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.
ML Systems$20K – $45K
Machine Learning Systems
ML that trains, ships, and stays sharp.
8–12w
Typical timeline
30-day
Stabilization period included
100%
Automated retraining pipeline
<4h
Retraining cycle (vs. days)
THE CHALLENGE
Sound familiar?
- Your model performs well in evaluation but degrades silently in production
- Retraining takes days because there's no automated pipeline
- Data feeds into your system inconsistently — model inputs aren't reliable
- Model versioning is manual and rollbacks have caused production incidents
- You find out about model failures from users, not from monitoring dashboards
OUR APPROACH
How we solve it
We build ML infrastructure as an engineering system, not a research project. Automated training pipelines, reproducible experiments, versioned models, deployment automation, and monitoring built in from the start — so your ML investment stays in production.
What you receive
- Feature engineering pipeline with data validation
- Automated training pipeline with experiment tracking (MLflow or W&B)
- Model version registry with rollback capability
- Inference endpoint with load balancing and autoscaling
- A/B testing framework for model version comparison
- Drift detection and automated alerting
- Runbooks and documentation for your engineering team
OUTCOMES
What you walk away with
01
Models that stay accurate as your data distribution changes over time
02
Retraining cycle cut from days to hours with full automation
03
Production issues caught by monitoring — not by user complaints
04
Full reproducibility — any model version can be rebuilt from scratch
05
Engineering team can ship model updates without production incidents
OUR DIFFERENCE
Why ML Systems from Virinchi
Production-first engineering
Every system is built to production standards from day one — no notebook demos, no prototypes handed off as products.
Drift detection built in
Every ML system we deliver includes monitoring, drift alerting, and automated retraining triggers — from day one.
Domain-specific expertise
We've shipped ML for FinTech, HealthTech, eCommerce, and Industrial clients across four markets. We know your sector's constraints.
USE CASES
Real-world applications
Fraud Detection Systems
Real-time scoring pipelines that flag anomalous transactions with sub-100ms latency.
Demand Forecasting
Time-series ML systems that improve inventory and supply chain decision-making.
Clinical Prediction Models
Validated ML models for clinical risk scoring integrated into EHR systems.
HOW IT WORKS
Our delivery process
01
Data Audit
Review data sources, pipeline quality, and feature engineering gaps. Define scope.
02
Feature Engineering
Build and validate the feature store with quality checks and transformation logic.
03
Training Pipeline
Automated pipeline with experiment tracking, reproducibility, and performance benchmarks.
04
Deployment Architecture
Inference endpoint, versioning, rollback capability, and traffic management.
05
Monitoring & Handover
Drift detection, alerting rules, dashboard setup, and team documentation.
Is this right for you?
Best suited for
- Startups with a first ML model in production that needs proper infrastructure
- Teams spending more time on ML operations than on model improvement
- Companies whose model accuracy is critical to core product quality
- Founders who need ML systems a future engineering team can maintain
Not the right fit for
- Companies without any production data yet
- Teams that need a quick proof-of-concept, not production infrastructure
- Projects where rule-based logic is sufficient for the use case
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
INVESTMENT
Engagement & pricing
$20K – $45K
8–12 weeks
- Includes audit, build, and 30-day stabilization period
- Can take over and improve an existing ML system or build from scratch
- Handover includes documentation and a team knowledge-transfer session
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.
MLOps is the set of practices that make ML models reliable and maintainable in production — covering CI/CD for models, monitoring, retraining, and governance. Without it, models degrade silently.
Yes. We regularly audit, stabilize, and improve ML systems built by other teams. We start with a technical health check before proposing changes.
We build drift detection into every ML system — tracking input data distributions, output distributions, and performance metrics against baseline. Alerts trigger before accuracy degrades materially.
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.
Insights
From our engineering blog
A collection of detailed case studies showcasing our design process, problem-solving approach,and the impact of our user-focused solutions.
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READY TO START?