AI Consulting & Development
From conversational AI agents and knowledge Q&A engines to agentic AI workflows, generative AI applications, and AI-powered marketing — we build intelligent systems that transform how businesses operate and serve their customers.
Book a Free ConsultationWhat is AI consulting, and what does it include?
AI consulting is deciding which business problems AI should solve, proving the approach on your own data, and taking it into production safely. Mantiqi does this for Saudi companies end to end: we scope the use cases, build the data and retrieval layer, then launch conversational AI, Arabic chatbots, RAG knowledge assistants or AI agents with a pilot group.
Typical timeline: Use-case workshop in week 1; first production pilot in weeks 4–6.
Why this matters
Most Saudi enterprises sit on years of customer data, internal knowledge, and operational signals that the team can't reach without a query specialist. ChatGPT-style demos are easy; production-grade AI that answers from your data, respects your access controls, and survives regulator scrutiny is the actually hard part. Mantiqi builds the AI layer that turns your existing systems — CRM, ERP, document stores, call recordings — into things your team can ask questions of, in Arabic and English, with citations back to source.
AI consulting for Saudi companies
Our AI consulting starts with a use-case workshop: two half-day sessions with your team to map where information gets stuck and pick the one or two use cases worth building first. You leave week 1 with a written scope you can take to procurement; then we build and measure the pilot with you, and train your team to run it.
- A use-case scorecard ranked by effort × impact
- An inventory of your data sources and an access plan
- A model and hosting recommendation: Saudi-hosted cloud, a cloud region outside the Kingdom, or on-premises
- A written scope with success metrics
What We Deliver
How we deliver
- 01
Use-case workshop
Week 1We sit with your team for two half-day sessions, map your current information bottlenecks, and pick the 1–2 highest-leverage use cases to build first. You leave with a written scope, success metrics, and a build plan you can hand to procurement.
- Use-case scorecard (effort × impact)
- Data-source inventory + access plan
- Model + hosting recommendation
- Written scope and success metrics
- 02
Data + retrieval foundation
Weeks 2–3We connect to your data sources, build the embedding + retrieval layer, and wire up evaluation harnesses before a single user-facing message. Cheap to fix problems here; expensive to fix them after launch.
- Vector store + retrieval pipeline
- Eval set + automated grading
- Access-control + audit logging
- Arabic + English language coverage
- 03
Production rollout
Weeks 4–6We ship the first agent / chatbot / Q&A surface to a pilot group, instrument it end-to-end, and run weekly review sessions to sharpen prompts, retrieval, and guardrails based on real usage.
- Production-deployed surface (web / WhatsApp / Teams)
- Observability dashboard with latency + grounding metrics
- Guardrails for PII, off-topic, and refusal paths
- Weekly evaluation reviews
- 04
Scale + handover
OngoingWe expand to additional use cases as you find them, train your team to own day-to-day operations, and move the model + infra to whichever environment (Saudi-hosted cloud, a cloud region outside the Kingdom, on-prem) your compliance team prefers.
- Internal team training + runbooks
- Cost optimization + model tier-down
- Quarterly model + retrieval refresh
- Optional managed-operations tier
Frequently asked questions
All of the above — we pick per use case. Customer-facing chatbots usually run on Claude or GPT for tone and reasoning quality. Internal document Q&A often runs on smaller models (Llama, Qwen) we host inside your tenant for cost and data-residency reasons. We can also run hybrid: a small router model decides which tier to escalate to.
Only if you explicitly approve it. We support fully in-Kingdom deployments on Saudi-hosted infrastructure or self-hosted models in your data center for clients with strict data-residency requirements (banks, government, healthcare). For lower-sensitivity use cases we'll use whatever cloud gives the best price-performance, with the boundary documented up-front.
Every answer in our deployments comes with citations back to the source documents. The evaluation harness we build in week 2 grades a held-out set of real questions weekly — we see grounding accuracy, retrieval hit-rate, and refusal rate as numbers, not vibes. If quality drops, the dashboard tells us before users do.
Yes — that's a baseline requirement we test on day one. We use models with strong Arabic coverage (Claude, GPT-4-class) and tune retrieval to match Arabic and English documents to questions in either language. Mixed-language queries (asking in Arabic about an English document) work correctly.
Per-query cost ranges from fractions of a halala (small models on internal docs) to a few halalas (frontier models on customer-facing chats), so the monthly bill depends almost entirely on volume and on which model each workflow actually needs. We instrument it from day one and report it alongside the rest of the running costs, so there are no surprises.
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 AI use-case scorecard
Send us a short note about what you'd want AI to do for your business. We'll send back a written scorecard ranking your top 3 use cases by effort × impact, with a rough build estimate for each. No sales pitch.
Get the scorecard