// AI / ENTERPRISE RAG
Your clients' knowledge, finally searchable and trustworthy.
Years of documents, policies and expertise, locked in files nobody can search. We build retrieval engines on your client's private data that answer with citations, refuse to guess, and run inside their own cloud. The system ships under your brand.
Start a RAG conversationGeneric models hallucinate on data they have never seen.
Clients across legal, finance, professional services and manufacturing all say the same thing: we have the knowledge, our teams just cannot find it. A public model invents answers about proprietary data it was never trained on, and most RAG proof-of-concepts never reach production because retrieval quality, governance and explainability all break down at once. The engineering behind a credible knowledge engine is harder than a demo suggests, and that is exactly where partners come to us.
What we build into the engine.
Tri-Cloud RAG Orchestration
RAG built on the cloud your client already runs: Vertex AI RAG Engine with Grounding on GCP, Amazon Bedrock Knowledge Bases on AWS, or Azure AI Search with semantic ranking. Every answer carries source citations, re-ranking and query rewriting.
Hybrid Retrieval
Vector search paired with BM25 keyword matching, so the system handles both fuzzy semantic queries and exact-match lookups. This is the difference between a demo that impresses and a system that holds up in production.
RAGAS Evaluation as a Deliverable
Faithfulness, Answer Relevance, Context Recall and Context Precision, wired in as a regression suite that runs on every content update. Quality is measured continuously, not asserted once at launch.
Confidence Thresholds
When grounding context is thin, the system says I do not know rather than inventing an answer. Retrieval confidence is scored on every query, so a wrong answer never reaches a regulator or a board paper.
Document-Level Access Control
Role-based permissions enforced at retrieval time. A user only ever sees source material they are cleared to read, so finance, legal and HR can share one engine without leaking across boundaries.
Production Handoff
A knowledge management workflow so the client's team adds and retires documents without us in the loop, plus monitoring dashboards tracking data freshness and retrieval quality over time.
The numbers behind grounded RAG.
How the options compare.
What your client gets from an internal build, a single-cloud vendor, or our team under your brand.
Enterprise RAG, answered straight.
01 What is the difference between RAG and fine-tuning?
RAG retrieves relevant documents at query time and feeds them to the model as context; the model weights never change. Fine-tuning rewrites the weights on your data. For knowledge retrieval, RAG is almost always the right call: content updates land instantly, every answer carries a source citation, and document-level access control is respected. Fine-tuning earns its place when the model needs to adopt a specific style or master a narrow task.
02 How do you stop the system giving a wrong answer on something important?
Two mechanisms. First, the RAGAS suite (Faithfulness, Answer Relevance, Context Recall) runs as a regression check on every content update, so quality is measured, not assumed. Second, retrieval confidence thresholds: when the system cannot find sufficient grounding context, it says so explicitly instead of guessing. Every answer ships with citations the user can verify.
03 Our client is worried about sensitive data going to a public model.
The RAG pipeline runs entirely inside the client's own cloud tenant. Data never leaves their environment. We document the data flow end to end and hand over a GDPR-compliant architecture diagram. For sovereign requirements, we deploy on-premises with locally hosted models, so there are no external API calls at all.
04 Why do so many RAG projects stall before production?
Between 40 and 60 percent of RAG proof-of-concepts never reach production, and the reasons are consistent: retrieval quality breaks down on real corpora, governance gaps surface late, and nobody can explain the AI's reasoning to an auditor. A demo hides all of this. We engineer chunking, embedding quality, vector selection, re-ranking and access control to be right at the same time, which is what production actually demands.
05 Can the system handle documents in more than one language?
Yes. Modern embedding models (text-embedding-3-large, Vertex AI text-embedding) support multilingual retrieval natively. For multi-language corpora we configure cross-lingual retrieval, so a query in English can surface the relevant French or German source, and the reverse.
06 What happens when new documents are added?
The handoff includes a knowledge management workflow, so the client's team adds and retires documents without us. Monitoring dashboards track data freshness, and RAGAS evaluation runs automatically on new content to catch any drop in retrieval quality before users feel it.
Tell us what your clients need.
A tri-cloud migration. A 200-site SD-WAN rollout. A security architecture before NIS2 hits. An AI system your client is asking about next quarter. We scope it, staff it, and deliver it under your brand. One conversation tells us if we are the right team.