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🤖 PRODUCTION-READY AUTONOMOUS AI

AI Agents That Work While You Sleep

Stop managing infrastructure manually. Deploy production-grade autonomous agents powered by Claude 3.5 Sonnet, Llama 3.1, and Agentic frameworks that self-remediate incidents, automate operations, and handle complex workflows—not demos or experiments. Real autonomous systems running 24/7 on your infrastructure, delivering measurable ROI in 3 weeks.

70%
Tasks Automated
3 Weeks
To Production
150+
Deployments
Schedule Live Demo → Explore AI Agents ↓
✓ 3-Week Deployment
✓ ROI Guaranteed
✓ 24/7 Support
AI Agent autonomous operations dashboard
WHY SAHI TECHNOLOGIES

Production Systems, Not Science Experiments

Most AI consulting firms sell expensive pilots that never reach production. Sahi Technologies builds autonomous agents that deploy in 3 weeks, handle real workloads, and deliver measurable ROI from day one.

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Fast Deployment

3-week timeline from kickoff to production. No 6-month discovery phases or endless POCs. We use battle-tested components and proven methodologies.

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Proven Value Delivery

Strategic investment in intelligent automation delivers significant operational value through improved response times, enhanced incident automation, and accelerated team productivity.

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Real Production Use

Our agents handle 450+ incidents monthly in actual production environments for real companies—not theoretical demonstrations or sandbox environments.

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Safety First

Multi-layered safety mechanisms: approval workflows for high-risk actions, automatic rollback, complete audit trails, and gradual autonomy ramp-up.

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Measurable Results

Track decision accuracy, resolution times, operational improvements, and incident rates through comprehensive dashboards. Average: 95%+ accuracy, 82% faster MTTR.

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Ongoing Support

Sahi Technologies provides continuous monitoring, optimization, and updates. Your agent improves over time through machine learning and feedback loops.

AI AGENT PORTFOLIO

Choose Your Intelligent Workforce

Every Sahi Technologies AI agent is built on proven LLM architectures, trained on domain-specific data, and battle-tested in production. Deploy individually or combine multiple agents for maximum impact.

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SRE Agent - Infrastructure Autopilot

Autonomous Site Reliability Engineering that monitors, diagnoses, and remediates infrastructure incidents without human intervention

Live in Production 70% Auto-Resolution 3-Week Deploy

What It Does

Sahi Technologies' SRE Agent continuously monitors your infrastructure across AWS, Azure, or GCP, detecting anomalies, diagnosing root causes, and executing remediation—all autonomously. When your API server's CPU spikes at 3 AM, the agent investigates logs, identifies the problematic code deployment, rolls it back, and documents everything before your engineers wake up.

Autonomous Capabilities

  • Auto-restart crashed services: Detects container crashes, service failures, and process terminations—restarts automatically with health verification
  • Disk space management: Monitors storage utilization, safely deletes old logs and temp files, archives data to S3 when thresholds are reached
  • Memory optimization: Identifies memory leaks, recycles problematic processes, adjusts JVM heap sizes based on usage patterns
  • Database query optimization: Analyzes slow queries, recommends index creation, identifies N+1 problems in application code
  • Traffic management: Routes traffic away from unhealthy instances, triggers auto-scaling during load spikes, manages blue-green deployments
  • Predictive alerting: Uses ML to predict failures 30-60 minutes before they occur, enabling proactive remediation

Technology Stack (Latest & Future-Ready)

LLMs: Claude 3.5 Sonnet, Llama 3.1 405B, GPT-4 (multi-model for optimal performance)
Agentic Frameworks: LangChain, CrewAI, Anthropic SDK with tool use for real-time execution
Monitoring: Datadog, Prometheus, CloudWatch, Azure Monitor, OpenTelemetry
Knowledge Base: Pinecone + Weaviate vector databases with 50K+ runbook entries and real-time learning
Infrastructure APIs: AWS SDK, Azure SDK, GCP SDK, Kubernetes API, Terraform, Pulumi, multi-cloud orchestration
Future-Ready: Multi-agent orchestration, real-time execution environments, autonomous decision-making with human verification loops

Real-World Results

82% Faster incident resolution
70% Incidents auto-resolved
70% Auto-remediation rate

📖 Read full case study: How a SaaS company reduced MTTR from 4 hours to 45 minutes

Investment

$35K - $50K

One-time implementation

  • ✓ 3-week deployment to production
  • ✓ Full integration with your tools
  • ✓ Custom runbook library
  • ✓ Team training included
  • ✓ 90 days of optimization
$3K - $5K/month

Ongoing monitoring & optimization

Schedule SRE Demo →

Deployment Timeline

Week 1: Infrastructure assessment, monitoring setup, agent configuration
Week 2: Shadow mode testing, decision accuracy validation, team training
Week 3: Production deployment with gradual autonomy ramp-up
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Support Agent - Customer Success AI

Intelligent customer support system handling tier-1 and tier-2 tickets across email, chat, and messaging platforms

Live in Production 82% Resolution Rate 2-Minute Response

What It Does

The Sahi Technologies Support Agent transforms customer success operations by handling routine support queries with human-like understanding and empathy. Integrating with Zendesk, Intercom, or Freshdesk, it responds to customers in seconds, resolves common issues autonomously, and escalates complex problems to human agents with full context.

Support Capabilities

  • Natural language understanding: Comprehends customer intent across 12 languages, handles slang, typos, and emotional language appropriately
  • Password resets & account management: Verifies identity through security questions, resets passwords, unlocks accounts, manages access permissions
  • Billing inquiries: Integrates with Stripe/payment systems to generate invoices, explain charges, process refunds, update payment methods
  • Product guidance: Answers feature questions using RAG retrieval from documentation, creates step-by-step tutorials, shares relevant help articles
  • Technical troubleshooting: Diagnoses common technical issues, provides troubleshooting steps, validates solutions with customers
  • Sentiment-based escalation: Detects frustrated or angry customers through tone analysis, escalates to human agents with conversation summary

Integration Ecosystem

LLM: Claude 3 Opus for nuanced conversation and empathy
Ticketing: Zendesk, Intercom, Freshdesk, Help Scout
Knowledge Base: RAG with product documentation, FAQ database, historical ticket resolution patterns
Billing: Stripe API, PayPal, Recurly integration for payment inquiries
Multi-channel: Email, live chat, in-app messaging, Slack

Proven Impact

2 min Average response time
82% Autonomous resolution
+18 NPS point increase

📖 Read case study: EdTech platform scales 5x without adding support staff

Investment

$30K - $45K

One-time implementation

  • ✓ 4-week deployment timeline
  • ✓ Knowledge base training
  • ✓ Multi-channel integration
  • ✓ Conversation design
  • ✓ Performance optimization
$2K - $4K/month

Continuous improvement & support

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Deployment Timeline

Week 1-2: Historical ticket analysis, knowledge base construction, conversation flows
Week 3: Shadow mode with human review of all responses
Week 4: Production launch handling 30% of tickets, gradual ramp to 80%+
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Compliance Agent - Security Autopilot

Continuous compliance monitoring for HIPAA, GDPR, SOC 2, and PCI-DSS with automated drift detection and remediation

Live in Production 100% Compliance Real-Time Scanning

What It Does

Sahi Technologies' Compliance Agent eliminates the stress of quarterly audits by maintaining continuous compliance across your cloud infrastructure. It scans every resource 24/7, detects policy drift instantly, automatically remediates violations, and generates comprehensive audit documentation.

Compliance Coverage

  • HIPAA compliance: Encryption at rest/transit verification, access control validation, audit logging checks, PHI data handling policies
  • GDPR requirements: Data residency verification, consent management validation, right-to-deletion workflows, cross-border transfer checks
  • SOC 2 controls: Access management, change tracking, incident response documentation, vendor management, system availability monitoring
  • PCI-DSS standards: Network segmentation validation, cardholder data encryption, access restrictions, vulnerability scanning
  • Policy drift detection: Real-time monitoring for configuration changes that violate security policies
  • Automated remediation: Immediate correction of violations like unencrypted S3 buckets, overly permissive security groups, missing MFA

Technical Implementation

LLM: Claude 3 for policy interpretation and compliance reasoning
Cloud Integration: AWS Config, Azure Policy, GCP Security Command Center
Infrastructure as Code: Terraform state analysis, CloudFormation drift detection
Ticketing: Jira integration for remediation tracking
Reporting: Automated audit report generation with evidence collection

Compliance Results

100% Compliance maintained
95% Audit time saved
Real-time Violation detection

📖 Read case study: HealthTech company achieves 100% HIPAA compliance

Investment

$40K - $50K

One-time implementation

  • ✓ 5-week deployment timeline
  • ✓ Compliance framework mapping
  • ✓ Policy automation setup
  • ✓ Audit trail configuration
  • ✓ Remediation workflows
$4K - $6K/month

Continuous compliance monitoring

Schedule Compliance Demo →

Deployment Timeline

Week 1-2: Compliance requirement mapping, baseline policy definition, AWS Config setup
Week 3-4: Agent training on compliance controls, shadow monitoring
Week 5: Production activation with automated scanning and alerting
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FinOps Agent - Cost Optimization AI

Intelligent cloud cost management that identifies waste, recommends optimizations, and implements approved changes automatically

Live in Production Continuous Optimization 24/7 Monitoring

What It Does

The Sahi Technologies FinOps Agent continuously analyzes cloud spending patterns, identifies waste, recommends optimizations, and automatically implements approved changes. It's like having a dedicated FinOps team working 24/7 to reduce your cloud bills—without the $200K+ hiring cost.

Cost Optimization Features

  • Anomaly detection: Identifies unusual spending spikes within minutes, alerts teams before costs snowball, analyzes root causes
  • Rightsizing recommendations: Analyzes 30 days of utilization data, recommends optimal instance sizes, calculates exact savings per resource
  • Reserved instance optimization: Identifies underutilized RIs, recommends conversions, suggests new RI purchases based on usage patterns
  • Idle resource cleanup: Detects unused resources (unattached volumes, idle load balancers, forgotten snapshots), estimates waste, schedules cleanup
  • Budget forecasting: ML-powered spend predictions, early warning of budget overruns, what-if scenario modeling
  • Automated optimization: Implements approved changes like deleting old snapshots, stopping non-prod environments, storage tier transitions

FinOps Stack

LLM: GPT-4 for cost analysis and optimization recommendations
Cloud Cost Data: AWS Cost Explorer, Azure Cost Management, GCP Billing
Visualization: Custom dashboards with Grafana, integration with CloudHealth
Automation: Python scripts, AWS Lambda, Terraform for infrastructure changes
Reporting: Weekly savings reports, monthly FinOps reviews, budget alerts

Financial Impact

35% Average cost reduction
$15K+ Monthly savings typical
90 Days To full optimization

📖 Read our cloud cost optimization guide for detailed strategies

Investment

$25K - $40K

One-time implementation

  • ✓ 3-week deployment timeline
  • ✓ Cost analysis & baseline
  • ✓ Optimization automation
  • ✓ Dashboard creation
  • ✓ Team training on FinOps
$3K - $5K/month

Continuous optimization & monitoring

Schedule FinOps Demo →

ROI Example

Monthly cloud spend: $50K baseline
Expected savings: 35% = $17.5K/month
Annual savings: $210K
Investment: $40K + ($5K × 12) = $100K
Net Year 1 benefit: $110K (110% ROI)
HOW IT WORKS

Sahi Technologies 3-Week Deployment Process

We've perfected AI agent deployment through 150+ implementations. No six-month timelines, no endless POCs—just proven systems that ship fast and deliver immediate value.

01

Week 1: Discovery

Days 1-2: Infrastructure assessment, integration point mapping, success metrics definition

Days 3-5: Agent configuration, knowledge base construction, staging deployment, tool integration setup

✓ Configured agent in staging
✓ Integration testing complete
✓ Success KPIs defined
02

Week 2: Testing

Shadow Mode: Agent observes real alerts, recommends actions, no production changes. Team reviews and validates accuracy.

Validation: Simulate known incidents, verify correct diagnosis and remediation, tune decision logic, achieve 95%+ accuracy.

✓ 95%+ decision accuracy validated
✓ Team trained on agent
✓ Safety mechanisms tested
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Week 3: Production

Gradual Rollout: Days 1-2 observation only, Days 3-4 low-risk automation enabled, Days 5-7 full autonomy with guardrails

Close Monitoring: Sahi Technologies engineers monitor 24/7 during first week, tuning based on real-world performance.

✓ Live in production
✓ Handling real incidents
✓ Delivering measurable results

Ready to Deploy AI Agents in Your Infrastructure?

Schedule a 30-minute discovery call with Sahi Technologies. We'll analyze your infrastructure, demonstrate agents in action, provide custom ROI calculations, and outline your 3-week deployment roadmap. No commitment required.

Schedule Free AI Demo →

✓ See live production agents | ✓ Custom ROI calculation | ✓ 3-week deployment guarantee