AI-Powered Customer Service
Transform your customer support with AI-driven chatbots, intelligent ticket routing, call center analytics, and self-service portals — reducing response times by up to 80% while improving customer satisfaction scores across voice, chat, email, and WhatsApp channels.
Why this matters
Customer-service AI is mostly sold as "deflect 80% of tickets" and mostly delivers a chatbot that frustrates customers into demanding a human. The hard part isn't the model — it's grounding answers in your actual product knowledge, handling Arabic + English mid-sentence code-switching that Saudi customers do naturally, and knowing when to escalate gracefully instead of looping. Mantiqi builds AI customer-service systems where the chatbot is one tier of a layered system: deflection where confidence is high, agent-assist (drafted responses) where it isn't, and proper handoff with full conversation context when a human is needed.
What We Deliver
How we deliver
- 01
Channel + volume analysis
Week 1We pull a sample of your last 90 days of customer interactions (tickets, chats, calls if available), categorize them by intent, and identify which intent buckets are AI-deflectable today, which need agent-assist, and which always need human-only handling. Output is a written deflection roadmap.
- Intent taxonomy from real ticket sample
- Deflection-feasibility scorecard per intent
- Channel volume + cost-per-contact baseline
- ROI projection (with conservative assumptions)
- 02
Knowledge base + RAG foundation
Weeks 2–4Connect to your existing knowledge sources (Help Center articles, internal SOPs, product docs, FAQ), build the embedding + retrieval layer, and ship an evaluation harness grading answer accuracy on a held-out set of real questions. The AI is only as good as what it can retrieve.
- Knowledge sources connected + indexed
- Retrieval pipeline with citations
- Eval harness with grading criteria
- Arabic + English language coverage
- 03
Channel rollout (chatbot + agent-assist)
Weeks 4–8Launch on the lowest-risk channel first (typically web chat or WhatsApp), measure deflection + CSAT against baseline, then expand to other channels. Voice is usually phase 2 — the underlying transcription quality has to be measured before committing.
- Production chatbot on first channel
- Agent-assist draft suggestions in your helpdesk
- Smart escalation with conversation context
- Real-time monitoring dashboard
- 04
Operate + expand
OngoingDaily eval runs catch regressions, weekly review with your CX team to feed misclassifications back into the knowledge base, and quarterly expansion to new channels / new intent categories. Most clients see 30-55% deflection on the first channel within 90 days, scaling to 60-75% by month six as the knowledge base sharpens.
- Daily eval reports
- Weekly CX-team feedback loops
- Quarterly channel + intent expansion
- On-call coverage for incidents
Frequently asked questions
We build on top — your helpdesk stays. The AI layer integrates as: (1) a chat surface that creates / updates tickets in your existing system, (2) agent-assist draft suggestions inside the agent's view, (3) intelligent routing that uses your existing queues and SLAs. Replacing the helpdesk is rarely the right move; the operational maturity around an existing helpdesk has real value.
Still have questions?
Our team is ready to help. Reach out and we'll get back to you as soon as possible.
Free offer
Free customer-AI feasibility audit
Send us 90 days of anonymized ticket data (or a description of your support volume, channels, and current pain). We'll send back a written audit covering deflection-feasibility per intent category, projected ROI, and a phased rollout plan.
Audit + intent analysis is fixed-fee from SAR 30,000. Initial channel rollout (chatbot + agent-assist) typically SAR 100,000–250,000. Per-channel inference cost typically SAR 0.20–1.50 per conversation. Managed-CX-AI tier (monitoring, knowledge-base curation, eval reviews) from SAR 10,000 / mo.
Get the audit