Build AI products that can find and use the right enterprise evidence.
Searchplex engineers the retrieval layer behind assistants, search experiences, and agents—connecting enterprise sources to the evidence the system needs to answer or act reliably.
Different AI products. The same retrieval dependency.
Organisations rarely begin by saying they need RAG. They build a chatbot, copilot, AI search experience, research assistant, voice application, or agent. Once that product must use proprietary knowledge reliably, the same questions appear underneath: what information is available, what applies to this request, what deserves priority, and what evidence should reach the model?
Searchplex works on that retrieval layer.
Each surface creates different pressures. Voice tightens latency; agents may retrieve repeatedly; other applications place greater weight on citations, permissions, source authority, or historical versions. The dependency is shared, but the workload is not.
The interface is simple. The knowledge problem is not.
A fluent interface can hide unresolved decisions below the prompt and model. Four production pressures determine whether the application can use enterprise knowledge reliably.
Information availability
Enterprise knowledge is distributed across systems, changes over time, and often exists in duplicate or historical versions. The application cannot use evidence that the retrieval path cannot reach or distinguish.
Access and applicability
Permissions, identity, source authority, and current or historical state determine whether information is valid for a particular user, task, and moment.
Retrieval quality
If useful evidence never enters the candidate set or loses during ranking, generation cannot reliably recover it. Even good retrieved evidence can still be misused downstream.
Workload constraints
Interactive traffic, filters, freshness, ranking cost, repeated agent retrieval, and latency expectations shape the architecture that can work in production.
RAG builds on the Retrieval Foundation
The Retrieval Foundation determines which enterprise knowledge is available, retrievable and ranked. RAG adds a generation layer that turns the selected evidence into an answer or other application output.
- 01
Source Connectivity & Governance
Control which sources are available, current, authoritative and accessible for the request.
- 02
Content Preparation
Preserve the structure, metadata and content needed downstream, including tables, sections and multimodal material where relevant.
- 03
Retrieval Modeling
Decide what can compete for retrieval: document, article, clause, ticket, product, passage or another searchable unit.
- 04
Retrieval
Retrieve the right candidate evidence using lexical, vector, hybrid, filtered or other paths appropriate to the workload.
- 05
Ranking
Prioritise the strongest evidence using relevance, authority, freshness and application signals; rerank selectively where additional inference earns its latency and cost.
- 06
Evaluation & Operations
Keep source, retrieval and ranking behaviour measurable and traceable so changes can be compared, regressions found and production failures investigated.
Generation
Answer · abstention · recommendation · actionContext efficiency
Select and assemble the smallest useful evidence set rather than filling the context window indiscriminately. Context quality affects answer quality, latency and inference cost.
Grounded generation
Use the supplied evidence faithfully and preserve source attribution where the product requires it. Hallucination or grounding failures must remain distinguishable from upstream retrieval failures.
Guardrails and abstention
When evidence is missing, insufficient or outside the allowed scope, abstain, constrain the response, fall back or escalate rather than invent an answer.
Policy
Retrieval policy controls what evidence is eligible. Generation policy controls what the application may say or do with that evidence.
Evaluation spans both sides of the boundary: did the Retrieval Foundation supply the right evidence, and did Generation use that evidence correctly?
Different applications. Different workloads.
The same retrieval dependency appears under products with materially different users, risks, and operating conditions.
Permissioned knowledge that keeps changing
Internal assistants and search experiences over documents, tickets, messages, and business systems. Access, versions, freshness, and source authority determine which answer is valid for each user.
View use case →High-stakes public assistantReliable answers under public and burst traffic
A customer-facing conversational system where retrieval quality, safety, fallback behaviour, and latency interact under brand and traffic pressure.
View use case →AI conciergeContext-sensitive retrieval before action
An agentic experience that may retrieve repeatedly before answering, recommending, routing, or escalating, while keeping context tied to the correct guest, reservation, and authority boundary.
View use case →Built for enterprise constraints — including Europe.
European organisations often need retrieval systems designed around GDPR, EU AI Act governance, data residency, and operational ownership. Searchplex treats these as architecture inputs, not compliance badges; the applicable obligations still depend on the organisation, data, and use case.
Controls in the retrieval path
Data protection
Enterprise knowledge may contain personal, employee, or customer data. Access boundaries and unnecessary exposure should be addressed in the retrieval design, consistent with GDPR data-protection-by-design and default principles.
Access and traceability
Where the workload requires it, teams should be able to inspect who requested information, what the system could retrieve, which source and version it used, and what evidence reached the model.
AI governance
The EU AI Act is risk-based, so obligations depend on the system and its role. Traceable sources, identifiable configurations, controlled access, and evaluable behaviour support governance; they do not guarantee compliance.
Data control
The deployment boundary should fit the organisation's residency, security, sovereignty, and operational requirements.
EU-ready deployment options
Where European jurisdiction or provider control matters, Searchplex can design the retrieval platform for EU-based infrastructure, such as OVHcloud, rather than limiting the architecture to global hyperscalers.
Managed
The fastest path when external platform operations are acceptable.
Managed in your cloud
Data and infrastructure stay in the customer's account while operational burden is reduced.
Self-hosted / sovereign
Infrastructure and operations remain under customer control, including on-premise or European-jurisdiction deployments.
Hybrid
Different data or workloads use different deployment boundaries while sharing retrieval logic.
Build what the workload requires.
The Enterprise RAG Tiers define four build scopes, from bounded retrieval and grounded answers to enterprise controls, domain optimization, and multi-step research. Languages, modalities, deployment, and specific models depend on the workload.
- 01
Foundational
Reliable retrieval over governed enterprise knowledge.
- 02
Enterprise-Grade
Permissions, connectors, versions, formats, scale, and operating constraints.
- 03
Domain-Optimized
Workload-specific retrieval and ranking improvements where evaluation shows value.
- 04
Deep Research
Multi-step retrieval, verification, and synthesis where the use case requires it.
Production evidence
CuratedAI and Searchplex's applied use cases show the same architectural decisions under different domains, languages, permissions, and interaction patterns.
CuratedAI — multilingual legal retrieval
Workplace Knowledge RAG
RAG and agentic systems share the same Retrieval Foundation. Agents add repeated retrieval, routing, accumulated context, and downstream actions. Explore Agentic AI →
Architecture questions
The questions that usually determine scope before implementation begins.
Building an AI product on enterprise knowledge?
Searchplex can design the Retrieval Foundation and generation layer behind your application—from enterprise sources and permissions to grounded answers or actions.