Two flagships.
One honest ecosystem.
We lead with two platforms — Microsoft Fabric and Databricks — where we hold partner-grade certifications and ship reference architectures. Around them, we engage pragmatically with the rest of the modern data and AI ecosystem: deep enough to integrate, ingest, and deliver against your requirement; honest about where we don't go further than that.
— Where we lead, we lead deep. Where we don't, we'll tell you.
Microsoft Fabric
For Microsoft-stack enterprises — Office 365, Dynamics, Azure-led IT. Fabric collapses the lakehouse, the warehouse, and Power BI into one surface. We build production-grade Fabric estates with FlexiContext sitting on top — and our partners can debug a Spark UI, tune a Direct Lake semantic model, and read an XMLA trace.
Databricks
For data-engineering-led organizations and where ML / agentic workloads dominate. Unity Catalog gives us a real semantic boundary; Mosaic AI gives us a real path from notebook to production model. We build production-grade Lakehouses with FlexiContext expressed in Unity Catalog metadata — and we maintain the toolchain end-to-end.
Real enterprises run heterogeneous stacks. We work with the tools below the way a good plumber works with a building you didn't design — deep enough to do the job right, without pretending to be the world expert in someone else's product.
Snowflake · BigQuery · Redshift · Synapse
We extract from, load into, and query against these. Strong on modeling, role-based access, share/marketplace boundaries — enough to integrate FlexiContext as a reader/writer.
SAP · Oracle EBS · Workday · Dynamics
We understand SAP transports, master data, and extraction surfaces deeply enough to get the data out reliably for analytics and AI. We don't implement S/4 — we partner with firms that do.
Azure · AWS · GCP
We deploy inside your cloud, your VNets, your KMS keys. Comfortable across all three for the data + AI control plane; we let your cloud team own the rest of the cloud.
Power BI · Tableau · Qlik · Looker
Power BI is where we go deepest (semantic models, RLS, Direct Lake). We deliver to Tableau, Qlik, Looker too — semantic-layer-first, dashboard-second, so the model survives the tool.
Fivetran · Airbyte · Informatica · ADF
We use what's already in your stack. We won't sell you a new ELT tool unless the audit finds your current one is structurally wrong — in which case we'll say so directly.
OpenAI · Azure OpenAI · Anthropic · Bedrock · Vertex
Model-agnostic by design. FlexiContext sits between your data and any frontier model — we'll pick the right one for the task, and re-pick when the frontier moves.
Purview · Unity · Collibra · Alation · Atlan
Catalog is where context lives. We're deepest on Purview and Unity (because of the flagships), competent on the others, and honest about where we'd rather plug in than rebuild.
Airflow · dbt · Dagster · GitHub Actions
Pipelines need owners, not heroes. We ship orchestration with tests, CI/CD, and runbooks — using whichever of these your team will actually maintain after we leave.
pgvector · Azure AI Search · Pinecone · Weaviate
We benchmark before we recommend. For most regulated enterprises, pgvector or Azure AI Search inside your perimeter beats a managed vector DB — but we'll deploy the latter when it's right.
Three tiers, named honestly.
Inside the firm, we say it like this. We're saying it on the website too.
We lead
Reference architectures, certified depth, partner-grade engineering, opinionated points of view. If you ask us "what would you do," we'll tell you in detail and ship it.
We build with
Production-comfortable — we'll model, deploy, and integrate. We won't out-expert the platform's own SI partners on edge cases, and we'll say so. Most of our work touches this tier.
We integrate
Deep enough to get clean, governed data out. We understand the transport layer, the master data, and the gotchas — but we don't implement these systems. For that, we recommend a partner.
Three questions, one platform.
For greenfield builds and major re-platforms, we don't bring a preference into the audit. We ask three questions and let your answers pick.
If your stack is already on Snowflake, BigQuery, or anywhere else and you're not re-platforming — that's fine. We integrate, deliver FlexiContext on top, and respect what you've built.
Four reasons we tier instead of pretending.
Cloud lakehouses move fast
OneLake, Unity Catalog, Mosaic — every quarter has new primitives. Two flagships is the most we can stay current on at partner-grade depth. Eight is theatre.
Most projects need Tier 2 work anyway
Real data lives in SAP, Snowflake, Salesforce, mainframes. Pretending we only do Fabric and Databricks would be useless. Tier 2 is where most of the actual work happens.
FlexiContext is platform-specific in implementation
The framework is platform-agnostic; the implementation is not. Fabric and Databricks are our two reference implementations. On other platforms we adapt the framework — same principles, different mechanics.
Honesty beats a bigger SOW
If your roadmap needs deep S/4 implementation or Snowflake-native engineering at frontier scale, we'll refer you to a partner who's deeper there. Saying so up front earns the trust to win the work we should do.