RAG vs Fine-Tuning: Choosing the Right LLM Approach for Your Product
Both RAG and fine-tuning improve LLM performance on your specific use case — but they solve different problems. Here's how to choose.
IoT & Edge AI$25K – $55K
Intelligent Connected Systems
Smart systems built for the real world.
<10ms
Edge inference latency
10–14w
Typical timeline
10K+
Device fleet scale supported
100%
Offline-capable architecture
THE CHALLENGE
Sound familiar?
- Your IoT devices generate data but you're not acting on it in real time
- Cloud latency is too high for the time-sensitive decisions your system needs to make
- You have sensor data but no pipeline to turn it into actionable intelligence
- Your connected product collects data — but doesn't do anything with it
- Edge hardware has limited compute and intermittent connectivity
OUR APPROACH
How we solve it
We build end-to-end intelligent connected systems — from sensor data ingestion through edge inference to cloud aggregation. We design for the constraint: if a decision needs to happen in milliseconds on a device with limited compute, we engineer for exactly that.
What you receive
- Edge inference pipeline optimized for target hardware
- Sensor data ingestion and normalization layer
- Real-time anomaly detection and alerting system
- Cloud aggregation layer with historical analytics dashboard
- Device management and OTA firmware update system
- Protocol integration: MQTT, OPC-UA, Modbus, or custom industrial protocols
- Monitoring infrastructure for device fleet health
OUTCOMES
What you walk away with
01
Decisions made at the edge in milliseconds — no cloud round-trip
02
Fewer false alerts through intelligent filtering at the source
03
Connected product that acts on data, not just collects it
04
Cloud infrastructure costs reduced by processing at the edge first
05
Architecture that scales from 10 devices to 10,000 without redesign
OUR DIFFERENCE
Why IoT & Edge AI from Virinchi
Edge-first architecture
We design the edge-cloud boundary before writing a line of code. Decisions that need milliseconds get made on-device.
Industrial protocol expertise
OPC-UA, MQTT, Modbus, CoAP — we speak the language of OT systems and integrate without requiring a full platform replacement.
Domain-specific expertise
We've built IoT AI for Industrial, HealthTech, and Smart Building clients across four markets. We know your sector's constraints.
USE CASES
Real-world applications
Predictive Maintenance Systems
Sensor-driven ML models that predict equipment failure before it happens, reducing downtime.
Quality Control Automation
Computer vision systems on manufacturing lines that detect defects at production speed.
Smart Facility Management
Building automation systems with AI-driven energy optimization and occupancy analytics.
HOW IT WORKS
Our delivery process
01
Environment Audit
Hardware constraints, connectivity patterns, data types, and latency requirements documented.
02
Architecture Design
Edge vs. cloud boundary decisions, data flow design, model selection for edge constraints.
03
Edge Pipeline Build
Inference engine, data normalization, protocol integration, and local storage.
04
Cloud Layer Build
Data aggregation, dashboards, alerting rules, and fleet management.
05
Field Testing
Device testing under real conditions, load simulation, and staged rollout.
Is this right for you?
Best suited for
- Startups building hardware-connected products with AI as a differentiator
- Companies with sensor data streams they aren't currently acting on
- Teams that need edge AI but lack embedded systems expertise
- Industrial and manufacturing startups modernizing legacy operations
Not the right fit for
- Pure software products with no hardware or sensor component
- Projects where cloud latency over 500ms is acceptable for the use case
- Teams without access to the physical hardware during development
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
INVESTMENT
Engagement & pricing
$25K – $55K
10–14 weeks
- Hardware procurement support available — we can specify and source edge devices
- Field testing phase included in all engagements as standard
- Post-deployment monitoring setup included
Ready to start?
Get a detailed proposal within 48 hours. No commitment required.
Frequently Asked Questions
Didn't find what you were searching for? Reach out to us at [email protected] and we'll assist you promptly.
Edge AI means running AI inference on the device or gateway — not in the cloud. This enables real-time decisions, offline operation, and reduced latency for time-critical industrial applications.
We work with NVIDIA Jetson, Raspberry Pi, STM32, and custom embedded platforms. Hardware selection is driven by your compute, power, and cost requirements.
Yes. We build integration layers that speak OPC-UA, MQTT, Modbus, and proprietary industrial protocols. We work with your OT team throughout the integration.
FROM OUR CLIENTS
Built with teams who ship
The team took our AI concept from whiteboard to production in 10 weeks. The architecture they designed handles 10x our expected load with no issues.
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READY TO START?