Your RAG pilot worked.
Now make it work in production.
Most enterprise RAG implementations stall between proof of concept and production deployment. ClarityArc bridges that gap -- delivering governed, access-controlled, production-grade retrieval systems built for how your organization actually operates.
The pilot worked. Production is different.
Demo environments use curated content, single users, and no security model. Production means thousands of users, complex permissions, multiple knowledge sources, and documents that change daily. That gap kills most RAG projects.
Your internal team hit a wall.
Developers can build a RAG prototype. Building one that enforces per-user access controls, handles chunking across 12 document types, stays current as content changes, and degrades gracefully when knowledge is absent -- that requires a different kind of experience.
You need a firm that has done this before.
RAG implementation is not a software product you configure. It is a systems integration engagement that touches your data governance, security model, infrastructure, and user experience. The wrong consulting partner costs you months and budget.
End-to-end RAG implementation -- not a handoff after architecture
Most consulting firms stop at solution design and hand you a technical spec to implement internally. ClarityArc stays through production. We own the outcome, not just the blueprint.
Every engagement covers the full implementation lifecycle -- from knowledge audit and architecture through build, test, production deployment, and performance monitoring. You get a working system, not a document.
Knowledge Audit & Source Mapping
We map every knowledge source -- SharePoint, Teams, file shares, ERP, databases -- and assess data quality, governance gaps, and access control requirements before architecture begins.
RAG Architecture & Pipeline Design
Embedding model selection, vector store configuration, chunking strategy, retrieval approach, and integration design -- documented and approved before build starts.
Governed Build with Access Controls
Per-user permission enforcement at retrieval time, content classification at ingestion, and audit logging baked into the pipeline -- not bolted on afterward.
Production Deployment & Monitoring
Production handover, performance baseline, retrieval accuracy tracking against real queries, and optimization tuning until the system meets the standard your organization requires.
RAG implementation consulting for teams that need it done right
You have the infrastructure. You need the expertise.
Your team can build. What they lack is experience with the specific failure modes of enterprise RAG at scale -- access control edge cases, retrieval degradation with ungoverned data, multi-source ranking conflicts. We bring that pattern knowledge from deployments across energy, banking, and industrial organizations.
You need this inside your existing Microsoft environment.
Your organization has made significant investments in Microsoft 365, SharePoint, and Azure. You are not building a greenfield AI platform -- you are extending what you have. ClarityArc specializes in RAG deployments that operate entirely within your existing Microsoft trust boundary.
You have a knowledge problem. You need a working solution.
Your team spends hours every week searching for information that exists somewhere in your organization. You have tried SharePoint search. You have tried intranet redesigns. You need an AI that answers correctly from your actual documentation -- with citations and access controls your compliance team can live with.
Three ways to engage, depending on where you are
Scoping & Architecture
Not ready to commit to a full build? We start with a structured discovery engagement -- knowledge audit, architecture recommendation, access control assessment, and a scoped implementation plan. You get everything you need to make an informed decision before committing to build.
3–4 weeksFull Implementation
Architecture through production deployment. One knowledge domain, governed sources, access controls, and a working agent in production. Includes performance baseline and 30-day post-launch monitoring. This is what most mid-market organizations need.
8–14 weeksMulti-Domain Enterprise Build
Multiple knowledge domains, multi-tenant retrieval, complex access control hierarchies, and integration with enterprise search surfaces like Microsoft Copilot or ServiceNow. Appropriate for organizations with complex knowledge environments and large user bases.
16–28 weeksThe checklist most implementations miss
Per-user access controls enforced at retrieval time -- not just at the UI layer
Content-aware chunking strategy tuned per document type -- not uniform fixed-size splitting
Automated incremental indexing so the knowledge base stays current as documents change
Source citations returned with every answer -- every response is auditable and verifiable
Graceful decline behaviour when the answer is not in the knowledge base -- no hallucinated responses
Retrieval accuracy metrics tracked in production -- recall, precision, and faithfulness measured
Data classification enforced at ingestion -- only approved, governed content enters the knowledge base
Full documentation and knowledge transfer so your team can operate and extend the system independently
The five mistakes that kill enterprise RAG implementations
Index everything available and filter responses after retrieval
Govern at ingestion -- only approved, classified content enters the knowledge base, eliminating retrieval of sensitive or outdated material before it reaches the LLM
Apply fixed-size chunking uniformly across all document types
Content-aware chunking tuned per document type -- procedural documents, policies, and technical manuals each have different optimal chunk structures for retrieval accuracy
Single retrieval method -- pure vector similarity search
Hybrid retrieval combining dense vector search with sparse keyword matching -- proven to outperform single-method retrieval by 15–30% on enterprise knowledge bases
Evaluate retrieval quality manually during development only
Structured evaluation framework with recall, precision, and faithfulness metrics tracked continuously in production -- retrieval quality is monitored, not assumed
Hand off to internal team after architecture and design are complete
Stay through production deployment, monitoring, and tuning -- the engagement ends when the system is performing to standard, not when the design document is signed off
What enterprise teams ask before engaging a RAG consulting firm
Do we need to have our data governance in order before we start?
No -- but you need to be willing to address it as part of the engagement. Data governance issues are the single most common cause of RAG project delays. We surface them in Phase 01 and scope the remediation work explicitly so there are no surprises mid-build. Organizations with ungoverned knowledge bases should expect Phase 01 to include a data remediation workstream alongside architecture design.
Can you work with our existing development team?
Yes. Most of our engagements involve a hybrid model -- ClarityArc leads architecture and governs the build, with client developers involved in specific integration workstreams they own. We adapt to your team's capacity and skill set. The goal is knowledge transfer alongside delivery so your team can maintain and extend the system independently after handover.
What is the difference between RAG implementation consulting and just buying a RAG platform?
RAG platforms -- tools like Azure AI Search, Glean, or Vectara -- provide the retrieval infrastructure. They do not govern your content, enforce your access control model, tune chunking for your document types, or integrate with your specific business systems. Implementation consulting is the work of making the platform function correctly against your actual knowledge environment. Most organizations that buy a platform and skip implementation consulting end up with a system that works in testing and fails in production.
How do you measure whether the RAG implementation is working?
We establish a retrieval quality baseline before launch using a structured evaluation set drawn from real queries in your domain. Post-launch, we track recall (did the system find the right content?), faithfulness (did the answer stay within what was retrieved?), and relevance (did the answer address what the user actually asked?). These metrics are monitored continuously, not just checked at launch.
What does RAG implementation cost for an enterprise organization?
Scoping-only engagements typically run $15,000 to $25,000. Full single-domain implementations range from $40,000 to $90,000 depending on source complexity and access control requirements. Multi-domain enterprise builds start at $100,000 and scale with scope. We publish a detailed cost breakdown to help you build a business case.
Intelligent Knowledge Systems
View the full practice →Your RAG project deserves a consulting partner who stays through production.
We start with a focused scoping conversation -- no commitment required beyond that. Bring your knowledge problem and your constraints. We will tell you what a production-grade implementation actually takes.