Move from fragile AI demos to defensible margin.
Customers and boards expect real AI workflows, but naive LLM wrappers risk hallucinations, GDPR/SOC 2 violations, and runaway inference costs.
We design enterprise-safe RAG pipelines, agentic tool-use guardrails, automated eval suites, and tiered model routing tailored to your domain data.
In Tel Aviv, the AI revolution isn't coming; it's already here. The pressure from the top tier of Israeli venture funds to integrate AI into your product is relentless. I've shipped over 15 SaaS products, and I can tell you that slapping an OpenAI wrapper on your app won't cut it anymore.
Startups are scrambling. You have product managers demanding 'AI features' without understanding the infrastructure required. The result is bloated prototypes, skyrocketing API costs, and models that hallucinate uncontrollably in production, damaging customer trust.
Relying entirely on third-party APIs with no proprietary data moat or fine-tuning strategy.
Token costs scaling linearly with users because there's no caching, semantic search, or query optimization.
Sending sensitive B2B customer data to public LLMs without proper sanitization, violating enterprise compliance.
Deploying generative AI without RAG (Retrieval-Augmented Generation) or strict guardrails, leading to unpredictable outputs.
AI features that live in a silo, requiring users to change their behavior rather than augmenting their existing flow.
Day 1-30: Audit your data architecture and implement a robust vector database. Build a semantic caching layer to slash API costs. Day 31-60: Implement RAG using your proprietary data to create a defensible moat. Day 61-90: Deploy fine-tuned models for specific edge cases and establish rigorous LLM evaluation pipelines.
You're competing for AI talent against Israeli cybersecurity and B2B SaaS unicorns. You can't just rely on hiring 10x prompt engineers; you need a systemic architectural approach that leverages your existing backend talent to build scalable AI infrastructure.
Your AI features are cost-effective, deterministic, and deeply integrated into your core product value proposition. Your enterprise clients trust your data handling.
If you're just looking to build a marketing demo to raise a seed round and don't care about scalable architecture, a dev shop might be cheaper. I build systems that scale.
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