AI Product Engineering :
Build Enterprise AI product That Are Ready for the Real World.

Cost depends on scope, data readiness, integration complexity, and whether you need a proof of concept, an MVP, or a full enterprise platform. Most engagements start with a scoping phase that defines a fixed estimate before any build work begins, so you know the number before you commit.

Timelines range from a few weeks for a focused proof of concept to several months for an enterprise-grade platform, depending on data readiness and integration scope. We size the timeline to your use case during scoping, rather than quoting a generic range.

Moving an AI prototype into production means turning a demo that works on simple data into a secure, scalable system that performs reliably on real data. This is where many projects fail. Key steps include:

• Defining success metrics like accuracy, speed, and cost
• Preparing production-grade data pipelines
• Testing and evaluating model performance
• Adding security guardrails and integrations
• Launching gradually and monitoring continuously

We protect your proprietary and customer data with a layered security approach, starting with NDAs and data processing agreements before any data is shared. Key safeguards include:

• Data anonymization and masking of sensitive information
• Encryption in transit and at rest, with role-based access controls
• AI model agreements that prevent your data from being used for training
• Private or on-premise model deployment for highly sensitive data
• Compliance with regulations like GDPR and India’s DPDP Act

Choosing the right AI model depends on the use case, data, accuracy needs, budget, and privacy requirements. Most products start with a foundation model like GPT, Claude, or Gemini combined with your own data (RAG), and use fine-tuned or custom models only when needed. We test shortlisted models on your real data, compare accuracy, speed, and cost, and build a flexible architecture so you can switch models later without rebuilding.

Yes. Most AI features can be added to your existing product through APIs without a full rebuild. Popular additions include AI chatbots, smart search, document processing, recommendations, and predictive analytics. The AI is built as a separate service that connects to your current system, so your core product stays intact. Older legacy systems may need minor updates, like an API layer, but rarely a complete rebuild.

You own the custom work built specifically for your product, source code, designs, and fine-tuned models developed under your engagement, once payment is complete, as set out in your service agreement. Zyrix retains rights to its pre-existing frameworks, reusable components, and accelerators used to build it faster; third-party models and platforms are licensed separately under their own terms. Full IP terms are defined in your contract.