The Right Answer, With Its Sources Attached
Retrieval is the search and answer layer your people, your assistants, and your agents all draw on. We build it once on the engines you already run, so every answer comes back with its sources and a score you can track.
Explore our other solutionsThe Challenge
Nobody logs the question that went unanswered, so the gap between what people ask and what comes back goes unrecorded.
Documents, not answers
Ten blue links when the person needed a number, a policy, or a straight yes.
Answers with no source
A generated response that reads well and cannot be traced to anything, so nobody will act on it.
Quality nobody measures
Answers judged by whoever complained most recently, tuned by opinion, and impossible to defend.
A new stack per channel
Site search, internal search, support, and now agents, each with its own index and its own answer.
The same layer serves a shopper on a product page, an employee looking for a policy, and an agent deciding whether it can commit to a delivery date. Every question one of them cannot answer becomes a question somebody else has to answer for them. Building it once, engine-independent and measured, keeps all three consistent, as long as the definitions underneath agree. That's what the Semantic Layer handles.
The Solution
Built on the search tools you already run: we organize your content so the right results can be found, work out what each question is really asking, build answers from sources people can check, and measure quality so it improves over time.
Retrieval Foundations
Three Ways to Search, One Answer
Different questions need different retrieval. An exact part number needs exact keyword matching, a vague description needs search that understands meaning, and "what works with this?" needs a map of how products relate. We combine lexical, semantic, and graph retrieval, routing each query to the approach that answers it.
We work on whatever you already run, including open-source engines and managed vector search. Building on the engine you have means the layer starts improving without switching platforms first.
Hybrid retrieval
Lexical, semantic, and graph retrieval combined and routed per query so the solution matches the need.
Content processing
Structured records and unstructured content through one pipeline, cleaned up and tagged as it comes in.
Platform neutrality
Built on the search platform you already have, whichever one that is.
Query Understanding
From a Messy Query to an Answerable One
Queries arrive with part numbers, typos, constraints, and two questions at once. We build the parsing, intent classification, and routing that turn one into something answerable. Facets, sorting, and recommendations come with it.
Conversations have context. When a follow-up says "the cheaper one," the system needs to know what was on screen a moment ago. Getting that right is what lets one retrieval layer serve a search box, an assistant, and an agent without three implementations behind them.
Intent and routing
Queries classified and sent to the retrieval strategy that answers them best.
Facets and refinement
Filtering, sorting, and recommendations that narrow a result set the way a person would.
Conversational memory
Follow-up questions resolved against what came before in the thread.
Grounded Answers
An Answer You Can Trace Back to a Document
Generated responses are built from retrieved content and cite what they used. When the content does not support an answer, the system says so. A system that admits the gap is one people will use in front of a customer or an auditor.
Permissions live inside retrieval. A person sees only what they are cleared for, an agent reaches only what it's been given access to, whether the request arrives from a search box, an assistant, or an MCP call. That is what makes it safe to let an agent answer without a person checking first.
Citations by default
Every answer carries the sources it was built from, visible to whoever reads it.
Gaps surfaced as gaps
When the content cannot support an answer, the system says so.
Access control inside retrieval
Permissions enforced at the point of retrieval, for people, assistants, and agents alike.
Measurement & Improvement
A Layer That Gets Better Every Cycle
We score answer quality against a judged query set and establish a baseline for where you are today. Analytics show what people looked for, what they found, and where they gave up.
Each tuning cycle targets a specific class of question: the spec questions that end in a call, the policy lookups that end in a ticket, the product questions that end in an abandoned session. The same measurement lets you score a new configuration or engine against the baseline before you commit to it, which turns a migration argument into a test.
Objective quality scoring
A judged query set and a number that moves when the answers move.
Search analytics
What people asked, what came back, and which questions still return nothing useful.
Test before you switch
Score a new configuration or engine against your baseline before it reaches production.
Find Out What Your Search Returns Today
Bring the questions your customers and your teams ask most, and we will look at what comes back.
Outcomes
Retrieval done properly shows up in the places people already measure.
Fewer questions that need a person
Questions that used to end in a ticket or a phone call resolve in the channel they started in.
Answers you can put in front of a customer
Citations and a system that admits its gaps make the output usable with a customer or an auditor.
One layer, every channel
Site search, internal search, assistants, and agents drawing on the same retrieval.
Improvement you can show
A number that moves cycle over cycle, against the questions the business cares about.
How Orium Helps
We have modernized search for distributors, retailers, and research platforms with large, complex repositories, and we operate it with you afterward, because answer quality drifts the moment the catalog does.
Assessment & Baseline
Score current answer quality against real queries and identify what is costing you results.
- Judged query set built from your actual search logs
- Objective quality baseline by query type
- Engine and configuration review across your existing search platforms
- Content and catalog readiness assessment
- Gap analysis against the answers your users expect
Retrieval Engineering
Build the hybrid retrieval, content processing, and query understanding on your stack.
- Hybrid keyword, vector, and graph retrieval
- Implementation on your existing search and vector platforms
- Content processing pipelines for structured and unstructured sources
- Query parsing, intent classification, and routing
- Facets, sorting, recommendations, and conversational memory
- Migration between engines, scored against your baseline first
Grounding & Guardrails
Citations, permissions, and failure behavior that make answers usable in production.
- Retrieval-augmented generation with source attribution
- Access control enforced inside retrieval, for people and agents
- MCP and API surfaces for agent access
- Behavior design for cases the content cannot answer
- Evaluation harnesses run on every release
Operate & Evolve
Ongoing operation and tuning as your catalog, content, and customers change, alongside your team or fully managed by us.
- Search analytics and query gap monitoring
- Continuous tuning against the baseline
- Predictive scoring of configuration changes before release
- Seasonal and catalog change support
- Fully managed operation, or enablement so your team runs it
Answer More Without Adding People
Talk with our team about building one retrieval layer your people, your assistants, and your agents can all draw on.
More Solutions
AI & Data Readiness
Learn moreFind out where your systems disagree and which of your AI plans the data can support today.
Semantic Layer & Ontology
Learn moreOne agreed definition of products, customers, and orders that every system works from, with changes versioned so you can see what each one affects before it ships.
Governed Data Products
Learn moreTurn contested extracts into owned assets with a contract, a named owner, a refresh schedule, and traceable lineage.
