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Embedding & retrieval design
Create retrieval patterns tuned to enterprise content and use cases.
Discuss this capability ↗Bring enterprise context and meaning to AI experiences.
Enterprise transformation rarely fails because a technology is unavailable. It stalls when architecture, operating context, data, security and adoption are treated as separate problems.
We bring those disciplines together so the capability can move from an initial priority into a repeatable operating model.
Enterprise technology creates momentum when architecture decisions are close to the work they are meant to improve.SOLVEXDATA / ENGINEERING PRINCIPLE
Define the business or technology behaviour that should improve.
Protect the constraints, controls and service expectations that matter.
Make ownership, observability and improvement part of the design.
We combine architecture decisions with engineering and operating context so the capability can move into production with clear ownership.
Create retrieval patterns tuned to enterprise content and use cases.
Discuss this capability ↗Design storage and indexing around semantic workloads.
Discuss this capability ↗Make enterprise knowledge discoverable by meaning, not only keywords.
Discuss this capability ↗Connect retrieval to controlled AI experiences.
Discuss this capability ↗Preserve business context, permissions and relevance signals.
Discuss this capability ↗Measure retrieval quality and improve the knowledge experience.
Discuss this capability ↗The outcome is a capability that is easier to operate, easier to evolve and better aligned to enterprise priorities.
Retrieve information by meaning and context.
Give AI systems access to approved enterprise knowledge.
Make content available across multiple experiences.
Respect metadata, permissions and business context in retrieval.
We focus on the workloads, decisions and operating moments where the capability creates practical value.
Make distributed knowledge easier to find through natural language.
Ground responses in approved enterprise content.
Improve context available to service teams and users.
Create semantic access across large document collections.
The delivery path is staged to reduce risk, create evidence early and leave behind a capability teams can run.
Identify documents, systems, metadata and access requirements.
Shape embeddings, indexing, filtering and retrieval patterns.
Implement ingestion, indexing and serving workflows.
Connect retrieval to controlled application and agent workflows.
Use representative queries and feedback to improve relevance.
Every enterprise environment is different. These are the conversations we typically bring into the room early.
It can complement keyword search. Hybrid approaches often provide better coverage across different enterprise search needs.
Access and metadata can be incorporated into retrieval design so users receive context appropriate to their permissions.
Evaluation can examine relevance, coverage, grounding and failure cases against representative enterprise queries.
Let’s map the current state, target outcome and practical path forward with your team.