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AI/MLDEVELOPMENT

Machine learning that earns its place in production.

Models are the easy half. Data pipelines, MLOps, and governance are why our AI ML development services still work a year after deployment.

Book an AI Readiness Call

Capabilities

What we build

ML, generative AI, computer vision, and MLOps — wired into operations, not slideware.

01

Machine learning

Prediction wired into operations: churn before it happens, credit and fraud risk before it costs, pricing that responds to demand, forecasts your inventory can trust. Every model ships with a baseline, an accuracy target, and a retraining schedule — because unmonitored models decay silently.

Churn predictionFraud riskDemand forecastingRetraining schedules
02

Generative AI & NLP

Document intelligence that reads contracts and invoices, semantic search that understands intent instead of keywords, RAG pipelines that answer from your private data with citations, and assistants trained on your business context. Plus sentiment analysis, chatbots, and voice interfaces.

RAG pipelinesSemantic searchDocument AIVoice interfaces
03

Computer vision & deep learning

Classification, similarity, and defect detection with CNNs and transformer architectures. In production: a diamond-similarity engine combining Random Forest classification, K-Means clustering, GrabCut feature extraction, and HSV/LAB color analysis — 80% accuracy, evaluation 60% faster than expert review.

CNNs & transformersDefect detection80% accuracy60% faster review
04

MLOps & model governance

Version control for models and data, deployment pipelines, drift detection, monitoring dashboards, and audit trails mapped to your compliance needs. This is the difference between an AI feature and an AI liability.

Drift detectionModel versioningAudit trailsDeployment pipelines
AI and machine learning project scoping
When to say no

Wherewe'dtellyounottouseML

Deterministic rules that never change. Datasets too small to learn from. Decisions requiring full explainability under regulation where a model can't provide it.

Deterministic rulesDatasets too smallFull explainability required
Our stance

Honestbeforeyoucommit

We've talked clients out of ML projects — a system that shouldn't exist is expensive at any accuracy.

Computer vision and production ML deployment

Proof

In production: a diamond-similarity engine combining Random Forest classification, K-Means clustering, GrabCut feature extraction, and HSV/LAB color analysis — 80% accuracy, evaluation 60% faster than expert review.

Discuss your project

80%

Vision accuracy

60%

Faster evaluation

MLOps

Governed & monitored

Commonquestions,straightanswers

Less than you fear, more than a spreadsheet. The readiness audit answers it precisely: what you have, what's usable, what's missing, and whether the project should proceed.

A scoped model with deployment: 8–16 weeks. Platform-scale AI: several months. The audit phase (2–3 weeks) fixes the timeline before you commit.

Yes — your cloud, your VPC, or on-premises where compliance demands it. Model and data residency stay under your control.

Both, chosen by the constraint: cost, latency, privacy, and quality trade off differently per use case. Private deployments of open models are increasingly the answer for sensitive data.

Monitored accuracy, automated retraining triggers, rollback paths, load-tested inference, and documentation. If it can't survive your traffic and your auditors, it isn't production-grade.

Nagar Software Solutions

Software and AI that ships — built for teams who need outcomes, not slide decks.

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