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Confidential client / Multi-site services

A data and retrieval foundation for complex technical support

For a multi-site services organization, Stakforge integrated data from many systems, built a modern warehouse and vector database, and developed retrieval AI for complex technical-support investigations.

Data ArchitectureSystem IntegrationRetrieval AI
DATA ARCHITECTURE / RETRIEVAL AIConnected information. Relevant context. Human judgment.Illustrative system model / anonymized engagement
Integrated data and semantic retrieval for technical supportIllustrative, anonymized system model. Multiple generic source systems integrate into a data warehouse. Knowledge content is prepared for a vector database. A technical question enters the retrieval path; the vector database returns relevant context, which is considered alongside integrated warehouse context by a support specialist. The diagram does not depict client data, specific products, or measured performance.01 / INTEGRATE02 / MODEL03 / INDEX04 / USESOURCE SYSTEMSSOURCE 01SOURCE 02SOURCE NDATA WAREHOUSEIntegrateModelServeSHARED DATA CONTEXTKNOWLEDGE CONTENTPREPARE + EMBEDVECTOR DATABASESCHEMATIC VECTOR SPACE?TECHNICAL QUESTIONRELEVANT CONTEXTDATA + RETRIEVALSUPPORT SPECIALISTHuman resolutionWarehouse and vector retrieval for technical supportIllustrative, anonymized system model. Multiple generic source systems integrate into a data warehouse. Knowledge content is prepared for a vector database. A technical question enters the retrieval path; the vector database returns relevant context, which is considered alongside integrated warehouse context by a support specialist. The diagram does not depict client data, specific products, or measured performance.SOURCESWAREHOUSEVECTOR DBCONTEXTSOURCE 01SOURCE 02SOURCE NKNOWLEDGEINGESTMODELSERVETO SUPPORTQUESTION / RETRIEVAL
ClientEnterprise support organization
IndustryMulti-site services
EngagementData foundation + retrieval AI
DeliveryArchitecture through implementation

Complex technical issues can depend on information held across many systems. This engagement centered on making that context easier for specialists to find and use during an investigation.

The design question was how to connect enterprise information to the point in the workflow where a specialist needs it, while leaving resolution with the team.

Stakforge treated data architecture, integration, and retrieval as one support problem. The warehouse provides a coherent foundation for information from multiple systems; the vector database and retrieval AI make relevant context easier to find when a complex issue reaches the team.

  1. 01Integrated information from multiple systems into a connected data architecture.
  2. 02Built a modern data warehouse for reusable, cross-system context.
  3. 03Developed a vector database and retrieval AI capability to find issue-relevant information.
  4. 04Brought retrieved context into the technical-support investigation, with the specialist responsible for resolution.

How the system works

A warehouse and retrieval layer with different jobs

The warehouse and vector index are complementary, not interchangeable. One organizes integrated enterprise data; the other makes relevant knowledge retrievable for a support question. The AI layer connects retrieval to the specialist's workflow rather than standing alone as a disconnected chat tool.

The model below illustrates their relationship without depicting the client's exact topology, vendors, source systems, or records.

DATA ARCHITECTURE / RETRIEVAL AIConnected information. Relevant context. Human judgment.Illustrative system model / anonymized engagement
Integrated data and semantic retrieval for technical supportIllustrative, anonymized system model. Multiple generic source systems integrate into a data warehouse. Knowledge content is prepared for a vector database. A technical question enters the retrieval path; the vector database returns relevant context, which is considered alongside integrated warehouse context by a support specialist. The diagram does not depict client data, specific products, or measured performance.01 / INTEGRATE02 / MODEL03 / INDEX04 / USESOURCE SYSTEMSSOURCE 01SOURCE 02SOURCE NDATA WAREHOUSEIntegrateModelServeSHARED DATA CONTEXTKNOWLEDGE CONTENTPREPARE + EMBEDVECTOR DATABASESCHEMATIC VECTOR SPACE?TECHNICAL QUESTIONRELEVANT CONTEXTDATA + RETRIEVALSUPPORT SPECIALISTHuman resolutionWarehouse and vector retrieval for technical supportIllustrative, anonymized system model. Multiple generic source systems integrate into a data warehouse. Knowledge content is prepared for a vector database. A technical question enters the retrieval path; the vector database returns relevant context, which is considered alongside integrated warehouse context by a support specialist. The diagram does not depict client data, specific products, or measured performance.SOURCESWAREHOUSEVECTOR DBCONTEXTSOURCE 01SOURCE 02SOURCE NKNOWLEDGEINGESTMODELSERVETO SUPPORTQUESTION / RETRIEVALConnected data and retrieval for a support specialistIllustrative, anonymized system model. Multiple generic source systems integrate into a data warehouse. Knowledge content is prepared for a vector database. A technical question enters the retrieval path; the vector database returns relevant context, which is considered alongside integrated warehouse context by a support specialist. The diagram does not depict client data, specific products, or measured performance.01 / SOURCE SYSTEMSDATA WAREHOUSEIntegrateModel + serve02 / PREPARE + EMBEDKNOWLEDGE CONTENTCONTENT03 / SEMANTIC RETRIEVALVECTOR DATABASE?TECHNICAL QUESTION04 / CONTEXT FOR A SPECIALISTRELEVANT CONTEXTINTEGRATED DATA + RETRIEVED MATERIALSUPPORT SPECIALIST / HUMAN RESOLUTION
Integrate / source systemsIndex / semantic retrievalUse / technical support
01

System integrations spanning multiple sources

02

Modern data warehouse for connected information

03

Vector database for retrieval

04

Retrieval AI oriented around technical-support investigations

From search to a support decision

A support question rarely respects system boundaries. Bringing data together before retrieval gives specialists a more useful path through the information surrounding a complex issue.

Retrieval narrows the search; the specialist determines what applies and what action to take. The architecture is designed to put connected information in front of the person responsible for resolution.

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