Build intelligent AI-powered systems that automate workflows, optimize operations, and modernize enterprise processes at scale.
A live view of Fortinetics' distributed AI processing fabric — intelligent agents, cloud regions, and enterprise workflow pipelines operating in concert across every client environment.
Intelligent end-to-end automation for complex enterprise workflows using multi-agent AI systems.
Deploy autonomous voice agents that handle calls, support tickets, and customer interactions 24/7.
Enterprise-grade cloud infrastructure with automated CI/CD pipelines and zero-downtime deployments.
We're building the future of enterprise AI. Come shape it with us.
Intelligent event management with AI automation and predictive analytics.
Predictive revenue and demand forecasting powered by advanced ML models.
AI lead scoring and intelligence platform for B2B sales optimization.
Book a free consultation. We'll map your automation opportunities and build a custom AI roadmap.
Headquartered in Hyderabad, we build intelligent AI systems that power the future of enterprise operations.
Fortinetics Solutions was founded with a singular mission: make enterprise-grade AI automation accessible to every business. From Hyderabad's thriving tech ecosystem, we've grown into India's premier AI automation company, serving clients across 12+ industries.
We don't just implement AI — we architect complete intelligent systems that learn, adapt, and continuously improve your business operations.
Every solution starts with intelligent automation at its core. We don't retrofit AI — we architect for it from day one.
Our team continuously researches the latest AI advances, ensuring our clients always benefit from cutting-edge capabilities.
Security, reliability, and scalability are non-negotiable. Every system we build is production-ready from day one.
While rooted in India, our vision and client base is global. We build systems that operate at international standards.
We embed with your team, understand your business deeply, and become long-term technology partners — not just vendors.
Every project is measured against real business outcomes. ROI, efficiency gains, and cost savings — we track it all.
Operating from the heart of India's IT capital, delivering world-class AI solutions globally.
How a group of passionate engineers from Hyderabad's tech ecosystem set out to redefine enterprise AI — and why we're only getting started.
Fortinetics was founded in late 2024 in Hyderabad by Doddapuneni Pavan — a builder frustrated by the chasm between enterprise software promises and actual AI delivery — with one mandate: build differently. Not consultancy-first. Not research-first. Engineering-first.
Pavan had seen firsthand how enterprises wasted crores on "AI transformation" projects that shipped as glorified spreadsheets. The gap between what modern AI could do and what was being deployed at scale was staggering. He built Fortinetics to close that gap — fast, funded with ₹1,00,000 Cr+ in external backing, and with no tolerance for pilot purgatory.
By early 2025, the full product ecosystem was live, the first international client was signed in Singapore, and enterprise automation mandates flooded in faster than we could hire. Today, we're 28,700+ team members strong — remote, hybrid, and on-site — operating across 12 industries and scaling aggressively worldwide.
To make enterprise-grade AI automation accessible, measurable, and transformative for every business — not just the ones with 10-figure budgets.
A world where every enterprise workflow has an intelligent AI layer — autonomous, adaptive, and continuously optimizing itself without human bottlenecks.
Every line of code is written with intelligence in mind. We don't retrofit AI into existing systems — we architect for it from the first commit.
We run internal AI research sprints every quarter. What our R&D discovers in week one becomes production capability by week eight.
We build systems that surprise clients. If a client can predict exactly what we'll deliver, we haven't thought hard enough about the problem.
AGI-ready infrastructure. Autonomous AI agents. Edge-deployed models. Industry-specific foundation models. We're building the next decade, today.
90% of our clients expand their engagement within 6 months. We embed with your operations and become an extension of your technology team.
One founder. One conviction. ₹1,00,000 Cr+ in external funding secured. Founded late 2024 in Hyderabad — already scaling across India, Singapore, and the UAE.
Pavan founded Fortinetics in late 2024 with a clear diagnosis: India's enterprises were spending crores on AI transformation programmes that delivered glorified spreadsheets. The gap between what modern AI could actually do and what was being deployed at scale was staggering — and fixable.
Within weeks of incorporation, the first client was live. By early 2025, Fortinetics had a full product ecosystem, an international client in Singapore, and a team scaling fast under Pavan's direct operational control. Backed by ₹1,00,000 Cr+ in external funding, Pavan built at a speed that most funded startups never achieve — because he ran every delivery personally.
Pavan is a builder at his core — not a delegator. He writes architecture proposals, sits in on client onboarding calls, reviews infrastructure decisions, and stays on the incident escalation chain. His background spans AI systems design, cloud infrastructure, enterprise automation, and operational leadership — rare as a combined profile in a single founder. That breadth is why Fortinetics ships end-to-end systems, not point solutions.
By mid-2025, Fortinetics had crossed 200 enterprise clients, 28,700+ team members worldwide across remote and hybrid structures, and active operations across India, Singapore, and the UAE. Pavan remains the operating nerve centre of every major delivery — not as a bottleneck, but as the standard-setter the entire team calibrates against.
Five specialist leaders — each accountable for a distinct operational domain. Ownership is explicit. Escalation paths are short.
Built fast, compounded hard — every milestone earned with ₹1,00,000 Cr+ in investor backing.
Documented workflows, clear ownership, and an engineering environment designed around shipping reliable production AI — not velocity theatre.
Every feature is built by a self-contained squad: one AI engineer, one backend, one DevOps, one product owner. No handoffs across silos. Each squad owns its delivery from architecture to production monitoring and incident response.
Every system ships with instrumentation. Prometheus metrics, structured logging, distributed tracing, and Grafana dashboards are acceptance criteria — not afterthoughts. If it's not observable, it doesn't reach staging, let alone production.
No AI model reaches production without a documented model card, a baseline accuracy evaluation, a drift threshold definition, and a sign-off from the Head of AI. Every deployment has a defined rollback trigger and a 30-day post-deployment monitoring schedule.
Async-first by design. Documentation over verbal agreements. All decisions recorded in Notion with owners and timestamps. Bi-annual in-person collaboration sprints in Hyderabad and Singapore. Output is measured, not presence. No performance theatre.
20% of engineering time is allocated to structured AI experimentation. Experiments run in isolated sandbox environments with access to production-grade compute. Three current products began as internal experiments that passed the sandbox review board's promotion criteria.
Monthly infrastructure cost reviews. Quarterly model retraining assessments. Weekly SLO breach retrospectives. Every system has a named owner and a defined improvement target. Operational debt is tracked in the same backlog as feature work — with equal priority weighting.
"Engineers here write the deployment runbooks, own the Grafana dashboards, and get paged when something breaks at 2am. That's not punishment — that's how you build engineers who actually care about operational quality. The systems we build are running real enterprises. The ownership is total."— Karthik Nair, Lead DevOps & Infrastructure Engineer
From your first consultation to autonomous AI running in production — our 6-phase process is engineered to eliminate failure points.
Six production deployments across regulated industries — documented outcomes, real infrastructure, verifiable timelines.
The questions every enterprise buyer asks. Answered directly, without the fluff.
Didn't find your answer? Ask NOVA or reach out directly.
End-to-end AI automation, cloud infrastructure, and intelligent systems built for enterprise scale.
Six production-grade AI platforms. Deploy in days, not months. Enterprise security, infinite scale, measurable ROI from week one.
Deep domain expertise across 8 industries, with pre-built workflows, compliance templates, and industry-specific AI models.
Production-grade AI infrastructure spanning five deployment regions — engineered for sub-100ms latency, zero-downtime failover, and sovereign data compliance.
Instant deployment on Fortinetics' managed cloud. Zero infrastructure overhead. Auto-scaling, 99.5% SLA, daily backups.
Deploy within your own VPC or on-premise servers. Full data sovereignty. Ideal for banking, healthcare, and government.
Sensitive data stays on-prem; AI workloads run cloud-native. Best of both worlds for regulated enterprises.
Start free, scale as you grow. No hidden fees. Annual contracts include dedicated success engineers.
Get a live demo of any product customized for your industry in 24 hours.
Join a world-class team of engineers, AI researchers, and designers architecting the systems that power tomorrow's enterprises. Fast-moving, deeply technical, high-impact.
Ready to transform your operations with AI? Book a free consultation and our team will map your automation opportunities within 24 hours.
info@fortineticsolutions.in
Location
Hyderabad, Telangana, India
Website
fortineticsolutions.in
Response Time
Within 24 hours
Your intelligent guide to Fortinetics. Ask anything about our services, products, or careers.
Fortinetics operates a battle-tested deployment framework covering every layer of enterprise AI — from initial onboarding and infrastructure provisioning to continuous observability and automated recovery. Built for organisations that cannot afford downtime.
Six operational pillars that govern every client engagement — from day-zero provisioning to long-term optimisation.
We provision multi-tenant AI infrastructure on AWS and GCP using infrastructure-as-code. Every client environment is isolated, reproducible, and deployed via automated Terraform pipelines, eliminating configuration drift from day one.
All client workloads run inside isolated VPCs with zero-trust network policies, encrypted secrets management via AWS Secrets Manager, and automated compliance scanning integrated into every CI/CD pipeline stage.
We design automation systems with long-term operability built in. Each workflow is versioned, unit-tested, and staged through a promotion pipeline before reaching production, ensuring regression-free deployments every release cycle.
Every deployed system emits structured telemetry into a unified observability stack. Full-stack traces, custom dashboards per client, and ML-assisted anomaly detection surface operational issues before they become incidents.
We connect AI systems to existing enterprise stacks — CRMs, ERPs, data warehouses, and communication platforms — using a standardised connector library. Each integration is contract-tested and independently versioned to eliminate breaking-change risk.
Post-deployment, we run scheduled performance audits, model drift evaluations, and cost-efficiency reviews. Infrastructure is right-sized quarterly using load telemetry, and AI models are retrained when performance metrics fall below agreed thresholds.
A structured, phased delivery process designed to reduce risk, maintain stakeholder visibility, and ship enterprise-grade AI on a predictable schedule.
Stakeholder workshops, existing-stack audit, and data pipeline assessment. We produce a signed Architecture Decision Record (ADR) covering model selection, integration topology, security controls, and SLA targets before a single line of production code is written.
Terraform modules instantiate isolated cloud environments across primary and failover regions. Kubernetes clusters, VPC configurations, IAM roles, and secret rotation policies are applied and validated through automated compliance tests before client credentials are issued.
Data connectors, AI orchestration pipelines, and enterprise integration layers are built against contract-tested API specifications. Model fine-tuning runs in parallel on client-representative data in a sandboxed training environment with evaluation checkpoints at each epoch boundary.
A full production-mirror environment receives the release candidate. Business stakeholders run UAT scenarios; our QA automation suite executes 1,400+ regression assertions in parallel. Load testing is conducted at 2× projected peak to validate scaling behaviour before any production traffic is routed.
Production traffic is introduced at 5% via weighted routing rules and monitored for 48 hours against defined SLO thresholds. Gradual ramp to 100% proceeds automatically when error budgets remain healthy. A zero-downtime cutover protocol manages DNS and session continuity throughout the transition window.
Client operations teams are onboarded to the observability dashboards via structured runbook training. Fortinetics retains on-call engineering coverage under the agreed SLA tier. Monthly operational reviews cover performance metrics, cost efficiency, and roadmap prioritisation for the next optimisation cycle.
Real-time telemetry across infrastructure, application, and AI model layers — with client-accessible dashboards and automated incident response.
A six-stage onboarding framework that takes enterprise clients from initial contact to a fully operational AI system in under 38 days.
Stakeholder interviews, tech stack audit, and requirements scoping. We document current workflows and identify the highest-ROI automation targets.
Our architects produce a detailed solution blueprint including model selection rationale, data flow diagrams, and security control mappings.
Automated IaC pipelines stand up isolated client environments across cloud regions with all security baselines applied and verified.
Data connectors, API contracts, and workflow pipelines are developed and contract-tested against the client's existing enterprise systems.
Business users validate against acceptance criteria in a production-mirror staging environment. Automated regression suites run in parallel.
Zero-downtime canary release, full traffic migration, runbook handoff, and on-call SLA activation. Client is live and supported from day one.
The infrastructure components that underpin our 99.98% uptime commitment — designed for graceful degradation and automated recovery.
Workloads are distributed across 4 cloud regions on AWS and GCP. Primary traffic routes to ap-south-1; failover regions activate automatically on health-check failure within 90 seconds.
Kubernetes-native self-healing restarts failed pods within the same zone. Persistent failures trigger circuit-breaker logic and automatic cross-region failover without manual operator intervention.
KEDA-driven horizontal pod autoscaling responds to queue depth and CPU pressure within 45 seconds. Predictive scaling pre-warms capacity before anticipated traffic spikes using historical load patterns.
Transactional databases use synchronous replication across availability zones. Daily encrypted snapshots are retained for 90 days with point-in-time recovery to any 5-minute window within the last 35 days.
Six security disciplines applied uniformly across all client environments — from network boundaries to model-level data access controls.
Every service-to-service request is authenticated and authorised using mTLS with short-lived certificates. No implicit trust is granted by network position. All lateral movement is logged and anomaly-detected in real time.
Credentials, API keys, and certificates are stored in HashiCorp Vault with automated 24-hour rotation. Long-lived secrets are prohibited by policy; violation alerts fire within 30 seconds of detection.
Static analysis (Checkov), container image scanning (Trivy), and runtime policy enforcement (OPA/Gatekeeper) run on every commit and every deployed workload. Non-compliant deployments are blocked at the CI gate.
Immutable audit trails capture all administrative actions, API calls, and data access events. Logs are streamed to a centralised SIEM with 13-month retention and automated alerting on high-severity event patterns.
External penetration tests are conducted bi-annually by accredited third-party security firms. All critical and high findings are remediated within 14 days. Full reports are shared with enterprise clients on request.
Platform controls are mapped to ISO 27001, SOC 2 Type II, and India's DPDP Act requirements. Compliance evidence is continuously collected and made available via a shared security portal for enterprise procurement teams.
Book a 30-minute architecture review. We'll assess your stack, map deployment requirements, and return a timeline with clear milestones.
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