In 2025, most enterprises in India are not “buying cameras.” They are buying a programmable video intelligence layer that must survive five realities:
- Mixed estates: old IP cameras, new IP cameras, and multiple sites
- Constant new use-cases: safety, operations, compliance, and incident response keep evolving
- Higher scrutiny: privacy governance and cybersecurity expectations are no longer optional
- Economics: bandwidth, storage, and maintenance drive total cost more than camera price
- Platform risk: vendor lock-in, update failures, and integration debt can sink adoption
That is exactly why IndoAI is built as a platform, not a single product.
IndoAI’s thesis (introduced early, because it changes how you should evaluate vendors)
IndoAI’s core platform capability is Appization: AI capabilities should be installable and upgradeable like apps, across your entire video estate, including:
- IndoAI Edge AI Cameras (apps can run close to the stream for low latency and resilience)
- IndoAI AI-Enabled Cameras (enterprise IP camera line sold under IndoAI branding, with a consistent IndoAI software and support layer)
- IndoAI Edge AI Box (brings the same Appization layer to existing CCTV networks without replacing cameras)
This is the ecosystem play: one app catalogue, one governance plane, multiple hardware footprints.
The goal of this article is to help enterprise buyers select the right platform and to show what “installable AI apps” must mean in practice, with India-specific considerations and measurable evaluation criteria.
Table of contents
- Why India in 2025 needs “platform + installable AI apps,” not just analytics
- Define “installable AI apps” precisely (so vendors cannot sell buzzwords)
- IndoAI platform architecture (what you are actually standardizing on)
- India economics: why edge and Appization often win on TCO
- The real competitor landscape in 2025 (and how to compare without losing the IndoAI narrative)
- The IndoAI-first selection framework: a procurement-grade checklist (14 questions)
- A practical IndoAI deployment blueprint for enterprises (what “good” looks like)
- Mini case studies (India-relevant patterns, IndoAI-first)
- Future-proofing in 2025: what you should demand for the next 24 months
- Conclusion: the simplest way to choose correctly
- FAQs
Why India in 2025 needs “platform + installable AI apps,” not just analytics
The market is scaling, but the bigger shift is maturity
Analyst estimates differ, but they converge on rapid growth and rising expectations:
- One estimate pegs India’s video surveillance market at USD 4.40B in 2025, reaching USD 7.12B by 2030. Mordor Intelligence
- Another estimate places India video surveillance revenue at USD ~2.03B in 2024 and USD ~5.26B by 2030, with ~17.2% CAGR (2025–2030). Grand View Research
This growth is not just more cameras. It is more stakeholders (security, HR, operations, compliance), more workflows, and more integration expectations.
India’s regulatory direction now directly impacts CCTV decisions
In 2025, two India-specific forces matter when selecting a platform:
A) DPDP Rules, 2025 operationalize India’s privacy regime
The Government of India notified the Digital Personal Data Protection (DPDP) Rules, 2025 on 14 Nov 2025, bringing the DPDP Act, 2023 into operational shape. Press Information Bureau+1
For enterprises, this turns “privacy” into concrete obligations like safeguards, retention discipline, accountability, and auditability.
B) Cybersecurity scrutiny of internet-connected CCTV has tightened
Reuters reported India’s new regulations requiring manufacturers of internet-connected CCTV cameras to submit hardware, software, and even source code for testing in government labs, effective from April 2025, driven by national cybersecurity concerns. Reuters
Separately, STQC documentation and guidance reflects a more formalized certification environment under IoT certification schemes relevant to CCTV. STQC+1
Bottom line: a vendor that cannot show governance controls, update security, and certification readiness is a long-term risk, even if their demo looks good.
Define “installable AI apps” precisely (so vendors cannot sell buzzwords)
Many vendors say “AI apps” when they mean “features.” Enterprises should insist on three proof points.
2.1 Packaging, versioning, and rollback (non-negotiable)
An “AI app” must be a versioned unit that can be installed, updated, and rolled back. If a vendor cannot demonstrate rollback, they are not production-ready for enterprise change management.Milestone’s approach is instructive: Milestone AI Bridge is deployed/operated via Kubernetes or Docker Compose to manage containers. doc.milestonesys.com
That is the direction of mature platforms: packaged deployments with lifecycle controls.
2.2 Compute governance (so one model doesn’t break the system)
If multiple AI apps run on the same edge device (camera or edge box), the platform must manage CPU/GPU/RAM contention and health.
Without resource governance, app ecosystems collapse under real-world conditions: updates cause unpredictable latency, false alerts spike, and operators lose trust.
2.3 Integration surface (APIs/SDKs) that enterprises can rely on
Enterprise adoption depends on integrating video intelligence into existing systems: access control, SOC tooling, ticketing, ERP, and mobile workflows.
Examples of mature integration ecosystems:
- Milestone Integration Platform (MIP SDK) provides open APIs and developer documentation for integrations with XProtect. doc.developer.milestonesys.com+1
- Genetec documents formal integration certification criteria and process, reflecting a quality gate for enterprise-grade integrations. developer.genetec.com+1
A platform without stable APIs becomes an island, and islands don’t scale in enterprises.
IndoAI platform architecture (what you are actually standardizing on)
When evaluating IndoAI, evaluate it like you would evaluate an enterprise platform:
Layer 1: IndoAI Appization (the core differentiator)
Appization means:
- Installable “AI apps” (vision models + business logic + alerting rules + evidence packaging)
- Versioning and controlled rollout (pilot one site, then expand)
- Central governance (roles, audit logs, policy, retention)
- Marketplace-ready ecosystem (internal apps, partner apps, vertical packs)
Layer 2: IndoAI Edge AI Cameras
Best when you need:
- Low latency decisions (entry gates, perimeter, safety)
- Lower bandwidth dependence (process on edge, send events not streams)
- Resilience during WAN outages
Layer 3: IndoAI AI-Enabled Cameras (IndoAI-branded enterprise IP camera line)
These expand hardware choice while staying inside the IndoAI software ecosystem:
- IndoAI branding, IndoAI onboarding, IndoAI support and SLAs
- A consistent IndoAI software and app experience
- Procurement simplification: enterprises can standardize across sites while allowing model choice by budget/tier
Layer 4: IndoAI Edge AI Box (retrofit for existing CCTV estates)
This is critical in India where many enterprises already have installed bases.
- Ingests existing IP camera streams
- Runs the same IndoAI AI apps at the edge-box layer
- Delivers IndoAI alerts, dashboards, evidence, and governance without replacing cameras
IndoAI’s platform advantage is simple:
You can modernize without rip-and-replace, and still build a consistent AI app ecosystem across new and old deployments.
India economics: why edge and Appization often win on TCO
4.1 Bandwidth and storage are the hidden multipliers
1080p streams vary widely depending on scene, motion, and bitrate caps. Axis notes that a typical H.264 1080p 30 fps stream can range roughly 1 to 10 Mbit/s depending on conditions. Axis Help
This matters because a platform choice determines whether you move full streams continuously, or whether you move events and evidence.
Appization economics model:
- Process at the edge
- Convert video to structured events (person detected, intrusion, PPE missing)
- Store full footage locally if needed, but operational workflows run on metadata + short clips
This reduces:
- WAN bandwidth
- Cloud egress/ingress costs (if used)
- Storage write rates
- Operator time wasted on “searching video”
4.2 Governance and auditability are not optional in 2025
DPDP Rules 2025 are now notified and operational. Press Information Bureau+1
Enterprises should prefer platforms where privacy and retention are product capabilities, not manual SOPs.
Also relevant: CERT-In’s 2022 Directions require many entities to enable logs and retain them for a rolling period of 180 days within Indian jurisdiction. CERT-In+1
Even if your CCTV system is not “the” ICT system in scope, enterprise security teams increasingly expect audit logs, traceability, and retention discipline aligned with broader cyber governance norms.
The real competitor landscape in 2025 (and how to compare without losing the IndoAI narrative)
Instead of comparing “camera vs camera,” compare platform archetypes.
Category A: Enterprise VMS ecosystems (strong core, integrations-led apps)
Examples include Milestone and Genetec.
- Milestone provides open platform APIs via MIP SDK. doc.developer.milestonesys.com+1
- Genetec documents integration certification criteria, signaling enterprise-grade expectations. developer.genetec.com+1
How IndoAI competes:
- IndoAI can be positioned as the AI app layer and edge intelligence layer that can coexist with VMS cores where needed.
- IndoAI’s differentiation is the installable app ecosystem across edge cameras and edge box retrofits, not just a central VMS console.
Category B: On-camera application platforms (validates Appization direction)
Axis ACAP explicitly supports building apps that run directly on network devices and emphasizes edge-based computing benefits such as reduced latency and bandwidth. Axis Developer+1
How IndoAI wins:
- IndoAI takes the “apps on edge” concept and makes it hardware-footprint flexible: IndoAI Edge AI Cameras, IndoAI AI-Enabled Cameras, and IndoAI Edge AI Box for existing estates, all governed by one Appization layer.
Category C: Cloud-managed camera platforms and API-first video platforms
Examples: Verkada (Command API), Eagle Eye (Video API).
- Verkada provides a Command API for programmatic interaction and integrations. Verkada Help+1
- Eagle Eye positions a REST-based Video API platform for recording/indexing/storing and building applications. Eagle Eye Networks+1
How IndoAI should position in India:
- India deployments often need edge resilience and predictable cost. IndoAI’s edge-first Appization can deliver cloud-like programmability without forcing cloud economics everywhere.
The IndoAI-first selection framework: a procurement-grade checklist (14 questions)
Use these in demos and RFPs. They are designed to separate “platform” from “features.”
App ecosystem and lifecycle
- Show me a new AI app being installed, not enabled.
- Show versioning: how do you know what is running where?
- Show rollback: can you revert an app in minutes?
- Show staged rollout: one site, then a region, then all sites.
- Show resource controls: what prevents an app from starving others?
Deployment flexibility (this is where IndoAI is structurally advantaged)
- Can the same AI app run on edge cameras and on the edge box?
- Can you extend AI to existing camera estates without replacing them?
- What is your operating mode during WAN outages?
Evidence, workflows, and operator productivity
- What is the evidence package for an incident (clip, snapshots, metadata, timeline)?
- How fast can an operator find “all entries at Gate 2 between 2–4pm where helmet missing”?
- What is the false alert management system (feedback loops, thresholding, suppression, retraining hooks)?
Security, privacy, and compliance readiness (India-specific)
- How do you map data handling and retention controls to DPDP expectations? Press Information Bureau+1
- What is your posture on CCTV cybersecurity testing/certification and secure updates? Reuters+1
- What audit logs exist and how long are they retained; can they align with broader 180-day logging expectations in Indian cyber governance? CERT-In+1
A practical IndoAI deployment blueprint for enterprises (what “good” looks like)
Phase 1: Stabilize and unify (Weeks 1–4)
- Inventory: list sites, camera counts, critical zones, network constraints
- Decide the core plane: IndoAI Appization as the “AI layer” standard
- Deploy IndoAI Edge AI Box for legacy camera clusters where replacement is not immediate
- Deploy IndoAI Edge AI Cameras or IndoAI AI-Enabled Cameras for new critical zones (entry, perimeter, high-risk areas)
Phase 2: App packs that show ROI (Month 2–3)
Start with use-cases that drive measurable outcomes:
- Intrusion/perimeter alerts for after-hours sites
- Safety compliance packs (PPE, restricted zones) for plants and warehouses
- Fire/smoke early warning in high-risk areas
- Vehicle identification and entry workflows for gated premises
Appization matters here because you can add these as app packs without re-architecting the system each time.
Phase 3: Operational integration (Month 3 onward)
- Integrate alerts into workflows (ticketing, WhatsApp escalation rules, SOC dashboards)
- Add role-based access and audit trails
- Create evidence exports and reporting for compliance/incident documentation
Mini case studies (India-relevant patterns, IndoAI-first)
Case A: Multi-site manufacturing group (300 cameras, 6 plants)
Problem: safety incidents and inconsistent enforcement, high cost of manual monitoring.
IndoAI approach:
- Edge AI Box in older plants to modernize without replacement
- IndoAI Edge AI Cameras in new lines and critical hazard zones
- Install PPE and restricted-zone packs as apps; tune thresholds site-by-site; stage rollouts
Economic logic: reduce operator time, reduce incidents, reduce bandwidth by pushing events not full streams.
Case B: Large housing society or township (200 cameras, gates + common areas)
Problem: gate incidents, unauthorized entry, slow investigations.
IndoAI approach:
- IndoAI AI-Enabled Cameras at gates and choke points for better capture quality
- Edge AI Box for remaining legacy cameras
Install apps: vehicle entry analytics, tailgating-type patterns (where relevant), perimeter after-hours
Governance: clear roles for society admin vs security supervisors, audit logs, retention policy aligned to governance expectations.
Case C: Campus (college or enterprise park)
Problem: incidents happen, but evidence is hard to assemble and response is slow.
IndoAI approach:
- Standardize on IndoAI Appization so new apps can be added each semester/quarter
- Use Edge AI Box to extend analytics to existing cameras
- Install apps that map to campus needs: crowding, restricted areas, fire/smoke, incident detection packs
Outcome: faster evidence retrieval and more consistent response workflows.
Future-proofing in 2025: what you should demand for the next 24 months
The next wave is not “more detections.” It is video-to-workflow automation:
- Semantic search over recorded video (search by intent, not timestamps)
- Event summaries, trend analytics, anomaly detection
- “Video agents” that can answer operational questions with evidence and audit trails
To be future-proof, you need:
- Appization lifecycle (installable updates)
- A stable metadata model
- Strong governance and audit logs
- Hardware-footprint flexibility (edge camera, edge box, on-prem, optional cloud)
IndoAI’s platform direction is aligned with this because Appization makes upgrades a product feature, not a project.
Conclusion: the simplest way to choose correctly
If you are an enterprise buyer in India in 2025, choose the platform that gives you:
- A real app lifecycle: install, update, rollback, staged rollout
- A governance plane that can stand up to DPDP-era expectations Press Information Bureau+1
- Security and compliance readiness in a stricter CCTV environment Reuters+1
- Retrofit capability for existing CCTV estates (so ROI is not blocked by replacement cycles)
- A clear 3 to 5-year TCO story driven by bandwidth, storage, and operational workload, not just camera price Axis Help
This is why IndoAI is positioned as a platform: Appization across IndoAI Edge AI Cameras, IndoAI AI-Enabled Cameras, and IndoAI Edge AI Box, with one unified ecosystem and one upgrade path.
FAQs
It means you are buying a platform where AI capabilities are packaged like software apps and can be installed, upgraded, rolled back, and governed across sites and devices. In a real enterprise setting, “installable AI apps” should include:
– Packaging and versioning: each AI capability has a version (v1.0, v1.1) and a release history.
– Deployment control: install an app to a specific site, camera group, or zone.
– Staged rollout: pilot first, then expand, without disrupting operations.
– Rollback: if a new model increases false alerts, you can revert quickly.
– Governance: role-based controls, audit logs, and retention policies.
If a vendor can’t demonstrate versioning + rollback + staged rollout, they are usually selling “features,” not an app-based platform.
Most “AI cameras” are either:
– Single-use case devices (one or two analytics features), or
– Cameras with a few bundled analytics features that don’t evolve easily.
IndoAI is designed as a platform with Appization at the center:
– You can install and upgrade AI capabilities like apps.
– The same AI app ecosystem can run across IndoAI Edge AI Cameras, IndoAI AI-Enabled Cameras, and can extend to existing camera estates using IndoAI Edge AI Box.
– This is critical in India because most enterprises have a mixed camera estate and cannot replace everything to adopt new AI use-cases.
Appization is the idea that AI should work like mobile apps:
– You can “install” an AI capability (for example: PPE detection, intrusion, fire/smoke, vehicle analytics).
– You can “upgrade” it as better versions are released.
– You can “uninstall/disable” it if the use-case changes.
– You can control where it runs and who can access outputs.
Enterprises benefit because they stop treating AI as a one-time project and start treating it as a repeatable, governed capability.
Yes, that is one of the biggest practical enterprise requirements in India. Many organizations already have IP cameras installed.
The IndoAI Edge AI Box approach allows you to:
– Connect to existing camera streams (typically via standard IP streaming methods).
– Run IndoAI AI apps at the edge-box level.
– Get IndoAI alerts, evidence clips, dashboards, and reporting.
– Modernize the system without waiting for full camera replacement cycles.
This is how enterprises adopt AI faster and reduce capex risk.
A mature platform should support multiple deployment surfaces depending on constraints:
– On-camera (edge AI camera): best for low latency and reducing bandwidth.
– On edge box: best for retrofitting existing CCTV and running heavier models near the site.
– On-prem server: useful for larger sites with strong IT infrastructure.
– Cloud: useful for centralized management and some cross-site analytics, but can increase ongoing cost and bandwidth dependency.
IndoAI’s platform thesis is to keep the same AI app ecosystem consistent across these surfaces, so you don’t end up with fragmented tooling.
Common mistakes that cause failure after pilot:
1) Buying a “demo-driven” solution without a rollout and governance plan.
2) Ignoring false positives until operators are overwhelmed and stop trusting alerts.
3) Assuming bandwidth and storage costs won’t matter (they usually dominate TCO).
4) Not planning integration into workflows (ticketing, escalation, evidence handling).
5) Vendor lock-in: choosing a closed system that becomes expensive to expand.
6) Not defining acceptance criteria (lighting, angles, capture quality, thresholds).
A platform evaluation must include deployment, governance, and operations, not just accuracy claims.
Enterprises should demand a structured approach:
– Define the environment: lighting, camera angle, distance, crowd density, uniforms, helmets, occlusion.
– Use a simple measurable framework: precision, recall, false positives per hour/day.
– Run a pilot with:
– A baseline period (no alerts, just logging)
– Shadow mode (alerts recorded but not acted upon)
– Controlled rollout (one zone at a time)
Also insist on operational controls like threshold tuning, suppression rules, and model version rollback.
Operational reliability and governance.
Even a good model fails if:
– It can’t be rolled back after an update
– It spams operators with alerts
– It lacks audit trails
– It can’t be scaled across sites cleanly
– It cannot produce evidence packages that are usable for real incidents
The best enterprise platforms treat AI as a “managed lifecycle,” not a one-time feature.
A realistic TCO model includes:
– Hardware: cameras, edge boxes, on-prem servers (if any)
– Storage: retention duration, bitrate, recording settings, redundancy
– Bandwidth: inter-site and cloud usage (if applicable)
– Software licensing: VMS, AI apps, and per-device costs
– Implementation: deployment, integration, testing, acceptance
– Operations: monitoring, updates, support SLAs, replacements
For multi-site enterprises, the hidden drivers are usually bandwidth + storage + operations effort, not just camera cost.
It means your platform can absorb change:
– New AI apps appear and you can deploy them without re-architecture.
– Models improve and you can upgrade safely with rollback.
– New sites get added fast using templates.
– Governance gets stronger with privacy and audit controls.
– You can adopt emerging features like semantic search and event summarization over time.
Future-proofing is not a promise. It’s a capability: lifecycle management + app ecosystem + integrations.
A platform-grade approach typically includes:
– Site-based policies and templates (HQ policy + site overrides)
– Role-based access control (who can view what, who can export evidence)
– Audit logs of administrative actions
– App version visibility (what runs where)
– Health monitoring (camera uptime, app performance, storage status)
– Staged rollouts and controlled upgrades
For Indian enterprises with 10–100+ sites, governance matters more than raw features.
If your AI use-cases involve personal data (faces, identity, attendance, visitor tracking), you should treat privacy as a formal requirement:
– Clear purpose definition (why you process)
– Access limitation (who can see identity vs generic events)
– Retention rules (how long footage and logs are stored)
– Evidence handling and audit trails
– Vendor accountability for security controls and updates
India’s DPDP regime has been operationalized further through notified rules, so enterprises should prefer platforms that make privacy and retention controllable at the product level rather than SOP-only.
You should almost always start small, but “small” must be designed correctly:
– Pick 1–2 sites with representative conditions (not the easiest site only).
– Select 2–3 AI apps that directly impact ROI (safety, intrusion, fire/smoke, operational incidents).
– Run a structured pilot with acceptance criteria and performance tracking.
– Then scale with templates, app packs, and staged deployments.
A platform like IndoAI is designed to let you expand app-by-app and site-by-site without redesign.
Lock-in typically happens through:
– Proprietary camera-only ecosystems
– Closed APIs and integration barriers
– Data trapped in vendor formats
– High switching cost because apps don’t port
To reduce lock-in:
– Prefer platforms that support mixed estates via an edge layer (like an edge box) and keep your app model consistent.
– Demand APIs for events and evidence exports.
– Ensure you can add new capabilities as apps rather than buying another vendor’s silo.
The goal is not “no lock-in.” The goal is controlled dependency with escape paths.
Ask for five live demonstrations (not slides):
– Install an AI app to a specific camera group.
– Upgrade the app version across 2 sites, and show rollout control.
– Roll back the app version and show audit logs of the action.
– Show evidence packaging: alert + clip + metadata export.
– Show multi-site dashboard: device health + app versions + alert volume.
Then ask for a 3-year TCO model for:
– 100 cameras across 10 sites
– 30-day retention
– 3 AI apps enabled
– Support SLA included
Any vendor who cannot do this is not an enterprise platform in 2025.
