| 945d5a91cfbc6255… | Section 5 presents a requirement-by-requirement crosswalk from the Summer Fridays RFP that compares a Fabric-native approach with a Pyrana-on-Fabric approach to support a compliant and differentiated vendor response. | active | contextual | yes | |
| 78dd6cc2829b5c02… | Section 6 frames the core decision as whether to build a governance layer internally or buy Pyrana, with the evaluation depending on what governance capabilities are needed and how long implementation would take. | active | contextual | yes | |
| fa814260d1e5332f… | For ingest, transform, and refresh in the Fabric-native approach, the document specifies Fabric pipelines plus Purview as the mechanism. | active | contextual | yes | |
| e01ff387ea419317… | For the requirement of where data is stored, the Fabric-native approach uses governed OneLake, while the recommended Pyrana-on-Fabric approach leaves OneLake unchanged as the store and keeps Pyrana's knowledge graph limited to cited claims and provenance within the customer's Azure tenant. | active | contextual | yes | |
| 76dbad23636f164b… | The RFP requires IT to have visibility and approval over data storage, ingestion, transformation, refresh, security, access, sensitive-data protection, production deployment changes, and the governance, testing, and monitoring of AI agents so that enterprise oversight is maintained. | active | contextual | yes | |
| b01461470e498f51… | The requirement-by-requirement crosswalk presents every requirement category from the Summer Fridays RFP with the Fabric-native approach and the Pyrana-on-Fabric approach shown side by side so that it serves as the backbone of a compliant, differentiated vendor response. | active | contextual | yes | |
| 3b56342857d3bfdf… | The build-versus-buy decision is framed around governance ownership and delivery time rather than ultimate capability, and this decision is addressed in Section 6. | active | contextual | yes | |
| 58ead10d6058b6dc… | The middle-column option of Fabric plus Foundry plus custom build can achieve the same end state as Pyrana because Microsoft supplies capable building blocks, but the difference lies in who builds, hardens, and maintains the governance layer and how long that takes. | active | contextual | yes | |
| 52229a6ef25c599f… | The stated Fabric Data Agent limitations on unstructured grounding and observability are tied to Microsoft's documented 2026 capabilities and are expected to continue evolving over time. | active | contextual | yes | |
| 688d692b23478caa… | The comparison legend defines the scoring symbols so that readers interpret check marks as provided or strong, triangles as partial or build-it-yourself, and crosses as not provided. | active | contextual | yes | |
| 6ae6cf15a9aea275… | For time to production of governed agents, Fabric Data Agents are fast but thin, Fabric plus Foundry is the longest because the team builds it, and Pyrana on Fabric can reach production in weeks with pre-governed capabilities. | active | contextual | yes | |
| 90eb4cc74797ba16… | All three approaches stay in Azure and honor the Fabric mandate, with Pyrana on Fabric specifically described as Azure-native and able to read OneLake. | active | contextual | yes | |
| fdad15ff333ac03e… | For a custom business-facing agent UI, Fabric Data Agents do not provide the capability because Power BI is BI, Fabric plus Foundry requires building a custom app, and Pyrana on Fabric offers UI kits and an SDK that can deliver it in weeks. | active | contextual | yes | |
| e988c483965ba2f1… | For prompt-injection and misuse protection, Fabric Data Agents offer platform-level partial support, Fabric plus Foundry requires implementation by the team, and Pyrana on Fabric includes the protection within its governance layer. | active | contextual | yes | |
| 27ca1bf346e1f925… | For agent-level observability and evaluation, Fabric Data Agents currently have a gap, Fabric plus Foundry depends on conventions the team enforces, and Pyrana on Fabric provides observability through OTel, Langfuse, and MLflow. | active | contextual | yes | |
| 50865f82f3428a48… | For deterministic claim checks under the principle 'validate, don't invent,' Fabric Data Agents do not provide the capability, Fabric plus Foundry requires teams to assemble it, and Pyrana on Fabric provides a generate-then-validate approach. | active | contextual | yes | |
| eec1107fe334ee58… | For fact-versus-recommendation-versus-assumption typing, Fabric Data Agents do not provide the capability, Fabric plus Foundry requires custom guardrails, and Pyrana on Fabric provides knowledge-type plus confidence labeling. | active | contextual | yes | |
| e76434b0e7c64930… | For source-to-AI citation on every answer, Fabric Data Agents provide only partial support, Fabric plus Foundry requires teams to design the mechanism themselves, and Pyrana on Fabric logs Context Unit IDs to provide the capability. | active | contextual | yes | |
| c1d0fcdfc16318e1… | For human-in-the-loop approval gates, Fabric Data Agents do not provide support, Fabric plus Foundry requires custom implementation, and Pyrana on Fabric provides native support. | active | contextual | yes | |
| fb493ad208408733… | For durable multi-step, multi-agent workflows, Fabric Data Agents do not provide the capability, Fabric plus Foundry requires orchestration through Agent Framework, and Pyrana on Fabric provides it through Harness plus Temporal. | active | contextual | yes | |
| 4b47343356956676… | For grounding on unstructured documents such as PDFs, Word files, and decks, Fabric Data Agents do not provide native support, Fabric plus Foundry requires teams to build retrieval themselves, and Pyrana on Fabric provides the capability through its Context Engine. | active | contextual | yes | |
| 3479afe1d635d83d… | For governed Q&A over structured data, Fabric Data Agents, Fabric plus Foundry plus custom build, and Pyrana on Fabric all support the capability, with Pyrana specifically reading the Fabric model. | active | contextual | yes | |
| 5a1d0369131bc78f… | The head-to-head comparison matrix evaluates agentic-application capabilities needed by the project, while Fabric's data and BI strengths are treated as already established and discussed separately in Section 7. | active | contextual | yes | |
| 9caf50152046f0a3… | The architecture applies a security rail across all layers using Entra ID identity, Azure Key Vault, AES-256 encryption at rest, TLS 1.2 or higher in transit, private networking, and per-tenant isolation so that it remains consistent with Pyrana's Azure production architecture. | active | contextual | yes | |
| c0809e3fd344f32b… | Pyrana provides unstructured-document grounding coverage that Fabric Data Agents do not natively provide, enabling cited retrieval from documents beyond structured BI data. | active | contextual | yes | |
| 08502f0ff32e09fc… | The knowledge path processes unstructured materials such as recap documents, launch or marketing calendars, claim libraries, and brand-health or competitive PDFs through the Pyrana Context Engine so that agents can retrieve atomic cited Context Units. | active | contextual | yes | |
| b44cbbe1d5f6bb9b… | For ingestion, transformation, and refresh, the Fabric-native approach uses Fabric pipelines together with Purview, indicating that these governance and data-movement functions remain in the Fabric stack. | active | contextual | yes | |
| 892b927d9db8ba56… | For the requirement of where data is stored, the Fabric-native approach uses governed OneLake, while the recommended Pyrana-on-Fabric approach leaves OneLake unchanged and stores only cited claims and provenance in Pyrana’s knowledge graph within the customer’s Azure tenant. | active | contextual | yes | |
| f0fd9e5c2e4768ab… | The Platform and IT visibility requirement states that IT must have visibility and approval over data storage, ingestion, transformation, refresh, security, access, sensitive-data protection, production deployment changes, and AI-agent governance, testing, and monitoring. | active | contextual | yes | |
| de1048db20594ce1… | The requirement-by-requirement crosswalk presents every requirement category from the Summer Fridays RFP with Fabric-native and Pyrana-on-Fabric approaches side by side so that the response can be compliant and differentiated. | active | contextual | yes | |
| 57c32924c355681c… | Section 6 is identified as the place where the build-versus-buy decision is addressed, linking the capability comparison to procurement strategy. | active | contextual | yes | |
| bc7f30fe5411542d… | The middle-column option of Fabric plus Foundry plus custom build can achieve the same end state as Pyrana, but the difference is who builds, hardens, and maintains the governance layer and how long that work takes. | active | contextual | yes | |
| 9a57682a4949402d… | The comparison legend defines the symbols so that readers interpret ✓ as provided or strong, △ as partial or build-it-yourself, and ✕ as not provided. | active | contextual | yes | |
| 7cdc411e2bb363f0… | For governed agents, Fabric Data Agents offer fast but thin time-to-production, custom Microsoft builds take the longest because the customer builds them, and Pyrana reaches production in weeks with pre-governed capabilities. | active | contextual | yes | |
| 19b15d026b027233… | All three approaches stay in Azure and honor the Fabric mandate, with Pyrana specifically described as Azure-native and able to read OneLake. | active | contextual | yes | |
| 343fd4b681782048… | A custom business-facing agent UI is not provided by Fabric Data Agents because Power BI is positioned as BI, while a Microsoft custom build can create one and Pyrana offers UI kits and an SDK that reduce delivery to weeks. | active | contextual | yes | |
| 6aa17a7d3a4bb6a9… | Prompt-injection and misuse protection are only platform-level in Fabric Data Agents, must be implemented by the customer in a Microsoft custom build, and are built into Pyrana governance. | active | contextual | yes | |
| 86f8ba1594ee4425… | Agent-level observability and evaluation are a current gap in Fabric Data Agents, require assembled conventions in a Microsoft build, and are supported in Pyrana with OTel, Langfuse, and MLflow. | active | contextual | yes | |
| 6879b49ea5d33ce3… | Deterministic claim checking under the principle 'Validate, don't invent' is not provided by Fabric Data Agents, requires custom implementation in a Microsoft build, and is supported in Pyrana through a generate-then-validate approach. | active | contextual | yes | |
| d30ccee528a9e63f… | Fact-versus-recommendation-versus-assumption typing is not provided by Fabric Data Agents, requires custom guardrails in a Microsoft build, and is supported in Pyrana with knowledge-type and confidence metadata. | active | contextual | yes | |
| b9ad01ed482436c9… | Source-to-AI citation on every answer is only partial in Fabric Data Agents, must be designed manually in a custom Microsoft stack, and is supported in Pyrana through logged Context Unit IDs. | active | contextual | yes | |
| ed987e77910f7441… | Human-in-the-loop approval gates are not available in Fabric Data Agents, require custom implementation in a Microsoft build, and are natively available in Pyrana on Fabric. | active | contextual | yes | |
| accfb6c3a64ea251… | Durable multi-step, multi-agent workflows are not provided by Fabric Data Agents, require orchestration through Agent Framework in a custom Microsoft build, and are natively supported by Pyrana through Harness and Temporal. | active | contextual | yes | |
| 55adab49c7caa50f… | Fabric Data Agents do not natively ground answers on unstructured documents such as PDFs, Word files, or decks, whereas Pyrana on Fabric provides this through its Context Engine and custom Microsoft stacks require self-built retrieval. | active | contextual | yes | |
| a35f10ad095c4eba… | Fabric Data Agents natively provide governed Q&A over structured data, while Fabric plus custom build and Pyrana on Fabric can also support that capability, with Pyrana reading the Fabric model. | active | contextual | yes | |
| 24c035dfa2fcc7de… | The head-to-head matrix evaluates agentic-application capabilities needed by the project, while Fabric’s data and BI strengths are treated as already established elsewhere in the document. | active | contextual | yes | |
| 3d447731be0c00ae… | The system applies a security rail across all layers using Entra ID, Azure Key Vault, AES-256 at rest, TLS 1.2+ in transit, private networking, and per-tenant isolation so that it remains consistent with Pyrana’s Azure production architecture. | active | contextual | yes | |
| b6e22ef9077101ec… | Pyrana Context Engine extracts atomic, cited Context Units from unstructured materials so that agents can retrieve and cite document-grounded knowledge with coverage Fabric Data Agents do not natively provide. | active | contextual | yes | |
| 06098f5f3d02b684… | Fabric pipelines load source data into OneLake or a warehouse and then into a governed semantic model so that Power BI dashboards and Pyrana agents can use the same governed foundation. | active | contextual | yes | |
| 1c25fb1d7fa6f613… | The architecture separates a structured data path from an unstructured knowledge path so that governed analytics and document-grounded agent behavior can coexist within one system design. | active | contextual | yes | |