Build and run enterprise-grade AI applications on the platforms that matter. End-to-end architecture, deployment, and managed operations.
Building production AI applications requires more than just picking a platform. You need reference architectures proven in enterprise deployments, deep expertise in MLOps and LLMOps, and the ability to handle real-world complexity: data pipelines, vector databases, retrieval-augmented generation (RAG), evaluation harnesses, fine-tuning infrastructure, and secure landing zones.
We partner with you across the leading AI platforms: IBM watsonx, API Connect, and watsonx Orchestrate for enterprise governance and workflow automation. Anthropic Claude for cutting-edge language models, Google Vertex AI for scale and integrations, Microsoft Azure AI Foundry for integrated cloud-native AI, and AWS Bedrock for serverless foundation models. Whether you're building chatbots, content generation systems, decision-support tools, or specialized ML workflows, we design, deploy, and operate solutions that work.
Our approach is pragmatic: we build on platforms that fit your architecture, skills, and risk tolerance. We establish LLMOps practices from day one—monitoring tokens and costs, evaluating model quality, managing deployments, and enabling teams to iterate safely. The result: AI applications that are production-ready, cost-efficient, and maintainable.
Proven patterns for LLM applications, RAG systems, fine-tuning pipelines, and multi-model orchestration. Scalable, secure, and aligned with your infrastructure.
Seamless integration with IBM watsonx, Anthropic Claude, Google Vertex AI, Azure AI Foundry, and AWS Bedrock. Deployment pipelines, versioning, and rollback strategies.
End-to-end RAG pipeline implementation: document ingestion, embedding, vector database (Pinecone, Weaviate, Qdrant), retrieval optimization, and context injection.
Build production LLMOps practices: prompt versioning, A/B testing, automated evaluation (factuality, toxicity, coherence), monitoring, and cost tracking.
Custom model fine-tuning on your domain data. Techniques: LoRA, QLoRA, supervised fine-tuning, RLHF setup. Evaluation and performance benchmarking.
Enterprise-grade security, compliance, and data governance. VPC isolation, encryption, identity & access management, audit trails, and regulatory alignment.
Production monitoring: latency, token usage, cost, error rates, model drift. Dashboards and alerting for SLA compliance and incident response.
Run-time support, incident management, optimization, upgrades, and continuous improvement. Dedicated team or augment your operations.
IBM watsonx
Enterprise AI platform with governance and model management.
Anthropic Claude
State-of-the-art LLMs with constitutional AI safety principles.
Google Vertex AI
Fully managed ML platform with end-to-end workflow automation.
Azure AI Foundry
Microsoft's cloud-native AI platform with enterprise features.
AWS Bedrock
Serverless API access to foundation models.
Understand your use case, data, scale, and regulatory requirements. Design reference architecture aligned with your platform strategy.
Deploy platform infrastructure, integrate models, set up data pipelines, RAG systems, and evaluation harnesses. Establish LLMOps practices.
Evaluate model quality, optimize latency/cost, fine-tune prompts, and establish baselines. Load testing and security hardening.
Production deployment, monitoring, incident response. Ongoing optimization, model updates, and scaling as demand grows.
8-12
Weeks to Production
Accelerated time-to-market with proven architectures and rapid deployment pipelines.
99.9%
SLA Uptime
Enterprise-grade availability with redundancy, failover, and monitoring.
50%
Cost Optimization
Efficient infrastructure, model selection, and caching strategies reduce operational costs.
100%
Audit & Compliance
Full logging, traceability, and governance for regulatory compliance.
Deploy a conversational AI system for customer support. RAG integration with knowledge bases, fine-tuning on domain-specific language, multi-language support, and sentiment analysis.
✓ 40% reduction in support tickets
Process vast document collections: contracts, research papers, financial documents. Extract insights, summarize findings, and answer complex queries across document sets.
✓ 10x faster document review
Build a recommendation system powered by LLMs. Combine user behavior, product catalogs, and contextual signals. A/B test different models and prompts for optimal engagement.
✓ 25% increase in conversion
Automate creation of marketing copy, social posts, email campaigns, and product descriptions. Brand voice consistency, A/B testing, and quality control via evaluation harnesses.
✓ 70% faster content creation
Build systems that augment human decision-making with AI insights. Synthesize data from multiple sources, explain reasoning, and support different workflows (sales, ops, finance).
✓ 30% faster decision cycles
It depends on your priorities: IBM watsonx for enterprise governance, Claude/Vertex AI for best-in-class models, Azure AI Foundry for Microsoft integration, AWS Bedrock for serverless simplicity. We help you evaluate options based on your use case, existing infrastructure, and team expertise.
Yes. Many enterprises use multiple platforms for different workloads. We design orchestration layers that abstract away platform differences, allowing you to leverage best-in-class capabilities while maintaining operational simplicity.
Smart retrieval is key. We implement chunking strategies, relevance scoring, and hybrid search (keyword + semantic). We also use smaller models for retrieval and larger models for generation, and cache context aggressively to reduce token consumption.
Prompt versioning, A/B testing, automated evaluation (factuality, toxicity, relevance), token tracking, cost monitoring, model performance analysis, incident response workflows, and retraining pipelines. Think DevOps for LLMs.
Absolutely. We handle end-to-end fine-tuning: data preparation, model selection, training infrastructure, evaluation, and deployment. We also build guardrails around data privacy and intellectual property.
Let's design and deploy the right AI platform for your use case, data, and scale. Talk to our AI platform experts today.
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