Address
USA | India

Email
info@nexaitech.com

Secure AI Infrastructure Solutions — Built for Trust, Scale, and Compliance

We support SaaS companies, FinTech platforms, BFSI organizations, and product-first engineering teams building compliance-grade infrastructure. Whether you’re delivering AI-driven features, scaling cloud-native or hybrid solutions, or meeting SOC2 and RBI standards- we help you do it with system architecture, not ticket-based code.

As an AI Infrastructure Consulting partner, we deliver reliable, audit-grade systems for regulated domains.

Secure AI Infrastructure Solutions – NexAI
Secure AI Infrastructure Solutions We Deliver

AI & Applied Intelligence

AI Infrastructure Consulting · LangChain pipelines · secure RAG stack · LLM observability · MLOps pipelines · ML lifecycle
We design explainable AI systems with secure foundations. Our delivery spans structured ML and modern LLM pipelines — architected for observability, governance, and scale.

What We Deliver

  • ML models for fraud, credit scoring, churn, and forecasting
  • End-to-end ML pipeline: feature engineering, training, tuning, deployment
  • Secure, explainable LLM pipelines (prompt engineering, RAG, indexing, token control)
  • AI agents for internal copilots, doc routing, and process automation
  • AI agent systems with structured task flows, memory, and external tool execution — built using emerging standards like MCP for cross-system integration
  • Data engineering for AI: dataset pipelines, joins, deduplication, schema validation
  • Feature store design and embedding preprocessing
  • Secure inference APIs for ML + LLM with access control
  • Evaluation pipelines: human-in-loop, drift, scoring
  • Governance-ready architecture with audit logging, guardrails and role-based control

Key Capabilities

  • RAG orchestration with custom retrievers, embedding tuning, and hybrid index strategies
  • Agent workflows with task chaining, tool calling, and context memory
  • Model Context Protocol (MCP) support for interoperable agent-to-tool communication and external system orchestration
  • ML model training (XGBoost, ensemble, deep learning) with drift-resistant pipelines
  • Feature engineering workflows and versioned dataset handling (feature stores)
  • Secure inference APIs for both LLM and ML models (token auth, rate limiting)
  • Prompt + model observability with full evaluation logs and performance dashboards
  • Model explainability using SHAP/LIME + monitoring for fairness/bias
  • Structured API integration with SaaS platforms, analytics layers, and DevOps pipelines
  • SOC2 / ISO 27001 aligned AI pipeline governance and audit readiness

“We build AI like infrastructure — modular, observable, and governed.”

AI Evaluation Framework (PDF)

A concise internal guide outlining how we architect ML and LLM systems with secure inference, observability, and governance built in. Designed for regulated AI delivery.

📥 Download the Framework

Applied AI Insights

Explore how we approach RAG pipelines, agent systems, and ML modeling — with an emphasis on auditability, risk scoring, and real-world deployment.

📚 Read AI Insights

Cloud And Infrastructure

Cloud architecture audit · Terraform + IAM · FinOps dashboard · cloud cost optimization · VPC hardening · multi-tenant cloud stack

We design infrastructure for outcomes — not just uptime. Our systems emphasize performance, operational excellence, and resilience under failure. From autoscaling to audit logs, every layer is built to support teams who need to ship fast and recover faster — without losing control over cost or compliance. We align with standards like the AWS Well-Architected Framework to ensure scalable, secure systems.

What We Deliver

  • Architecture-first infra setup with cloud-native primitives
  • End-to-end CI/CD with rollback safety, promotion workflows, and staging isolation
  • Containerized platforms (Docker, EKS, GKE) or serverless-first systems (Lambda, Step Functions)
  • Auto-scaling policies aligned to workload demand and latency SLAs
  • Structured VPC design, subnets, routing, DNS, and load balancing
  • IAM architecture with org-level policy separation and scoped roles
  • Real-time logging + alerting for app + infra with integrated dashboards
  • Cost controls: budget alerting, tagging enforcement, and forecast reporting
  • Platform health checks, self-healing triggers, and DR planning
  • Environment-specific isolation (dev/stage/prod) with config governance

Key Capabilities

  • Multi-env Infrastructure as Code (Terraform, CDK, Pulumi)
  • GitOps pipelines with promotion gates and rollback safety
  • Progressive deployments: weighted canary, blue-green, SLO-based triggers
  • Intelligent auto-scaling and warm-pool management
  • High-availability architecture with multi-AZ failover and DR readiness
  • Integrated observability: logs, metrics, alerts, health probes
  • IAM design with scoped roles, permission boundaries, and org policies
  • Secure config + secrets management aligned to environment isolation
  • Cost control frameworks: tagging enforcement, budget alerts, usage tracking
  • VPC and network segmentation: NAT, ingress, load balancing, service mesh
  • Deployment hygiene: naming standards, audit trails, changelog workflows

“Moving fast is easy. Moving fast with accountability, rollback, and visibility — that’s architecture.”

Cloud Architecture Blueprint (PDF)

A modular reference used to scope and design resilient, auditable infrastructure — including release automation, environment isolation, and cost tracking from day zero.

📥 Download the Blueprint

Modern Cloud and Infra Insights

Explore how we approach RAG pipelines, agent systems, and ML modeling — with an emphasis on auditability, risk scoring, and real-world deployment.

📚 Read Cloud Insights

Product Engineering

SaaS architecture design · API-first platform engineering · CI/CD hardening · RBAC · secure multi-tenant systems

We help platform teams build reliable, modular SaaS systems with strong identity, observability, and infra alignment from day one — not just MVPs.

What We Deliver

  • Backend architecture for secure MVPs and scalable SaaS platforms, including RBAC, audit trails, and billing hooks
  • Authentication systems: email OTP, OAuth, 2FA, token/session management, and environment-aware access controls
  • Modular backend design with clean domain separation across auth, CMS, AI, payments, notifications, and content
  • Structured API lifecycle including internal/public routes, rate limiting, logging, retries, and versioning — across REST, GraphQL, FastAPI, and inference layers
  • External API integrations with billing (e.g. Stripe), identity, analytics, observability, and operational tools
  • Inference and AI tool APIs: secured prompt routing, model config endpoints, usage-based metering
  • Event-based microservice scaffolding with queues, pub/sub flows, dead-letter logic, and execution guarantees
  • Database design and management: schema strategy, normalization, multitenancy, backup, migration, compliance and archival
  • Operations tooling: admin panels, audit views, override systems, impersonation, log review interfaces
  • File/media system architecture with role-based access, quota enforcement, and metadata handling
  • CI/CD pipelines with integration safety, gated rollout, rollback support, and change visibility
  • System design patterns enforced: event-driven flows, idempotency, retry logic, and architectural boundaries

Key Capabilities

  • Multi-tenant and single-tenant database design with row-level access control
  • Schema migration, indexing strategy, backup policy, and data lifecycle enforcement
  • Modular service architecture with scoped domains and clean separation of logic
  • API lifecycle controls: auth scopes, rate limiting, logging, retries, and versioning
  • Integration of third-party APIs: billing, identity, analytics, CRM, observability, etc.
  • AI inference API scaffolding with secured routing and usage monitoring
  • Asynchronous architecture: job queues, pub/sub events, retry/backoff logic
  • RBAC enforcement across backend, dashboards, and scoped internal tooling
  • CI/CD pipelines with staging environments, feature flags, rollback hooks
  • Internal tool scaffolding: audit panels, override logic, impersonation modes
  • System patterns: idempotency, DTO separation, circuit breaking, and execution tracing
  • Observability-first design: trace IDs, tenant/user logs, metric tagging for feature usage

“We don’t build features. We build systems that evolve, scale, and hold up under pressure — without losing velocity.

SaaS Development Framework (PDF)

A complete reference for structuring secure, modular SaaS platforms — covering auth, RBAC, billing, multi-tenancy, and production-ready API design.

📥 Download the Framework

How We Engineer Platforms

See how we architect backend systems that don’t just ship fast — but scale with clarity, permissioning, rollback, and real-world resilience.

📚 Explore Our Development Process

Security And Compliance

SOC2 SaaS engineering · DevSecOps pipelines · ISO 27001 readiness · audit trail logging · secure LLM deployment · token security

Security isn’t a checkbox — it’s a system of controls. We embed security into infrastructure, application logic, identity flows, and AI operations. From token behavior to logging trails, our architecture aligns with frameworks like SOC2, ISO 27001, PCI DSS, and India’s IT Act — built not just to pass audits, but to survive real risk events.

What We Deliver

  • IAM architecture with scoped roles, policy boundaries, and enforcement at infra, app, and dashboard layers
  • Infrastructure hardening across compute, storage, container, serverless, and network boundaries — with control-plane observability
  • API and data-layer protection: token-based scoping, request rate limits, role-aware input validation, output redaction
  • AI system safeguards: prompt auditing, inference API throttling, token lifecycle control, PII masking, and secure output pipelines
  • Network-layer enforcement: VPC segmentation, ingress/egress control, DNS hardening, and lateral movement isolation
  • DevSecOps pipeline integration with CI/CD hooks for policy scanning, secret detection, and deploy-gate enforcement
  • Penetration testing support (white/black/gray box), including remediation workflows and retest automation
  • Active security operations integration: anomaly detection, log-based alerting, breach simulation (red/blue teaming), and Cortex XDR integration
  • Audit log pipelines across all user/auth/config actions with evidence snapshots, tagging, and export readiness
  • Structured compliance alignment to SOC2, ISO 27001, PCI DSS, GDPR, and India’s IT Act — with controls mapped to systems, not documents
  • Incident posture systems: override tracking, user impersonation controls, alert fatigue thresholds, and post-mortem trail integrity

Key Capabilities

  • IAM design with policy segmentation, role overlays, scoped session behavior, and cross-surface isolation
  • CI-integrated DevSecOps: policy-as-code, secret scanners, deploy-gate checkers, and pipeline-based SAST/DAST
  • Token lifecycle design: expiry logic, rotation, refresh validation, session recovery, and leak boundary mitigation
  • Network control architecture: VPC enforcement, zero-trust egress, and service-bound routing
  • AI system enforcement: inference rate control, output filter chains, input validation, and compliance tagging
  • Full audit log design: identity-event-object traceability with export tagging and time-based filtering
  • Red/blue team test readiness: endpoint visibility, scenario-based playbooks, and anomaly simulation
  • Palo Alto Cortex XDR integration: alert triage, signal correlation, and post-breach analysis
  • Posture dashboards: real-time drift detection, config deltas, non-compliant rule surfaces
  • Evidence pipeline automation: role-based access reports, policy map exports, and action trail snapshots
  • Runtime hardening enforcement: container immutability, signed images, egress traps, and config integrity checks
  • Alert flow design: escalation logic, fatigue mitigation, runbook linkage, and SLA-aware response timers

“We build AI like infrastructure — modular, observable, and governedSecurity is trust, encoded in your system’s architecture — not just in your paperwork.”

Security Hardening Checklist (PDF)

A full-stack guide to IAM, infra, AI, and API-level enforcement — with patterns and controls mapped to real compliance frameworks.

📥 Download the Checklist

Compliance Engineering That Holds Up

Explore how we integrate compliance into delivery — from build-time guardrails to runtime enforcement and post-incident analysis.

📚 Read Security Insights

Our consulting covers the full stack: from architecture design to deployment and monitoring- all part of NexAITechs AI Infrastructure Consulting approach.

Let’s talk strategy.

Schedule a Strategy Call