Maayan AI Platforms help organizations scale artificial intelligence with speed, trust, and consistency. As enterprises adopt machine learning, GenAI copilots, and agentic automation, one challenge appears again and again: AI work becomes fragmented. Teams run isolated experiments, build one-off pipelines, duplicate tools, and deploy models inconsistently. The result is higher cost, slower delivery, greater risk, and solutions that cannot scale across departments.
Maayan Technologies solves this by building AI Platforms that support the Live Enterprise vision—organizations that are AI-powered, digitally agile, continuously learning, and resilient. An AI platform is the “operating system” for AI inside the enterprise. It standardizes how data is accessed, how models and GenAI applications are built, how deployments are governed, and how performance is monitored. It enables many teams to innovate quickly while protecting security, privacy, and brand integrity.
AI platforms are not just a collection of tools. They are engineered foundations that integrate five critical layers:
Data and Knowledge Layer – trusted data, metadata, catalogs, and enterprise knowledge for RAG and search.
Build and Experiment Layer – notebooks, SDKs, feature stores, prompt management, evaluation harnesses.
Deploy and Serve Layer – model serving, GenAI gateways, API management, and scaling.
Govern and Secure Layer – identity, access, policy enforcement, audit trails, approvals, and responsible AI.
Operate and Improve Layer – monitoring, drift detection, cost visibility, and feedback loops.
Maayan AI Platforms are designed to remove friction between experimentation and production. We help organizations move from “a few pilots” to an AI factory—where teams can create, deploy, and improve AI solutions repeatedly with predictable quality and measurable outcomes. Whether you are starting with a foundational MLOps platform, a GenAI platform for copilots and knowledge search, or a unified enterprise AI platform that supports multiple business units, Maayan builds platforms that are practical, scalable, and adoption-driven.
We design the target platform architecture aligned with your business goals, environment constraints, and governance requirements.
Platform vision aligned to the Live Enterprise model
Current state assessment: tools, workflows, maturity, governance gaps
Target architecture: data/knowledge, ML build, GenAI build, deploy, monitor, secure
Platform scope: centralized vs federated model, shared services vs self-service
Toolchain selection guidance: vendor options, open-source tradeoffs, integration plan
Implementation roadmap with phased rollouts and quick wins
Platform KPI framework: adoption, deployment cycle time, cost, reliability, compliance readiness
Deliverables: Platform Blueprint, reference architecture, roadmap, governance plan, KPI model.
MLOps is the foundation for reliable ML in production. We implement the systems and processes needed for repeatable deployments.
Data-to-model CI/CD pipelines
Model registry and artifact management
Automated validation and evaluation checks
Approval workflows for release to production
Monitoring for drift, performance, and data quality
Retraining pipelines (scheduled or trigger-based)
Standardized templates for model packaging and serving
Outcome: faster, safer model deployment with predictable operations.
GenAI at enterprise scale requires more than an LLM API call. We build a secure and governed GenAI foundation:
RAG Services: ingestion pipelines, chunking strategies, embeddings, vector databases, retrieval tuning
Enterprise Knowledge Search: citations, source traceability, and role-based access boundaries
Prompt Management: approved prompt libraries, versioning, templates by use case
Evaluation Harness: relevance testing, hallucination checks, red-teaming workflows, regression tests
GenAI Gateway: routing, policy enforcement, rate limits, logging, cost tracking
Agentic Runtime: tool-calling patterns, human-in-the-loop approvals, action audit trails
Multimodal Enablement: where relevant—document understanding, image-based workflows
Outcome: scalable and trustworthy GenAI deployment across multiple functions.
AI platforms must connect to enterprise systems reliably. We build the “connective tissue” that allows AI services to operate inside business workflows.
APIs and connectors for ERP, CRM, MES, ITSM, data warehouses, and enterprise apps
Event-driven pipelines for real-time triggers
Metadata integration: catalogs, lineage, governance enforcement
Data access controls aligned to identity and role policies
Feature store integration for consistent training and inference inputs
Outcome: AI becomes usable inside operations—not isolated in data science environments.
We implement scalable serving infrastructure that supports both ML models and GenAI services.
Model serving endpoints (batch and real-time)
Autoscaling and performance tuning
Model routing, A/B testing, canary releases
Caching strategies for GenAI and retrieval systems
API management integration and secure access patterns
Multi-tenant runtime design for multiple teams
Outcome: reliable, scalable AI services with controlled releases.
Maayan embeds governance into platform design so teams can move fast without increasing risk.
Identity and access management: SSO, RBAC, least privilege
Audit logging and traceability: who used what data/model/prompt and when
Data protection: encryption, secrets management, network segmentation
Responsible AI policy enforcement: approvals, documentation, fairness checks where relevant
Model risk management: classification of models by risk and governance level
Compliance readiness: documentation templates, evidence collection, reporting mechanisms
Outcome: enterprise trust and compliance-ready AI operations.
Platforms must improve over time. We implement observability and cost discipline as standard.
Monitoring dashboards: latency, error rates, throughput, user adoption metrics
Model and retrieval quality monitoring: drift, relevance degradation, hallucination patterns
Cost visibility: usage by team, project, use case; cost alerts and budgets
Feedback loops: user rating signals, human review pipelines, continuous refinement
Operational runbooks: incident response, rollback strategies, upgrade procedures
Outcome: stable operations, controlled cost, and continuous performance improvement.
A platform succeeds when teams use it. We build onboarding and self-service capabilities:
Template projects for common use cases
Standard SDKs, APIs, and internal documentation portals
Developer portals for datasets, models, and GenAI services
Training and enablement programs for platform users and administrators
Guardrail-by-default workflows so compliance is automatic, not manual
Outcome: higher adoption, faster onboarding, and consistent delivery across teams.
Maayan AI Platforms are built differently—designed not just for technical completeness, but for adoption, trust, and measurable value.
We build platforms that support real-time adaptation and continuous learning. This means:
feedback loops and monitoring baked into the platform
operational metrics that inform continuous improvement
scalable patterns that support many AI products, not just one
This creates a platform that becomes smarter and more valuable with use.
Many organizations build separate stacks for ML and GenAI, creating duplication and inconsistent governance. Maayan integrates:
MLOps (models, training, retraining)
LLMOps (prompts, evaluation, retrieval monitoring)
Agentic runtime (actions, approvals, audit trails)
So teams can build across AI modalities with one governed foundation.
We design governance as an enabler—policy-as-code, automated checks, and approval workflows that align to risk level.
low-risk use cases move fast with standard guardrails
high-risk use cases get deeper controls (documentation, explainability, reviews)
This approach keeps teams agile while maintaining leadership confidence.
We recommend toolchains based on fit, not fashion. We integrate with your existing ecosystem and avoid unnecessary complexity. The result is a platform that is realistic to operate long-term.
Maayan brings proven platform components and templates:
reference architectures for different environments
pipelines and templates for standard deployments
evaluation harnesses for GenAI and ML
onboarding playbooks and operational runbooks
This helps clients move from blueprint to working platform faster.
We treat platform adoption like a product:
strong developer experience
self-service capabilities
clear documentation and training
measurable adoption KPIs
This ensures the platform becomes the default way teams build AI.
Maayan AI Platforms create measurable improvements across speed, reliability, cost, and governance.
Shorter cycle time from idea → prototype → production
Faster onboarding for new teams and projects
Reduced time spent rebuilding pipelines and infrastructure repeatedly
Consistent deployments with standardized CI/CD and monitoring
Reduced production incidents through controlled releases and observability
Better model performance over time via drift monitoring and retraining loops
Lower cost per use case through shared platform services
Improved utilization of infrastructure resources
Cost visibility and budgeting through FinOps and usage tracking
Audit-ready AI operations: traceability of models, prompts, and data access
Reduced GenAI risk through evaluation frameworks and guardrails
Stronger security posture with policy enforcement and access control
Faster scaling of AI across functions (support, sales, operations, finance, HR)
Higher adoption of AI solutions due to trust and usability
A repeatable “AI factory” that continuously delivers value
Maayan AI Platforms is designed for organizations that want outcomes fast, without sacrificing trust, security, or scalability.
At Maayan Tech, we are committed to providing exceptional service and support. Your questions, feedback, and inquiries are important to us, and we will do our best to respond promptly. Whether you’re a business seeking technology solutions or an individual with a query, we are here to assist you.
