AI Automation
Quiet AI. Customer-support agents, content pipelines, and back-office automations — that ship value, not buzzwords.
Deep dive
AI is the most over-promised technology on the market right now. Saudi businesses get pitched chatbots, copilots, and "AI transformation" packages every week — most of which would have been delivered as a mediocre rule engine in 2018 and called automation. We start by separating the pitch from the value.
Our AI work is grounded in three rules. First, we will not ship anything that hallucinates in a customer-facing context unless there is a human in the loop reviewing it. Second, every AI feature gets a measurable success metric on day one — not vibes, not engagement, hours saved or revenue moved. Third, we build with provider-agnostic abstractions (OpenAI, Anthropic, Google Gemini, local Llama) so you're not held hostage by a single vendor's pricing.
Real wins look like: voice-note transcription for Arabic-dialect inbound (Sales OS does this for insurance brokers), document extraction for ZATCA-compliant invoice processing, customer-support agents on WhatsApp that can resolve 60–70% of repetitive questions and escalate the rest cleanly, content pipelines that turn product specs into bilingual marketing copy, and back-office automations that compress 3-day approval cycles into hours.
We deliver in 4–12 weeks for most engagements. Use-case audit and pilot in weeks 1–4, hardening and monitoring in 5–8, expansion to adjacent workflows after that. The first thing we ship is always the smallest viable slice with measurement built in — proof of value first, scaling second.
Arabic-first
Generic LLMs handle Modern Standard Arabic decently and Saudi/Khaleeji dialects badly. We benchmark every model against actual Saudi-dialect inbound on day one, then either fine-tune on the client's data or route between models per task. The result is AI that recognizes Saudi voice notes, not just textbook Arabic.
Deliverables
- Use-case audit
- WhatsApp / chat agents
- Document & content pipelines
- Internal automations
- Monitoring & guardrails
What's not included
- "AI transformation" strategy decks. We ship working software, not slide decks.
- Black-box vendor integrations where you can't see what the model is doing or why.
- Customer-facing AI without human escalation paths. Every agent we ship has a clear handoff to a person.
Process
-
01 Use-case audit
Identify where AI actually saves hours — not just what sounds clever.
-
02 Pilot
One narrow use case shipped fast. Measured. Iterated.
-
03 Production
Hardened, monitored, with guardrails and human-in-the-loop where needed.
-
04 Expansion
Roll the playbook out to adjacent workflows, one at a time.
Typical timeline
4–10 weeks
Timelines flex with project scope, team availability, and response time on your side. We give a precise schedule after the discovery call.
Common questions
Will you use OpenAI / Anthropic / Google? Can we choose?
We pick per task based on benchmarks against your actual data. Anthropic (Claude) for nuanced reasoning, OpenAI (GPT) for speed and ecosystem, Google (Gemini) for multimodal and long-context, local Llama when data residency matters. The abstractions we ship let you switch providers later — that flexibility is part of the architecture.
How do you handle Arabic and Saudi dialects?
We benchmark across MSA + Saudi/Khaleeji on day one, on real inbound from your team or domain. Most generic models score poorly on Khaleeji voice. The mitigation is either fine-tuning on your transcribed data, prompt-engineering with dialect-specific examples, or routing voice notes through Whisper-large + a translation layer. We pick what fits your accuracy budget.
How do you measure if the AI is actually working?
A success metric is part of the discovery. For a customer-support agent: deflection rate, CSAT, hours saved per agent. For a content pipeline: time-to-publish, cost per piece, brand-voice score. For document extraction: accuracy on a held-out test set. We instrument from day one — if we can't measure the win, we don't ship.
What if model costs spike?
Every AI feature we ship has a per-call cost ceiling and a fallback path. If GPT-5 costs 3x what was assumed, we route to a cheaper model below a confidence threshold and only escalate to the premium model when needed. Cost optimization is a deliverable, not an afterthought.