Production-Ready RAG in 4-6 Weeks.
Not Another POC That Stalls.
Your documents become an intelligent knowledge system. Your team gets accurate, sourced answers. Your customers get AI-powered support without hallucinations. And you get there faster than you thought possible.
RAG: AI That Answers From Your Knowledge, Not Its Imagination
Large language models are impressive—until you need them to answer questions about YOUR business. Ask ChatGPT about your return policy, your product specs, or your internal procedures, and you'll get confident-sounding nonsense.
Retrieval-Augmented Generation (RAG) solves this by connecting AI to your actual documents and data. Instead of making up answers, RAG retrieves relevant information from your knowledge base and uses that context to generate accurate, grounded responses.
The result: an AI system that can answer questions like a knowledgeable employee who's read every document in your organization—but responds instantly, never forgets, and works 24/7.
How RAG Works
Retrieval
When someone asks a question, the system searches your documents for relevant passages
Augmentation
Those passages are provided to the AI as context
Generation
The AI crafts a response using your actual information, not its training data
This is how you build AI assistants that give correct answers about your products, chatbots that resolve customer issues, and search systems that actually understand what people are looking for.
Why Most GenAI Projects Never Make It to Production
The pattern is painfully common: excitement, pilot, stall.
The Impressive Demo That Goes Nowhere
Your team builds a proof-of-concept. It's impressive in the demo. Leadership gets excited. Then the POC sits in staging for six months while everyone debates security, accuracy, and ownership.
The Accuracy Problem No One Solved
The demo worked on cherry-picked examples. In production, the AI hallucinates on edge cases. Customer-facing deployment? Too risky. Without systematic evaluation, you can't deploy with confidence.
The Integration Nightmare
Your documents are scattered across SharePoint, Confluence, Drive, legacy systems. The POC worked on a clean test dataset. Connecting to real enterprise systems? Different challenge entirely.
The "Who Owns This?" Paralysis
Is this an IT project? A product initiative? Something for the AI team that doesn't exist yet? Without clear ownership and timeline, GenAI projects become perpetual experiments.
Knowledge Trapped in Documents
The result: millions of dollars of enterprise knowledge remains locked in documents nobody reads, while competitors ship AI-powered experiences that win customers.
The GenAI Accelerator exists because we've seen this pattern too many times—and we've built a methodology to break it.
From Documents to Production AI in 6 Weeks
The GenAI Accelerator isn't a proof-of-concept factory. It's a structured program that delivers production-ready RAG systems—with the evaluation framework, safety controls, and operational tooling required for real-world deployment.
What You Get
Production RAG System
A fully deployed retrieval-augmented generation system connected to your knowledge sources. Not a demo—a production system ready for real users with real questions.
Accuracy Evaluation Framework
Dashboards showing retrieval precision, answer quality, and confidence scores. Know exactly how well your system performs and track improvement over time.
Safety & Guardrails
Controls that prevent hallucination, enforce source attribution, handle edge cases gracefully. Essential for customer-facing or high-stakes use cases.
Observability & Analytics
Full visibility into what's being asked, how the system responds, where it struggles. Usage patterns and performance metrics that inform optimization.
Operational Runbook
Documentation covering monitoring, alerting, scaling, troubleshooting. Your team can operate and evolve the system after deployment.
How We Deliver Production RAG in 6 Weeks
Speed doesn't mean cutting corners. Our accelerator achieves rapid deployment through parallel workstreams, reusable components, and a methodology refined across dozens of implementations.
Discovery & Data Connection
We map your knowledge landscape—where documents live, how they're structured, what formats they're in. In parallel, we establish connections to priority data sources and begin ingestion.
- • Knowledge source audit
- • Use case prioritization
- • Data pipeline configuration
- • Document processing and chunking
- • Vector embedding generation
Deliverables: Knowledge architecture map, connected data sources, initial vector index
RAG System Development
We build the retrieval system, integrate the LLM layer, and establish the evaluation framework. The system begins answering questions against your actual knowledge base.
- • Retrieval pipeline optimization
- • LLM integration and prompt engineering
- • Evaluation test set creation
- • Initial accuracy measurement
Deliverables: Functional RAG system, baseline accuracy metrics, evaluation dashboard
Safety, Guardrails & Integration
We implement safety controls, connect to authentication systems, and integrate with target applications—chat interfaces, internal tools, or customer-facing systems.
- • Guardrail implementation
- • Source attribution enforcement
- • SSO/authentication integration
- • API development
- • Security review
Deliverables: Production-hardened system, integrated with target platforms, security documentation
Launch & Knowledge Transfer
Production deployment, user onboarding, and handoff to your team. We ensure you have everything needed to operate, monitor, and improve the system.
- • Production deployment
- • User training
- • Operations team handoff
- • Documentation finalization
Deliverables: Production system live, trained team, complete documentation, 30-day support
What Can You Build in 6 Weeks?
RAG is versatile. Here's what organizations are deploying with the GenAI Accelerator:
Enterprise Knowledge Assistant
Turn scattered documentation into an intelligent assistant that answers employee questions instantly. Onboarding, HR policies, technical docs—all accessible through conversation.
Impact: Improved onboarding, reduced IT tickets, democratized knowledge
Customer Support AI
AI that resolves customer inquiries using your actual product docs, FAQs, and support history. Accurate answers, properly sourced, with graceful escalation.
Impact: Faster resolution, reduced costs, consistent experience
Intelligent Product Search
Go beyond keyword matching. Search that understands what users want and returns relevant results even when they don't use the right terminology.
Impact: Better conversion, reduced abandonment, improved discovery
Technical Documentation Assistant
Enable engineers to query complex documentation conversationally. API references, architecture docs, troubleshooting guides—instantly accessible.
Impact: Faster development, less time searching, better knowledge retention
Sales Enablement AI
Give sales teams instant access to product info, competitive intelligence, and case studies. Answer prospect questions in real-time during calls.
Impact: More confident conversations, faster deals, consistent messaging
Compliance & Policy Assistant
Make regulatory documents and internal policies accessible through natural language. Essential for regulated industries.
Impact: Reduced compliance risk, faster interpretation, audit-ready responses
RAG for Every Stage and Situation
Startups Building AI-Powered Products
Challenge
You want AI features in your product but don't have an ML team.
Our Approach
The accelerator lets you ship intelligent search, Q&A, or assistant capabilities without building AI infrastructure from scratch.
What You Get
Production AI features in your product timeline, not a research project timeline.
Accuracy You Can Measure, Not Just Hope For
"Does it work?" isn't a yes/no question for RAG systems. Accuracy varies by question type, document domain, and use case. That's why every deployment includes a rigorous evaluation framework.
What We Measure
Retrieval Quality
Does the system find the right documents? We measure precision (are retrieved docs relevant?) and recall (are all relevant docs found?).
Answer Accuracy
Does the generated answer correctly reflect retrieved information? We evaluate faithfulness to source material and factual correctness.
Hallucination Rate
How often does the system generate information not supported by documents? We track this continuously—lower is better.
Response Quality
Beyond accuracy—is the response helpful, well-structured, and appropriate for the audience?
Coverage Gaps
What questions can't be answered well? Identifying gaps guides knowledge base improvements.
The result: dashboards that show exactly how your RAG system performs, where it excels, and where it needs improvement. Not gut feel—measured performance.
Built on Modern Foundations, Adapted to Your Reality
We're not locked to any single vendor or framework. Technology choices are driven by your requirements:
LLM Selection
OpenAI, Anthropic, Azure OpenAI, open-source models—selected based on your needs for capability, cost, and data residency.
Vector Databases
Pinecone, Weaviate, Qdrant, pgvector, Elasticsearch—chosen based on scale requirements, existing infrastructure, and operational preferences.
Embedding Models
OpenAI embeddings, Cohere, open-source sentence transformers—matched to your domain and performance requirements.
Orchestration
LangChain, LlamaIndex, custom implementations—architectural decisions based on complexity and maintainability needs.
Deployment
Cloud-native, on-premises, hybrid—deployed where your data and compliance requirements dictate. We adapt to your infrastructure, not the other way around.
Common Questions About the GenAI Accelerator
Investment & Engagement Options
The GenAI Accelerator is structured for maximum value in minimum time. Here's how engagements typically structure:
Discovery Sprint
Not sure if RAG is right? We evaluate your use cases, assess data readiness, and provide architecture recommendations with go/no-go guidance.
Best for: Organizations exploring GenAI options
Standard Accelerator
Production RAG connected to 2-3 data sources, single use case deployment, evaluation framework, safety guardrails, 30-day support.
Best for: Organizations with clear use case and defined data
Enterprise Accelerator
Multiple data source integrations, multiple use cases, advanced security requirements, custom integration development, extended support.
Best for: Large organizations with complex data landscapes
Every project starts with a conversation. No commitment required.
Why the GenAI Accelerator Succeeds Where Others Stall
Production Intent, Not POC Mentality
From day one, we're building for production. Architecture decisions, security, and operational tooling are built in—not bolted on after the demo.
Evaluation as Foundation
Most RAG implementations hope they're accurate. We measure accuracy systematically from the start. You know exactly how well your system performs before customers see it.
Guardrails by Design
Safety controls aren't optional features—they're architectural decisions. Source attribution, confidence thresholds, and hallucination prevention are built into the design.
Real Enterprise Integration
We don't pretend your data is clean. We connect to messy enterprise reality—SharePoint, Confluence, Drive, legacy systems—and build solutions that work with actual infrastructure.
Knowledge Transfer Included
We're not creating dependency. Every engagement includes documentation, training, and handoff so your team operates and evolves the system independently.
Honest Scoping
Not every problem needs RAG. We'll tell you when fine-tuning, simple search, or traditional software would serve you better. Our goal is solving your problem.
Ready to Turn Your Knowledge Into Intelligence?
Start with a conversation. We'll discuss your knowledge landscape, potential use cases, and timeline—then tell you honestly whether the accelerator is right for your situation.
At a Glance
Key Takeaways:
- •GenAI Product Accelerator ships production RAG features in 4-6 weeks with measurable accuracy and safety gates
- •Includes full eval suite, CI regression checks, and observability dashboards
- •Supports cloud, on-prem, and hybrid deployments with PII protection and compliance (HIPAA, SOC2)
- •Average 91% accuracy and <3% hallucination rate in production
GenAI Product Impact: Measured Results
When to Choose What
GenAI Product Accelerator builds RAG features for search and Q&A. For multi-step workflows with actions, consider Agentic AI.
GenAI Product Accelerator
Best for RAG/search/Q&A features
- ✓Knowledge retrieval and semantic search
- ✓Document Q&A and summarization
- ✓Conversational AI assistants
- ✓Content generation with grounding
GenAI Product Outcomes
See the math →- •Working RAG feature in prod with accuracy ≥ target
- •Evals dashboard and CI check for regressions
- •Usage analytics and safety monitoring
- •Measurable user satisfaction or task completion rate
- •Cost per query optimized and tracked
What You Get: GenAI Product Deliverables
Our standards →Timeline
4 weeks (MVP), 6 weeks (production-ready with full evals and monitoring)
Team
architect, MLE, FE, BE, QA; security reviewer for prod
Industry Benchmarks & Statistics
Based on 35+ production RAG deployments across enterprise and mid-market companies in retail, healthcare, and financial services.
Inputs We Need
- •10-50 sample Q&A pairs for evaluation
- •Source documents or knowledge base
- •Accuracy targets and success metrics
- •PII/compliance requirements (HIPAA, SOC2, etc.)
- •Existing APIs or systems to integrate
Tech & Deployment
Vector stores: Pinecone/Weaviate/pgvector with hybrid search. Models: OpenAI (GPT-5/4), Anthropic (Claude 3.5), Google (Gemini), or open-source (Llama 3). Chunking: semantic splitting with overlap; metadata enrichment. Retrieval: BM25 + dense embeddings; reranking with Cohere/cross-encoders. Observability: LangSmith/Phoenix/custom; cost tracking per query. Deployment: Cloud (AWS/GCP/Azure) or on-prem; API Gateway + auth (OAuth2/API keys). Safety: PII redaction, content filters (Azure Content Safety/Llama Guard), rate limits.
Proof We Show
Full evidence list →Frequently Asked Questions
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