We Build RAG-Powered Knowledge Systems So Your Support Team Answers 50% Fewer Tickets
Built by Georgia Tech-trained engineers with hands-on LLM evaluation experience. For B2B SaaS companies drowning in repetitive support requests.
Built for modern SaaS teams using AI-powered support stacks
Your Support Team Is Drowning. Your Docs Aren't Helping.
Same questions, hundreds of times
Your agents re-answer the same 15 questions 200+ times a month. Morale drops. CSAT drops.
Knowledge scattered everywhere
Notion, Confluence, Slack threads, tribal memory — nobody can find the right answer fast enough.
New reps take months to ramp
Onboarding a new support rep takes 3+ months. They shadow, guess, and escalate while learning.
Help center exists, nobody uses it
You built a help center, but customers still open tickets because they can't find what they need.
Meanwhile, your ticket backlog grows, handle times creep up, and you keep hiring more agents instead of fixing the root problem: your knowledge isn't accessible.
RAG-Powered Knowledge Systems That Actually Answer Questions
RAG (Retrieval-Augmented Generation) connects your existing documentation to an LLM. Instead of hallucinating answers, the system retrieves the right docs and generates accurate, cited responses — in seconds.
Fewer tickets reach human agents. Your team handles what actually needs a human.
Agents find answers instantly via internal search instead of digging through Confluence.
Every answer links back to the source document. No black-box guessing.
Deploys inside your AWS/Azure/GCP. Your data never leaves your environment.
Three RAG Products. One Goal.
Fewer Tickets, Faster Answers.
Customer-Facing Support Bot
$8,000–$20,000
An AI assistant that lives on your help center or in-app widget. It ingests your help articles, product docs, FAQs, and changelogs — then answers customer questions with source citations and confidence thresholds. When it's not sure, it escalates to a human.
- ✓Target: 40–53% ticket deflection rate
- ✓Stack: LangChain + pgvector + GPT-4o + FastAPI
- ✓Delivery: 3–5 weeks
Internal Knowledge Base Search
$12,000–$25,000
A private search tool for your support agents, engineers, and sales reps. Ingests Confluence, Notion, Slack exports, runbooks, SOPs, and onboarding docs. Your team asks questions in plain English and gets precise answers in seconds.
- ✓Target: Agent handle time reduction + internal ticket deflection
- ✓Stack: LlamaIndex + pgvector + Claude 3.5 Sonnet
- ✓Delivery: 4–6 weeks
RAG-Augmented Onboarding Assistant
$15,000–$30,000
Guides new customers or new employees through their first 30–90 days. Answers setup questions, surfaces relevant docs contextually, and reduces time-to-activation. Includes optional $1,500–$3,000/month retainer for ongoing doc updates.
- ✓Target: Time-to-activation, 30/60/90-day retention
- ✓Stack: LangChain + pgvector + GPT-4o + RAGAS eval
- ✓Delivery: 5–8 weeks
RAG Readiness Audit
$2,000–$3,000
1–2 week turnaround
Before you invest $10K–$30K in a full RAG build, know exactly what you're working with. This paid diagnostic gives you a clear picture of what to build, what ROI to expect, and what it will take — so you can make a confident go/no-go decision.
📋What You Get
- 1Full review of your existing docs, support workflows, and tool stack
- 2Gap analysis: what's missing, what's outdated, what's duplicated
- 3Suggested RAG architecture tailored to your stack
- 4Expected ROI model (ticket deflection, handle time savings, cost reduction)
- 5Implementation roadmap with timeline and budget
- 610-page report delivered to your team
From Audit to Production in 4 Steps
Audit
We review your docs, support stack, and workflows. You get a 10-page report with architecture recommendations and ROI projections.
1–2 weeks · $2K–$3KPrototype
We build a working RAG prototype using anonymized sample docs. You see results on your actual content before committing to a full build.
2–3 weeksDeploy
Full production system deployed into your infrastructure (AWS/Azure/GCP). Your data never leaves your environment. RAGAS evaluation ensures quality.
3–8 weeks (varies by product)Iterate
Post-launch monitoring, accuracy tuning, and doc ingestion updates. Optional retainer for ongoing improvements as your product evolves.
OngoingDuring the build phase: You provide anonymized or synthetic sample documents only. We sign a mutual NDA + DPA (for EU/GDPR compliance) + MSA + SOW before any data is discussed. Production deployment happens inside your infrastructure — we never store your data.
Your Data Stays in Your Infrastructure. Period.
We don't ask you to send us your data. We deploy into your environment and give you full control.
AES-256 Encryption at Rest
All stored embeddings and docs encrypted with AES-256.
TLS 1.3 in Transit
Every API call and data transfer encrypted with TLS 1.3.
Role-Based Access Control
RBAC ensures only authorized team members access the system.
Full Audit Logging
Every query and system action logged for compliance and review.
PII Redaction Before Embedding
Personally identifiable information stripped before vector storage.
No-Training-Data Clause
Contractual guarantee: your data is never used to train any model.
Deployment options: AWS · Azure · GCP — your cloud, your rules.
Is This Right for You?
✓This Is Built for You If…
- ✓You're a US-based B2B SaaS company (Series A or B, $5M–$30M raised)
- ✓You have 20–200 employees and a support team of 3–15 agents
- ✓Your agents are drowning in repetitive, low-complexity tickets
- ✓Your docs exist but are scattered across Notion, Confluence, and Slack
- ✓You've considered hiring an AI engineer but haven't pulled the trigger
- ✓You want to reduce support costs without reducing support quality
✗This Is Not the Right Fit If…
- ✗You're pre-product-market fit and don't have a support team yet
- ✗You're a consumer app without structured documentation
- ✗You expect magic results without investing in doc quality
- ✗You need a general-purpose chatbot (not doc-grounded RAG)
- ✗Your total budget for AI projects is under $5,000
Meet the Founder
Saud Kamran
Founder, Muqarnas Afza
I've trained and evaluated LLMs at scale, built backend systems at Ericsson, and studied the engineering and business side of AI at Georgia Tech. I started Muqarnas Afza to do one thing well: build RAG systems that actually reduce support load for SaaS companies.
Common Questions
How accurate are the answers?
RAG systems ground every response in your actual documentation, so accuracy depends on doc quality. We evaluate using the RAGAS framework (faithfulness, relevance, context precision) and typically achieve 85–95% accuracy on well-documented topics. Every answer includes source citations so users can verify.
How long does implementation take?
The RAG Readiness Audit takes 1–2 weeks. A full build ranges from 3–8 weeks depending on the product. Customer-facing bots are 3–5 weeks, internal search is 4–6 weeks, and onboarding assistants are 5–8 weeks. We scope this precisely during the audit.
What if our docs are a mess?
That's more common than you think — and it's exactly what the audit uncovers. We identify gaps, duplicates, and outdated content, then provide a prioritized cleanup plan. You don't need perfect docs to start; you need a clear path to good-enough docs.
Can you work with our existing tools (Zendesk, Intercom, Freshdesk)?
Yes. Our systems integrate with your existing support stack. The RAG layer sits behind your current tools — customers interact through your existing channels, and agents use a familiar interface. We don't replace your helpdesk; we make it smarter.
What happens after launch?
We provide a handoff package with full documentation, architecture diagrams, and runbooks. For ongoing support, we offer retainers ($1,500–$3,000/month) that cover doc ingestion updates, accuracy monitoring, and performance tuning as your product evolves.
Why not just use ChatGPT or build it in-house?
ChatGPT doesn't know your docs — it hallucinates. Building in-house means hiring an AI engineer ($150K+ salary), months of R&D, and ongoing maintenance. We deliver a production-ready, evaluated system in weeks, deployed in your infrastructure, for a fraction of the cost of a full-time hire.
Ready to Cut Your Support Ticket Volume in Half?
Start with a RAG Readiness Audit. In 1–2 weeks, you'll know exactly what to build, what it costs, and what ROI to expect — before committing to a full project.