Built with enterprise-grade technology
Trusted by startups and Fortune 500 enterprises
How it works
From first call to production
No slides about the future. We ship a working agent on your data and scale only what proves out.
- Discover· Week 1
Find where agents actually pay for themselves
We start with your P&L, not our roadmap. We shadow your team, map where hours leak to repetitive work, and quantify the cost of delay and error. One measurable win is defined — and we don’t write code until it’s agreed.
Hours mappedCost of delayOne win metric - Prepare· Week 1–2
Connect your data. Keep your stack.
No rip-and-replace. We wire into the systems you already run — CRM, ERP, sheets, docs, DBs — break down the silos, and add a memory layer so agents remember past decisions and hold context across conversations.
CRM / ERP / SheetsMemory layerContext pipeline - Validate· Weeks 2–4
Prove ROI on your data, or walk away
A working agent on your real data and workflows, measured against the win we defined. Hit the benchmarks and we scale. Miss them and you stop here — no sunk cost, no theatre demo on dummy data.
Your data · your workflowsHit metric or stop - Refine· Week 4–5
Your team breaks it before customers do
Early users test in real workflows. We fix the unglamorous stuff — prompt edge cases, handoff friction, and that one spreadsheet format that always breaks. Accuracy and UX are tuned before anyone external sees it.
Real usersEdge cases fixedHandoff tuned - Operate· Ongoing
Ship with a safety net
Accuracy alerts, drift detection, and fallback protocols from day one. When your process changes, the agent is retrained. Going live is the middle, not the finish line.
Accuracy alertsDrift detectionFallback ready
Outcomes
Built and Delivered
Production agents deployed for enterprises
Case Study
Margin Optimization Agent
How we helped a QSR chain catch revenue problems across 100+ branches before they became losses.
Faster analysisLive in 2 weeksSame-day fixesDaily alerts
How it works
Analyzes full funnel: impressions → clicks → carts → orders → revenue
Identifies exact problem: visibility, conversion, ops, or pricing
Sends daily branch alerts with corrective actions
Case Study
Multilingual Voice Agent
How we reduced training time for a restaurant chain with an AI assistant in 7+ Indian languages.
7+ languagesFaster onboarding24/7 availabilityLive in 3 weeks
How it works
Voice + chat in Hindi, Tamil, Telugu, Kannada, Bengali, Marathi, English
Instant answers from menus, SOPs, recipes, training guides
Eliminates repetitive manager questions during onboarding
Case Study
Multi-Agent Recruitment System
How we automated 75% of resume screening, freeing HR for strategic work while improving candidate quality.
75% time savedZero candidates missedLive in 3 weeks
How it works
Resume extraction → Social validation → GitHub analysis → Scoring
HR focuses on pre-scored top candidates only
Replicates thorough recruiter evaluation at scale
Why teams stay
Built for outcomes, not demos
Agents that are reliable the Monday after launch — not just impressive in a sales call.
Weeks, not quarters
POC in 3–4 weeks on your real data. Production within 1–3 months. You see ROI before the next planning cycle.
Your stack, not ours
We plug into what you already run — CRMs, ERPs, spreadsheets, DBs. No migration project required to see value.
Measured, not promised
Every agent reports time saved, throughput gained, and capacity freed. If it doesn’t move a metric, it doesn’t ship.
It's like adding a hiring coordinator. Manual screening, social validation and interview scheduling which consumed 8+ hours weekly. The multi-agent system cut that by 75%. Our team now reviews only the top 15% and focuses on strategic hiring and initiatives, not admin work.
— Ananya
By industry
Not generic. Built for your workflow.
Same engine, different wiring. We shape agents around how your industry already operates.
The problem
Product recommendations miss personalization, driving cart abandonment. Spreadsheet inventory causes stockouts. Manual competitor scraping wastes hours.
How agents fix it
AI agents deliver real-time personalization, track competitor pricing, generate visuals instantly, and predict restocking needs.
Personalized recommendationsCompetitor intelligenceInventory optimizationVisual generation
What we build
Agents shaped to the job
Pick the capability, we handle the wiring. Each agent maps to a business outcome you can measure.
🎙️
Voice Agents
Automate customer support, sales, and service ops with multilingual call handling.
Voice Agents
24/7 lead qualification
Automated appointment booking
Call transcription and scoring
⚙️
Workflow Agents
Eliminate repetitive tasks across your existing systems without custom integration.
Workflow Agents
Process automation
Task routing
Exception handling
📊
Analytics Agents
Query sales data, inventory metrics, and customer analytics conversationally.
Analytics Agents
Answer business questions instantly
Generate automated reports
Spot trends and patterns
🔍
Enterprise Search
Your entire knowledge base becomes instantly searchable and queryable.
Enterprise Search
Search all company documents
Extract contract details
Answer compliance questions
🧑💼
Recruitment Agents
Screen resumes, rank candidates, schedule interviews — cut hiring time.
Recruitment Agents
Screen and score resumes
Schedule candidate interviews
Rank applicants by fit
📈
Forecasting Agents
Predict demand, optimize inventory, and reduce waste using your historical patterns.
Forecasting Agents
Sales forecasting
Demand planning
Risk assessment
🛡️
Monitoring Agents
Monitor operations in real-time, flag anomalies before they escalate.
Monitoring Agents
Real-time monitoring
Anomaly detection
Custom alert triggers
🏗️
Custom Solutions
Off-the-shelf doesn't fit? We build agents for your workflows and industry constraints.
Custom Solutions
Industry-specific logic
Designed for your stack
Scale as you grow
Comparison
AI agent vs chatbot vs workflow automation
Choosing the right abstraction matters. An AEO-ready summary for quick extraction:
| Capability | Chatbot | Workflow automation | AI agent |
|---|---|---|---|
| Behavior | Reactive Q&A | Deterministic steps | Goal-directed, tool-using |
| Memory | None | Stateful | Persistent + reflection |
| Handles exceptions? | No | If pre-coded | Adapts via reasoning |
| Best for | Support Q&A | Rule-based flows | Multi-system judgment work |
Source: CloudRaft production experience across QSR, HR, finance, and cloud-native platform engineering. See What is an AI agent? for definitions.
What our customer say about us
CloudRaft played a key role in establishing robust, industry-standard DevOps practices for Enkrypt AI. Their expertise in scalable enterprise Kubernetes deployments, networking, monitoring, and operations ensured seamless customer deployments. Their reliable support significantly strengthened our platform, and we highly recommend CloudRaft.
Prashanth Harshangi
CTO, Enkrypt AI
You guys were awesome to deal with and quickly understood our problem, and implemented a solution. Appreciate your support and we will keep you in mind for future engagements.
Joseph Vinikoor
Director, IFF
CloudRaft supported Rezolve.ai in upgrading Kubernetes and Postgres while safely managing live production infrastructure. They researched relevant technologies, provided valuable recommendations, and delivered the project with professionalism and dedication. We highly recommend CloudRaft.
Udaya Bhaskar Reddy
CTO, Rezolve.ai
CloudRaft have carved out a niche and established themselves as leaders in Observability consulting. At KloudMate, we are thrilled to collaborate with them, together aiming to set newer standards for APM and Observability practices, for businesses across the board.
Pranab Buragohain
CEO, KloudMate
CloudRaft demonstrated strong Red Hat OpenShift backup expertise and quickly earned our trust. They resolved complex configuration issues, set up a reliable test environment with replicated production data, and delivered a successful outcome. Their professionalism and commitment make them a partner we would confidently engage again.
Ran Livneh
CTO, WeRTech Israel
Our AI Success Stories
How a 100+ Branch QSR Chain Automated Root Cause Analysis with Agentic AI
We helped a 100+ branch QSR chain cut performance diagnosis from days to minutes — using a custom data pipeline, Power BI analytics, and an agentic AI system that monitors every branch daily.
Read Case Study →Multilingual Voice AI assistant for QSR kitchen operations
CloudRaft built a multilingual voice AI assistant for a 100+ branch QSR chain in India, giving kitchen staff instant answers from SOPs and recipes in their own language, using Sarvam BulBul v3 and GraphRAG.
Read Case Study →
Building AI Cloud for India
Discover how CloudRaft helped a datacenter build an AI cloud platform using cloud native technologies improving scalability, cost-effectiveness, and performance.
Read Case Study →
Our Insights
Jan 29, 2026
Context Graphs for AI Agents: The Complete Implementation Guide
Context Graph is a knowledge representation framework that enables AI systems to understand relationships between data. Learn how Context Graphs improve reasoning, reduce hallucinations, and power enterprise AI.
Context Graph
Feb 27, 2026
Best AI Agent Frameworks in 2026
Discover the best AI agent frameworks for enterprise in 2026 and learn how to choose the right one for your use case, team, and production requirements.
AI
Dec 23, 2024
AI Web Agents: The Future of Intelligent Automation
Discover how AI Web Agents and Large Action Models are revolutionizing automation with intelligence, adaptability, and seamless efficiency
AI
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