Build bulletproof AI foundations. Deploy vector databases, orchestration, RAG systems, and monitoring for secure, scalable agents.
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Scalable foundations for real-world AI systems that perform under enterprise demands.
The success of AI doesn't hinge on the model - it hinges on what surrounds it. Only 2% of organizations possess the four foundational technology capabilities required for enterprise AI. Without hardened infrastructure, even the most advanced agents collapse under the weight of fragmented data, unscalable pipelines, and brittle integrations.
We assess and upgrade your technical environment to support AI at scale: vector databases for semantic operations, secure data lakes with automated pipelines, MLOps orchestration frameworks, and comprehensive observability layers.
Our implementations leverage proven technologies like Kubernetes, Apache Kafka, Delta Lake, and specialized vector stores for high-performance operations.
The result isn't just infrastructure - it's confidence. Confidence that your data is unified, your models are auditable, and your systems can scale from pilot to production without architectural rewrites. This service requires 4-8 month implementation cycles, making it essential for organizations where AI represents a core business capability.
Everything else breaks if these aren’t in place.
Unified Data Access Through Lakes
Centralize structured and unstructured data - from APIs and PDFs to databases and file systems - and convert it into vector representations. This enables semantic operations across formats and breaks data silos without rewriting upstream systems.
Fast, Scalable Retrieval
Deploy enterprise-grade vector stores like Weaviate, Qdrant, or Milvus to power similarity search across millions of embeddings with sub-second latency - essential for grounding, personalization, and long-context AI behavior.
Modular, Observable Orchestration
Use LangChain, Temporal, or custom execution engines to build composable, fault-tolerant pipelines. Avoid brittle DAG logic and enable dynamic task routing, contextual memory, and multi-agent workflows.
Secure, Isolated Inference Processing
Run models in containerized or MCP-style environments with enforced GPU allocation, network isolation, and audit logging. This is critical for sensitive domains like healthcare, finance, and regulated SaaS.
Monitoring + AI Intelligence
Integrate real-time observability with Prometheus, OpenTelemetry, or custom dashboards. Detect latency spikes, data drifts, or failure cascades before they degrade agent performance.
Flexible Governance & Access Control
Embed role-based permissions, content filters, API gateways, and usage throttling from day one - not as an afterthought. Prepare now for regulatory requirements like the EU AI Act and ISO/IEC 42001.
Candice Wu
Product Manager, Sisyphus
Measurable Infrastructure Impact
Why infrastructure investment isn’t optional - it’s urgent.
2%
Organizations are infrastructure-ready
Only 2% of companies possess the dynamic compute, secure data architecture, and modular deployment infrastructure needed for scalable, production-grade AI systems.
4.3x
Higher success rate with MLOps
Organizations with mature MLOps pipelines are 4.3× more likely to achieve sustained business value from AI deployments.
80%
Reduction in system resolution time
Companies implementing comprehensive AI infrastructure monitoring achieve 80% reduction in Mean Time to Resolution (MTTR)
42%
Active AI adoption with proper infrastructure
Organizations with properly deployed AI infrastructure report 42% active AI adoption rates compared to 13% industry average.
From fragmented systems to production-grade AI infrastructure.
Infrastructure & System Assessment
Deep audit of current architecture, data flows, and technical constraints
Inventory of existing systems, APIs, and compute environments
Bottleneck and latency diagnostics
Data lake, pipeline, and storage readiness review
Regulatory compliance and security baseline mapping
Fit-gap analysis against AI-readiness architecture templates
Blueprint Design & Stack Definition
Custom infrastructure plan tailored to your scalability and governance needs
Selection of vector DBs, orchestration frameworks, and data storage tech
System diagram for AI pipelines, retrieval logic, and inference endpoints
Integration patterns for MCP-like components and model interfaces
Access control, observability, and failover requirements mapped
Implementation & Integration
Build and integrate the infrastructure backbone
Deploy data lake pipelines and vector indexing services
Set up containerized inference environments with GPU orchestration
Configure orchestration engines (e.g. LangChain, Temporal)
Embed monitoring, tracing, and drift detection
Integrate access control, API gateway, and audit logging systems
Validation & Hardening
Test, optimize, and secure the full infrastructure stack
Load testing, observability stress testing, and fault injection
Redundancy validation and failover simulation
Security testing across data access, model exposure, and pipeline control
Infrastructure-as-code versioning and rollback mechanisms
Knowledge Transfer & Scale Enablement
Ensure self-sufficiency and readiness for long-term growth
Internal team enablement on architecture operations and scaling
Documentation and runbooks for infrastructure ops
Strategy for horizontal and vertical infrastructure scaling
Optional extension into agentic or retrieval system deployment
Everything you need to know about building scalable, secure AI infrastructure with Lunari.