RAG Development Services for Enterprise Knowledge
We build retrieval-augmented generation (RAG) systems that answer from your own documents and data, with citations your team can check and accuracy measured against benchmarks agreed with you.
What We Build With RAG
RAG systems designed for data privacy, security and measurable accuracy, for organizations across the USA, Canada and the UK.
Enterprise Knowledge Q&A Bots
Assistants that answer employee questions from your documentation, with links to the sources they used.
Document Intelligence & Insights
Extract and summarize information from unstructured documents for your decision-makers to review.
AI Copilot for Workflow Support
Copilots inside CRM and ERP tools that suggest next steps based on relevant internal data, for users to accept or change.
Enterprise Document Q&A Automation
Query large collections of contracts, policies and research, with every answer linked to its source.
Real-Time AI Summarization
Pipelines that fetch current data from approved sources, such as news or pricing feeds, and produce timely summaries.
Sector-Specific RAG Solutions
We build pipelines using your domain data, terminology and regulatory requirements, for the sectors we serve.
RAG Across the Industries We Serve
Examples of how RAG supports teams in our focus sectors.
Customer Support
Assistants answer routine questions from manuals and account history, and route complex issues to your team.
EdTech
Search across course content and policies to answer learner and staff questions, with sources linked.
Retail
Answer product and policy questions from your catalog and help content, with escalation to your team.
FinTech
Search policies, procedures and case history so analysts can find what they need faster.
Healthcare
Search administrative policies, payer rules and documentation for staff. Clinical decisions stay with clinicians.
Logistics & Supply Chain
Search shipping documents, contracts and procedures to resolve exceptions faster.
Media
Search archives, rights documents and metadata to find and reuse content.
Insurance
Check policy wording and claim documents so adjusters can decide with the facts in front of them.
Build RAG Systems Your Team Can Check
Turn your documents and data into answers with citations, evaluated against benchmarks agreed with you.
Why Choose Agentic Resources for RAG Development?
We fix the data and definitions first, build governance in, and measure accuracy against agreed benchmarks.
01
Architecture Built for Production
We design RAG systems for your data volumes and response-time targets, tested before go-live.
02
Built Around Your Data
RAG pipelines designed around your document types, data structures and access rules.
03
Works With Your AI Stack
We work with models and tools from providers such as OpenAI, Anthropic, LangChain and Pinecone, selected per project.
04
Measured Retrieval Accuracy
We tune embeddings, chunking and reranking to measurably reduce hallucination rates, reported through evaluation benchmarks agreed with you.
05
Agentic RAG
Where useful, agents act on retrieved information to complete multi-step tasks, with human approval at consequential steps.
06
Diagnostic to Operation
From the readiness diagnostic through build, deployment and ongoing operation.
How We Architect RAG Systems
Retrieval, embedding and orchestration components chosen for your data, security and performance needs.
We use vector databases such as Pinecone, Milvus and Weaviate, chosen for your data volume, security and hosting needs.
We select embedding models from providers such as OpenAI, Hugging Face and Cohere, evaluated on your own data.
Routing selects the most relevant context for each question, so answers stay grounded in your sources.
We design pipelines on cloud-native infrastructure that scales with your data and users.
We connect RAG to models from OpenAI, Anthropic, Google, Meta and open-source providers, managing context windows and cost.
Frameworks We Design For
We design RAG systems to support your obligations under these frameworks. They are not certifications held by Agentic Resources.
Power Better Answers With Custom RAG
Connect language models to your enterprise knowledge with access controls, citations and measured accuracy.
How We Deliver RAG Systems
A staged process, from diagnostic to limited production, with a decision point at each step.
Requirement Analysis
We identify the business questions, data sources and users, and agree accuracy benchmarks for the RAG system.
Project Planning
Our team creates a detailed roadmap, defining technical milestones, resource allocation, and timelines to ensure transparent and timely project delivery.
Architecture and Design
We design a modular RAG architecture, selecting vector databases, embedding models and orchestration for reliable retrieval.
Agile Development
Using iterative sprints, we build and refine the RAG pipeline, ensuring continuous feedback and integration of features throughout the dev lifecycle.
Security and Access Control
We implement robust security protocols, including data encryption and Role-Based Access Control (RBAC), to protect sensitive enterprise information.
System Integration
We connect the RAG system to your CRM, ERP and APIs through approved interfaces.
Testing and Optimization
We evaluate retrieval accuracy and response quality against the agreed benchmarks, reducing hallucination rates and latency.
Compliance Validation
We review the system with your team against the frameworks that apply to you, such as HIPAA (US), GDPR and UK GDPR (EU and UK), and SOC 2 audit requirements, before production.
Deployment and Handover
We manage the smooth launch of your AI system into the live environment, providing complete documentation and training for your internal teams.
Ongoing Support
Our commitment continues post-launch with proactive monitoring, performance updates, and technical support to keep your RAG system at peak efficiency.
How You Can Engage Us
Four engagement models, plus pilot and managed-operation options.
Managed Projects
End-to-end delivery against an agreed scope, timeline and budget, with fixed or variable commercial structures.
Dedicated Teams
A multidisciplinary team assigned to your organization and roadmap.
Staff Augmentation
Named RAG, data and engineering specialists embedded within your existing teams.
Consulting & Advisory
Readiness, architecture, data strategy and governance planning, with no obligation to build.
Controlled Workflow Pilot
One RAG use case tested in a sandbox against agreed accuracy benchmarks.
Managed Operation
We monitor, operate and improve the system until your team is ready to take over.
Unlock the Value of Your Enterprise Knowledge
Start with a free 30-minute AI Opportunity Review. We will look at one use case with you and tell you what it would take.
FAQs
RAG grounds AI in your private data, ensuring accuracy and providing citations, unlike generic LLMs which may hallucinate or lack specific context.
We integrate diverse sources including SQL/NoSQL databases, cloud storage (S3/Drive), PDFs, CRMs, and real-time APIs for comprehensive knowledge access.
We use vector databases and cloud infrastructure sized for your data and users, tested under expected load.
Yes, our RAG solutions can fetch live data via APIs, allowing the AI to answer questions based on the most current inventory or market prices.
We use reranking, filtering and source verification, and measure hallucination rates against agreed benchmarks.
Fine-tuning teaches a model new styles/tasks, while RAG gives the model a searchable library of facts that can be updated without retraining.
RAG systems can scale to large document collections and many users; we size and test them for your expected load.
Document-heavy sectors, including financial services, insurance, healthcare administration, education, logistics, retail and media.