run under the platform contract you already have.
Do you only use Claude?+
No. Our agent architecture is model-neutral, and we choose the model for each use case.
How do you stop agents from going wrong?+
With evaluation gates on every release, autonomy granted in stages, access governed through your platform's catalog, and spend caps.
Does our data have to leave our platform?+
No. We build agents inside the cloud or data platform you already govern.
How do we start?+
With a four-week diagnostic that scores use cases on value and feasibility.