Build enterprise RAG
on your data, in your environment.
We design enterprise RAG architecture for proprietary data, governed retrieval, and production-ready generative AI systems.Most enterprise RAG and generative AI systems work in demos. The ones that work in production are built on a different retrieval architecture. That is the difference we make.
71% of organisations use generative AI. Production outcomes tell a different story.
of organisations regularly use generative AI in at least one business function
Adoption is not the problem. Production is.
Most generative AI systems fail when they move beyond demos because the model is not grounded in reliable enterprise context. Without strong retrieval, systems produce fluent answers that cannot be trusted.
Agentic systems amplify the risk. When an agent starts from incorrect context, every step in the chain compounds the error.
The organisations seeing real outcomes share one trait: they built the retrieval layer properly before building applications on top of it. The fix is not a better model. It is a properly designed retrieval architecture.
The Executive Case for Production RAG
Go beyond impressive demos. A production RAG system delivers audited answers grounded in all your enterprise data—text, tables, and images—creating competitive advantage while reducing risk.
Verifiable Answers
Data Sovereignty
Multi-Format Intelligence
Domain-Specific Tuning
From Demo to Reality: Why Enterprise RAG is Hard
Naive RAG fails in production. Naive agentic AI fails faster. This is now an accepted fact across the industry. The question is not whether your current pipeline will hit limits — it is when, and whether your architecture can absorb it.
Messy, Multi-Format Data
Scale & Performance
Security & Permissions
Deep Domain Adaptation
Evaluation & Feedback
Lifecycle Management
The Path to Deep Research Readiness
Our Enterprise RAG Tiers Framework is your roadmap from foundational data cleanup to agentic, multi-format analysis—the capabilities required for true deep research.
Foundational
Unified, governed knowledge base with clean, structured data foundation.
The first step is getting your knowledge base into a shape where reliable retrieval is possible.
Enterprise-Grade
Total knowledge access across any format with scalable infrastructure.
This is where connectors, controls, security, and real operating constraints become part of the system.
Domain-Optimized
Systems that speak your language with specialized terminology and workflows.
The system starts adapting to your jargon, concepts, and domain-specific evaluation needs.
Deep Research
Agentic systems for multi-step analysis with autonomous reasoning capabilities.
This is where iterative retrieval, verification, and synthesis start compounding into real strategic value.
RAG in Action: Transforming Industries
We deliver RAG solutions that solve critical business problems across industries.
Pharma & Life Sciences
Legal & Compliance
HR & Policy Portals
Customer Support
RFP & Bid Automation
Workplace Search
The Engine, Not Just the Interface
A chatbot is the interface your user sees. RAG is the engine behind it—retrieving facts, generating accurate answers, and providing citations.
We build the mission-critical RAG engine first, then deliver it through the interfaces your users actually need.
Chat UI
Web app (Chatbot), Slack, or Microsoft Teams.
Intelligent Search
Synthesized answers with citations—not just ten blue links.
Embedded Co-pilot
Assistants inside portals, CRMs, and line-of-business apps.
Voice & Call Center Assist
Real-time retrieval for agents.
Compliance is decided at the retrieval layer. Not the LLM layer.
Most enterprise AI projects treat GDPR, the EU AI Act, and data sovereignty as legal reviews that happen after the system is built. That is backwards.
Every compliance question your legal team will ask — what data was retrieved, from where, under what access controls, for which user, and why it was surfaced — is determined by how the retrieval system is designed. Searchplex builds Enterprise RAG systems where compliance is structural, auditable, and enforced in the architecture from the start.
GDPR
Right to erasure does not stop at the application layer. It has to propagate through the retrieval index. Access controls have to apply at query time, not after results are returned. Data residency has to be enforced at the infrastructure level, not assumed.
We design retrieval systems where GDPR requirements are built into indexing, access control, and serving paths — not delegated to application logic that can be bypassed, drift, or fail silently.
EU AI Act
High-risk AI systems require explainability and auditability. If your RAG system cannot show why a specific document was retrieved for a specific query, you have an architecture problem — not just a reporting gap.
We build retrieval layers where results can be traced back to the signals, rules, and ranking logic that produced them, so auditability is part of the system itself.
Sovereign AI
Your documents, embeddings, and retrieval logs should remain under your control. Many AI vendors require data to pass through external infrastructure. For regulated environments, that is often unacceptable.
Searchplex designs self-hosted and customer-controlled retrieval architectures so the index, vector representations, query logs, and access boundaries stay inside your environment.
The retrieval layer is where enterprise data is exposed, filtered, ranked, and returned. It is also where it can be controlled, audited, and protected. Building compliance in after the fact costs more, breaks more, and proves less than designing it into retrieval from day one.
Your infrastructure. Your terms.
Every retrieval system we build can be deployed against the model that fits your operational, compliance, and commercial requirements. We have run all four in production — including deployments inside client cloud accounts and sovereign deployments on European infrastructure.
Managed infrastructure. Full retrieval power.
A managed retrieval platform handles cluster provisioning, scaling, upgrades, and operational monitoring. You get production-grade retrieval without the infrastructure overhead. Searchplex manages the retrieval architecture, schema design, and ranking logic while the platform handles the operational layer beneath it.
Teams who want to move fast without internal capacity to operate a distributed retrieval cluster. Typical for product companies and enterprise teams with constrained platform engineering resources where data residency is not a hard constraint.
Managed operations. Inside your environment.
A managed deployment can run entirely inside your own AWS, GCP, or Azure account. Your data never leaves your environment, but you still benefit from automated provisioning, upgrades, and monitoring.
This resolves the tension between operational simplicity and data residency. Searchplex designs the retrieval system. Your cloud account owns the infrastructure. No dedicated in-house retrieval operations capability required.
Regulated enterprise organisations — legal, financial services, healthcare — where data residency is a hard compliance requirement but internal platform engineering capacity for self-hosted operations is limited.
Your infrastructure. Your operations. Your jurisdiction.
The retrieval system runs on your own cloud account or on-premise hardware, with full operational ownership inside your team. Nothing transits external infrastructure at any point. For organisations with European-jurisdiction requirements, the same model can be deployed on OVHcloud.
This is the right path when operational sovereignty matters as much as data sovereignty, and when infrastructure independence is a hard requirement rather than a preference.
Large enterprise and public sector organisations with mature internal infrastructure teams who require complete operational sovereignty alongside data sovereignty, including teams that need European-jurisdiction infrastructure.
Sensitive data in. Everything else out.
A managed-in-account or self-hosted cluster for regulated content can sit alongside a managed platform for non-regulated workloads. Two data planes, unified retrieval logic, one interface. More operationally complex — but often the only architecture that satisfies compliance requirements and engineering velocity simultaneously when document classification is mixed.
Enterprise organisations operating across jurisdictions or teams with mixed data classification — regulated content sitting alongside general knowledge bases where different residency requirements apply to different corpora.
The same retrieval layer powers both.
RAG systems and agent-driven workflows are built on the same foundation: retrieving the right information, grounding outputs, and enforcing access correctly. As teams move toward agents, the demands on retrieval increase — not decrease.
Your Questions, Answered
Enterprise RAG Case Studies
See how Searchplex applies retrieval architecture in production for hybrid AI search systems and enterprise modernization work.
CuratedAI: from semantic-only search to hybrid multilingual legal retrieval
Ready to build a Deep Research engine your business can trust?
Searchplex helps teams move from promising RAG experiments to production retrieval systems with stronger grounding, clearer evaluation, and a deployment path that fits the business.