AI that reaches production.
Not slideware.

Production-grade AI systems — not slide decks. A real engagement results in deployed AI agents, automation pipelines (n8n, MCP servers), integrations with existing systems (Salesforce, HubSpot, ERPs), measurable ROI within 60–90 days, and an internal team that can operate it after handoff. Anything less is a slideware exercise.

Two differentiators: (1) Operator background — Shai Truchman ran NYSE-listed tech (Spark Networks / JDate) and led digital transformation at the largest hotel chain in Europe (Fattal & Leonardo). This isn't theory. (2) Documented case studies across finance, hospitality, e-commerce, and family offices.

Most engagements run 60–90 days from discovery to production. Complex enterprise integrations (regulated industries, legacy systems) extend to 90–180 days. Pricing is project-based after a 14-day diagnostic — no monthly retainer traps, no hidden token costs.

Production-grade enterprise AI engagements in 2026 typically range from $40K–$60K for a focused single-workflow agent, $80K–$180K for a multi-system integration with MCP and n8n orchestration, and $200K+ for board-level transformations spanning multiple business units. Wizlu prices after a paid 14-day diagnostic that produces a fixed quote — never a retainer with open-ended billing. The diagnostic itself is $8K–$12K and is credited toward the full engagement if you proceed.

Big Four (Deloitte, Accenture, McKinsey, BCG) make sense when you need 50+ FTEs onsite, a board-defensible vendor brand, or a 12–24 month transformation program. Wizlu makes sense when you need a working system in 60–90 days, want a senior operator inside the room rather than three partners and twenty associates, and care more about the deployment metric than the deck. Most clients use Wizlu for the build and a Big Four for the surrounding change management — that combination tends to ship.

MCP is an open standard, introduced by Anthropic in late 2024 and rapidly adopted across major model vendors, that defines how AI models connect to enterprise data and tools. Before MCP, every AI integration was bespoke plumbing that broke when you changed model vendor. With MCP, a single well-designed server exposes Salesforce, internal databases, SharePoint, or proprietary tooling to any compliant model — Claude, GPT-5, Gemini, future models. The strategic value is vendor independence: customers with a mature MCP layer can A/B test models for cost and quality without rewriting integrations.

For enterprise AI workflows, n8n remains the strongest choice in 2026 for three reasons: (1) Self-hostable, meaning sensitive data never leaves your environment — a non-negotiable for regulated industries, family offices, and any company with EU operations under GDPR. (2) Native AI agent and MCP support that Zapier and Make are still catching up to. (3) Audit-friendly visual workflows that internal ops teams can maintain after handoff. Zapier and Make remain better for lightweight SMB use cases without integration depth.

Production AI agents (OpenAI, Anthropic, Google), n8n automation workflows, MCP (Model Context Protocol) servers, RAG architectures, fine-tuning when justified, vector databases, and AI strategy at the board level. Tooling is chosen for the problem — never the other way around.

Default to build when: the workflow is a competitive advantage, you have proprietary data the agent must reason over, per-seat licensing economics break above ~50 users, or you need vendor independence. Default to buy when: the workflow is fully commoditized (generic meeting summaries, basic coding assistance), you have fewer than ten target users, or you have zero internal capability to maintain. By mid-2026 the build threshold has dropped sharply — a focused agent that took a four-person quarter in 2024 takes a single architect six weeks today.

Yes — and this is a rare specialty. Shai operates at the intersection of AI infrastructure and private capital (he also runs UHNWS, a private exchange for blue-chip art and alternative assets). Family offices, single-family wealth structures, and private investors get an advisor who understands both the technology stack and the discretion required around proprietary deal flow and sensitive financial data.

Public-share-permitted examples: a European hospitality chain achieved 31% support-ticket deflection with 97-day payback. An Israeli e-commerce retailer reached 9x merchandising throughput per editor and $340K annualized ops savings. A single-family office recovered 11 hours per analyst per week on deal triage. ROI shape varies by industry, but the threshold for a committed Wizlu engagement is documented 6–18 month payback — without that projection in the diagnostic, the engagement does not proceed.

Three metrics, agreed in writing before kickoff: (1) Time-to-production (target: 60–90 days). (2) Documented ROI — hours saved, revenue attributed, error rate reduction. (3) Internal team autonomy — the client team can extend and maintain the system after handoff. No vanity metrics.

Hospitality (Fattal & Leonardo background), e-commerce and retail (Daka90 leadership), online services (Spark Networks / JDate), financial services, family offices, art and luxury markets (UHNWS), aviation MRO, and SMB operations. Cross-industry pattern recognition is part of the value.

Yes. The EU AI Act came into substantial enforcement through 2025–2026, and most production engagements now require Article 6 risk classification, transparency obligations, and human-oversight documentation as part of the build. GDPR Article 22 (automated decision-making) is reviewed for every customer-facing agent. Wizlu does not provide legal opinion, but engagements are designed to be compliance-ready and to integrate with the client's legal review, not to short-cut it.

Diagnostic phase. We audit existing workflows, data sources, and team capability. Output: a written roadmap with prioritized AI opportunities, expected ROI per opportunity, a phased delivery plan, and a fixed quote. Diagnostic is paid but credited toward the full engagement if you proceed.