Build the foundation before the copilot
Bristlecone builds Google Cloud data platforms for SAP-heavy manufacturers: landing zones, BigQuery data lakes with medallion design, and data feeds to planning platforms, with governance and cost alerts in place from day one.
There's a lot of excitement about AI on Google Cloud, and for good reason. But the companies that get value fastest are the ones whose data is ready first. That's the work we do.
AI pilots move fast. Unready data slows them down.
What slows them down is data that isn't ready: SAP extracts without business meaning, planning data that arrives late, and no clear owner for cloud spend.
We build decision-grade data foundations on Google Cloud first, prove value on one focused use case, then scale in stages.
Already running on Google Cloud? Get more from it
If you already have a Google Cloud environment, we can strengthen the landing zone and security posture, organize SAP and non-SAP data into BigQuery layers the business can trust, set up cost alerts, and connect the data to the planning and analytics tools your teams use.
Standing you can check
- Leader in the IDC MarketScape: Worldwide Supply Chain, SAP & All Other Ecosystem Services, 2025-2026.
- Supply chain focus since 1998, with 1,000+ customer engagements.
- Part of the Mahindra Group.
What we do on Google Cloud
Assess and plan
Readiness and cost before you build: a data readiness assessment across SAP, BW, and Google Cloud, feasibility and Google Cloud TCO scoring for each use case, and cloud migration planning.
Land and migrate
A secure landing zone and a clean move, with Terraform-based landing zones and CI/CD, and SAP BW on HANA data moved into a BigQuery data lake.
Build the BigQuery data platform
A medallion design with SAP and non-SAP sources: Cloud Storage landing with bronze, silver, and gold layers in BigQuery, SAP extraction with SAP-certified integration tools, Salesforce and dealer system sources, and subject models for warranty, demand planning, finance, spend, sales and service, HR, and quality.
Govern and secure
Controls configured from the start: cloud security posture management to CIS benchmarks, access control, logging, monitoring, a data catalog, and automated lineage, alerting, and data health dashboards.
Feed planning and analytics
Data that reaches the people who decide, including feeds to Kinaxis planning and BI on BigQuery with Looker and Qlik.
Operate and optimize
Uptime and spend under control, with budget and utilization alerts and infrastructure run to defined availability and recovery objectives.
SAP data on BigQuery, feeding the planners
Our reference build on Google Cloud takes SAP ERP and BW data, lands it with SAP-certified integration, structures it in BigQuery medallion layers, and sends planning-ready data to Kinaxis. Sources such as SAP, Salesforce, and dealer and registration systems land in Cloud Storage, are refined through bronze, silver, and gold layers in BigQuery, and are served through subject models and BI, with planning data written back to Kinaxis.
Accelerators built from delivery
- Prebuilt KPI dashboards for spend, inventory, cost to serve, S&OP, warehouse, and logistics.
- Edge Data Readiness Accelerator: metadata-driven integration, used to move BW on HANA data into the lake.
- Assessment frameworks: enterprise data assessment and data quality frameworks.
What a BigQuery foundation lets you do next
- Plan: forecast accuracy improvement and planning data to Kinaxis.
- Source: spend analytics across entities.
- Make: quality and warranty analytics.
- Deliver: logistics spend analytics on BigQuery.
- Service: AIOps for SAP L1 and L2 support.
Secure by design. Predictable to run.
Landing zones are built with Terraform, security posture is monitored and configured to CIS benchmarks, and access control, logging, and cataloging are part of the data platform design.
Budget and utilization alerts are configured at setup, and every use case is scored on Google Cloud TCO before it's built.
From decision-grade data to AI
Our Google Cloud AI path runs in four gated phases:
- Foundations: data readiness, governance, and BigQuery medallion layers.
- Business case: use cases scored on value and Google Cloud TCO.
- Proof of value: one focused use case, with Vertex AI MLOps and explanations grounded in Gemini.
- Scale: expand in phases once the proof holds.
What leaders ask about Build the foundation before the copilot
Practical answers to the questions that often shape the first conversation.
Can you bring SAP data into BigQuery?+
Yes. We've moved SAP BW on HANA data into a BigQuery data lake and feed planning systems from it.
Do you run Google Cloud platforms after go-live?+
Yes, to defined availability and recovery objectives.
Do we need to fix our data before starting an AI pilot on Google Cloud?+
You don't need perfect data, but you do need data that's ready for the use case you're proving. We assess readiness first, so the pilot isn't slowed down by problems that could have been spotted early.
Do you only work on Google Cloud?+
No. We also deliver on Snowflake, Databricks, AWS, and Microsoft Azure.
How do we start?+
With a data readiness assessment, then a focused proof of value.