Build enterprise RAG that grounds answers in your data.
From a first knowledge assistant to a system spanning teams and data sources, Searchplex builds RAG applications that find the right evidence, respect access permissions, and give users answers they can verify.
Where we can help
Start with the situation closest to yours.
Launch a first RAG application
You have a defined set of sources and a workflow in mind, such as an internal assistant that answers policy or support questions with citations.
- Test it with a proof of concept →
Bring one retrieval question, sample data, and representative queries. In about three weeks, get a working prototype and findings against agreed criteria.
- See the Foundational scope →
Start with one workflow and a defined set of sources. Build a first production application with grounded answers and an evaluation baseline.
Bring more sources and users into scope
You need to connect fragmented knowledge, enforce permissions, or handle versions, languages, and higher traffic.
- See the Enterprise-Grade scope →
Connect multiple governed sources with access-aware retrieval, versions, freshness, and traceability, built for production load and your deployment requirements.
Improve a RAG prototype or live system
It works in demos, but quality is inconsistent: answers miss key evidence, cite stale sources, or cost too much to serve. You need to measure the failure and fix the part of the system causing it.
- Fix an existing RAG system →
Trace where useful evidence is lost, fix the stage responsible, and evaluate what changed. You get a focused improvement and a before-and-after evaluation.
- See how we evaluate RAG →
What Searchplex can build
We can build a production RAG application or implement the retrieval and ranking layer for an application your team owns. We agree upfront who handles source integration, access controls, answer generation, and rollout.
- 01
Foundational
A first production assistant or RAG application for a defined workflow and a known set of sources.
- 02
Enterprise-Grade
Connect multiple sources, enforce access rules, and handle freshness and production traffic.
- 03
Domain-Optimized
Tune retrieval and ranking for the language, terminology, and evidence needs of your domain.
- 04
Deep Research & Synthesis
Custom models, multi-step retrieval, verification, and synthesis, scoped as a bespoke engagement where the use case requires it.
RAG evaluation: how you'll know it works
We approach RAG as a retrieval and relevance engineering problem. A prototype that answers well in demos can still fail on the questions, users, and documents that matter in production. We agree what “good” means up front and evaluate against it, so quality is something you can check, not something you take on trust.
The right evidence
Does retrieval find the right source and version for representative questions?
Faithful answers
Do answers stay within the retrieved evidence, cite it, and fall back when it is insufficient?
Safe access
Does each user receive only what they are permitted to see?
Fast and affordable
Does it meet latency targets under realistic load, at a cost you can sustain?
When quality is inconsistent, this evaluation shows which stage is failing: retrieval, ranking, access rules, context assembly, or generation. Each has a different fix. The evaluation runs on a test set built from your real questions and can be re-run, so a change to a model, a source, or ranking is evaluated before it goes live. In a concierge agent, for example, an answer can be well grounded and still wrong for the guest, unit, or booking. See the AI concierge use case →
- Where practical, we keep retrieval separate from the generation model so you can evaluate a model change on its own.
- You can keep infrastructure under your control with deployment in your cloud or on your own infrastructure.
RAG in practice
CuratedAI — multilingual legal research
Workplace Knowledge RAG
Deployment and data control
We scope deployment around your security, data residency, and operational requirements, including EU hosting when needed. We can design for source permissions and traceability so teams can check which evidence reached the model.
Managed
Use provider-managed infrastructure when it fits your operating model.
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.
Different applications. Different workloads.
The same retrieval dependency appears under products with materially different users, risks, and operating conditions.
Reliable 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 →Planning search across enterprise sources beyond the RAG application? Enterprise Search Consulting → covers the underlying search architecture and implementation. RAG and agentic systems share the same Retrieval Foundation. Agents add repeated retrieval, routing, accumulated context, and downstream actions. Explore Agentic AI →
Work with us the way that suits your team
Either way, you own the result, and the aim is a team that can run it without us.
Searchplex delivers the project
Specialists paired with your engineers
Ready to discuss your RAG project?
Bring one workflow and the sources it depends on. We’ll discuss what to build or improve first, what needs testing, and what we’d need from your team.