AI Product Factory
Add AI capabilities to your
visionary products
Add AI capabilities to your
visionary products
Product leaders are under immense pressure to add AI capabilities: smart assistants, automated workflows, or predictive insights, to remain competitive. However, internal engineering is focused on core products, and they likely lack the specialized experience required to handle LLM hallucinations, prompt engineering, agent orchestration or traditional machine learning model development.
Production engineering for AI starts with choosing the right partner who understands software engineering, not just data science, to surgically insert the right AI solution into your existing value stream. We engineer AI capabilities directly into your product stack. From requirement definition to production deployment, we handle implementation without requiring you to build an internal AI team or pause roadmap execution.
We don't start coding immediately. We audit your product workflows and ideas to identify high-value, low-risk opportunities where AI solves a genuine user problem, ensuring you don't build "AI for AI's sake.
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We build functional prototypes within your environment to test technical feasibility and user value. If it fails, it fails fast and cheaply. If it works, we have the blueprint for production.
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We build the "sidecar" AI services using Google Cloud's Vertex AI stack. We handle the API development, vector search implementation, and agentic orchestration required to connect the AI to your existing backend.
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We implement the necessary engineering controls (evals, grounding, output parsing, human-in-the-loop) to ensure the AI interacts safely with your users and data.
Aliz brings 15+ years of Google Cloud infrastructure heritage. We know that a successful AI feature is more than about choosing the right model. It's about the latency, the cost-per-query, the security context, and the Ops required to keep it running.
We work alongside your engineering team, ensuring that when we hand over the keys, your team understands how to maintain and evolve the feature.
While we run on GCP, we know how to integrate into .NET, Java, or Node.js monoliths running on-prem or other clouds.
We use a gated development process. We validate the "AI solvable" aspect before you commit to full engineering costs.
We utilize our internal libraries for RAG, Guardrails, Deployment templates and Eval (Evaluation) to shave weeks off development time.
Reach out, and let’s take your business to the next level.
