AI Implementation Services: Strategy → Pilot → Scale
Practical AI that pays for itself. We deploy automation, agentic workflows, and decision intelligence that cut costs, grow revenue, and speed up teams—without heavy lift from your side.
Strategy → Pilot → Scale
Fast pilot in ~30 days · Enterprise-grade guardrails · Clear ROI tracking
Founder of AIvanguard.tech, Managing Partner at Gotha Capital. 18+ years leading logistics, e-commerce, and AI-driven transformations across MENA. MBA (Distinction), Bradford University; B.Sc. Industrial Engineering.
What we implement
Customer operations
- AI support assistants (first-line triage, KB Q&A, multilingual)
- Agent co-pilot (suggested responses, summaries, next-best-action)
- Voice IVR with LLM routing & intent prediction
Revenue & marketing
- Personalised campaigns and content pipelines with human-in-the-loop QA
- Lead scoring, propensity models, churn prediction
- Dynamic pricing and merchandising nudges
Ops & automation
- Process automation (email → ticket → workflow → CRM)
- Document automation (invoices, POs, claims, KYC)
- Forecasting (demand, inventory, staffing)
Data & governance
- Data readiness and quality checks, PII handling
- Retrieval (RAG), guardrails, audit trails
- MLOps & observability (drift, latency, cost controls)
Our approach (weeks, not years)
| Phase | Focus | Outputs | Typical duration |
|---|---|---|---|
| 1) Discovery & ROI framing | Identify high-leverage use cases; align on data, risks, KPIs | Prioritised use-case map, data inventory, success metrics | 1–2 weeks |
| 2) Rapid pilot | Working slice (LLM + integrations + human-in-the-loop) | Pilot in sandbox or limited prod; quality gates | 3–6 weeks |
| 3) Scale & MLOps | Harden, monitor, integrate; cost/perf tuning | SLAs, dashboards, guardrails, run-books | 4–8 weeks |
| 4) Enablement | Train teams; handover with co-ownership | Playbooks, prompt library, governance checklist | 1–2 weeks |
Case studies
Customer service automation — regional e-commerce
Context: A Middle Eastern e-commerce retailer with ~30,000 orders/month faced rising service costs. A 15-agent team was stuck answering repetitive “Where is my order?” across WhatsApp and email.
What we deployed: Multilingual AI assistant trained on policies, returns, and order-status API. The bot handled 60% of first-line tickets and escalated complex cases to humans with structured summaries in Zendesk.
Impact after 10 weeks:
- Avg response time: 14 → 2 minutes
- CSAT: 82% → 91%
- Support cost/order: −37%
- Breakeven: week 9
Stack: LLM + RAG over policy/KB, WhatsApp Business API, Zendesk, analytics dashboard.
Churn prediction & re-engagement — subscription food app (Jordan)
Context: A subscription app saw 12% monthly churn and little insight into cancellation reasons.
What we deployed: Churn-risk model + automated WhatsApp/Push sequences and personalised offers. Human agents focused on high-value saves.
Impact after 3 months:
- Churn: 12% → 7.5%
- AOV: +8%
- Net ROI: 4.3× (retained LTV − costs)
Stack: Feature store on transactional & feedback data, campaign automation, cohort reporting.
Pricing & ROI
| Package | What’s included | Timeline | Fee (USD) | Best for |
|---|---|---|---|---|
| Starter Pilot | Discovery, 1 prioritised use case, pilot build, basic analytics, handoff Includes 1–2 week discovery, sandbox pilot, basic dashboard, handoff playbook. |
4–6 weeks | $1,500–$9,000 | SMBs validating one workflow fast |
| Growth | 2–3 use cases, light productionisation, guardrails, dashboards, team training | 8–12 weeks | $9,000–$18,000 | Teams ready to scale beyond pilot |
| Scale & MLOps | HA setup, observability, cost/perf tuning, SSO, RBAC, governance & audits | 12–16 weeks | $18,000–$32,000 | Mid-market with compliance needs |
Expected ROI windows: Tier-1 support automation and document workflows typically hit 3–6× ROI in 3–6 months (labor hours saved, deflected contacts, 24/7 coverage). Marketing use cases often show uplift within the first 6–8 weeks (CTR/AOV/retention). We track time saved, revenue impact, and cost-to-serve from day one.
How we work (risk controls built-in)
- Privacy by design: PII minimisation, field-level masking, tenancy isolation, data retention rules
- Guardrails: prompts governance, output filters, policy checks, retrieval-scope control
- Human-in-the-loop: review queues for sensitive actions; feedback-to-learning loop
- Observability: cost per task, latency SLAs, success metrics, drift detection, eval harness
- Portability: open components where possible to avoid lock-in; cloud or on-prem
FAQs
Which use cases pay back fastest?
Tier-1 support, invoice/claims automation, and sales enablement (proposal/summary co-pilot). If you have volume, payback can be inside one quarter.
Do we need perfect data first?
No. We start where unstructured knowledge (policies, KB, tickets) plus guardrails deliver value quickly, then improve data quality in parallel.
Will quality drop if we automate?
Not if designed right. AI handles repeatable intents; humans take edge cases. CSAT usually rises when response time falls and answers are consistent.
Do you support Arabic + English?
Yes. We deploy multilingual assistants and content pipelines, including Arabic with diacritics-aware retrieval where needed.
Trusted by fast-growing teams
SaaS scale-ups · E-commerce brands · Fintech innovators
“We didn’t cut jobs — we gave our agents superpowers. AI handles routine chats while humans solve real problems.” — Operations Manager, Regional Retailer
Next steps
- Book a 20-minute scoping call
- We map 2–3 high-ROI candidates and a 30-day pilot
- Pilot → Go/No-Go → Scale with MLOps
© 2025 AIvanguard.tech — AI Implementation Services by Ehab AlDissi
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