Newpage supports AI and data transformation by offering end‑to‑end services including foundation model tuning, LLM pipeline engineering, RAG systems, multi-agent automation, MLOps, cloud optimization, and data engineering.

We help clients identify high‑impact AI use cases, design robust data pipelines, and operationalize models in production while maintaining governance, security, and vendor‑agnostic flexibility.

Newpage’s AI delivery methodology follows six key phases:

1. Discovery & Problem Definition: Understand business objectives and AI‑relevant challenges.
2. Data Collection & Preparation: Ingest and clean data from multiple sources.
3. Model Development & Prototyping: Build and tune models against real business metrics.
4. Validation & Testing: Evaluate performance on validation sets and edge cases.
5. Deployment: Integrate models into production systems or applications.
6. Monitoring & Maintenance: Continuously track model performance and retrain as needed.

This structured approach ensures repeatable, measurable, and production‑ready AI outcomes.

Newpage delivers a wide range of advanced AI solutions tailored to the needs of businesses of all sizes, including AI agents and assistants, multi‑agent systems, RAG (Retrieval‑Augmented Generation) and knowledge systems, and custom Gen AI workflows. We build:

Intelligent Agents that automate routine tasks, support customer interactions, and augment internal teams
Multi‑agent systems that coordinate specialized agents to solve complex business processes

• RAG and knowledge‑based solutions to enable organizations to securely ground AI responses in internal documentation, data lakes, and proprietary content, ensuring accurate, context‑aware answers and reducing hallucinations.

These solutions can be integrated into existing applications, CRMs, helpdesks, and internal knowledge portals to drive efficiency, personalization, and decision‑making at scale.

Our data engineering process ensures that AI systems run on clean, structured, high-trust data:

• Data ingestion from enterprise systems (CRM, EHR, ERP, LIMS)
• Semantic data modeling & feature engineering
• Data quality pipelines with anomaly detection
• Vectorization strategies & embedding optimization
• Governance policies for access, retention, and lineage This foundation ensures that AI outputs are accurate, contextual, and auditable.

Cloud engineering provides the infrastructure, scalability, and tooling needed to run AI and data workloads efficiently. Using platforms such as AWS, Azure, and Google Cloud, Newpage designs cloud architectures that enable elastic compute for training large models, secure storage for data lakes, and low‑latency serving for real‑time AI applications, all while balancing cost and performance.

Newpage’s cloud team supports:

• Designing GPU-ready architectures for inference & training
• Optimizing storage, compute clusters, and vector databases
• Ensuring low-latency APIs for AI-driven applications
• Integrating cloud-native security and disaster recovery
• Building CI/CD pipelines for AI models (MLOps)

MLOps (Machine Learning Operations) refers to the practice of applying DevOps‑like principles to machine learning systems including, versioning models, automating pipelines, monitoring performance, and managing deployments. It is critical for maintaining AI reliability, reproducibility, and compliance at scale, especially when deploying models across multiple business units or production environments.

Newpage leverages a broad stack of modern AI and data platforms, including:

• ML/GenAI frameworks: OpenAI, PaLM2, Hugging Face, Mosaic, Meta
• Vector databases & similarity search: Weaviate, Pinecone, Milvus, Chroma
• Cloud data and analytics platforms: Databricks, Google Cloud, Azure, AWS

This allows us to build flexible, high‑performance AI applications that integrate seamlessly with existing data ecosystems.

Generative AI models (e.g., those built on OpenAI, PaLM2, or open‑source stacks) can be wrapped as APIs and integrated into existing enterprise systems via an API‑first

architecture. This approach gives organizations full control over prompts, data routing, and governance, reduces vendor lock‑in, and allows flexible reuse of AI capabilities across multiple products, workflows, and channels.

We embed Responsible AI, privacy-by-design, and data-minimization frameworks at every layer. Our AI pipelines include:

• Differential privacy & PII masking
• Model explainability (XAI)
• Access control & role-based agent governance
• Audit logs for all LLM interactions
• Secure cloud deployment (AWS/Azure/GCP) This ensures pharma-grade and healthcare-grade compliance

Yes. Newpage builds enterprise-grade RAG architectures using Pinecone, FAISS, Qdrant, Weaviate, and cloud-native vector stores. We support:

• Document chunking & semantic indexing
• Hybrid search (BM25 + vectors)
• Source-grounded citations for regulatory needs
• Private, secure knowledge systems RAG helps enterprises prevent hallucinations and deliver factual, traceable answers

Absolutely. Our MarTech engineering team specializes in:

• Salesforce Einstein + GenAI integrations
• Adobe Experience Cloud + AI-driven personalization
• Custom marketing data pipelines
• LLM content workflows for CRM and marketing automation This improves campaign targeting, customer insights, and content velocity

We specialize in:

• Pharmaceuticals & Biotech (bioinformatics, medical content automation, PV, regulatory ops)
• Healthcare (clinical workflows, care management, EHR augmentation)
• Retail (customer analytics, supply chain optimization)
• Hi-tech (DevOps AI, test automation, code intelligence)
• Manufacturing (predictive maintenance, quality control, digital twins)

We work with OpenAI GPT-4/4.1, Claude, Gemini, LLaMA, Mistral, and domain-specific models for medical, biotech, and enterprise use cases. Both closed-source and open-source models are supported for flexible deployment.

A Gen AI use‑case workshop is a facilitated session where business and technical stakeholders collaboratively explore AI‑enabled opportunities within their organization. Attendees typically include product managers, data engineers, analytics leaders, and C‑suite decision‑makers who want to identify, prioritize, and align on high‑impact generative AI initiatives tied to measurable business outcomes.

Yes—via our X-Tend Talent Services, we provide:

• Dedicated AI engineers & MLOps specialists
• Data engineers, cloud engineers, QA automation experts
• Salesforce, Adobe, microservices, DevOps, and full-stack developers We offer short-term, long-term, ODC/GCC, or managed team models

We offer 1–3 weeks rapid AI pilots covering:

• Use-case discovery
• Data readiness assessment
• Prototype LLM workflows
• RAG knowledge layer setup
• Success metrics & cost modeling

This enables IT teams to validate value quickly

Yes. We specialize in full-scale production rollouts, including:

• High-availability APIs
• Monitoring dashboards
• Drift detection
• AI governance workflows
• Training & change management

This ensures adoption across real business units.

Yes. Our engineering team builds:

• LLM-powered APIs with FastAPI/Django
• Microservices & event-driven systems
• AI test automation
• AI agents for business workflows
• Cloud-native deployments on AWS, Azure, and GCP

• Deep life sciences and other regulated industry expertise
• Compliance-first engineering
• Strong MarTech + AI integration capability
• Proven ODC/GCC setup experience
• Sustainable, Net Zero-certified global operations
• Rapid deployment with pre-trained accelerators

This uniquely positions us as a full-stack AI, data, cloud, and engineering partner.