AI development services: LLM apps, RAG and AI agents
InfoDive Labs is an AI development company in India that builds LLM applications, RAG systems, AI agents and custom machine learning models, and runs them in production with monitoring, evaluation and retraining, not just demos.
We build, train, and deploy ML models that survive production. From NLP and computer vision to predictive analytics and AI automation, every system ships with monitoring, drift detection, and a path to continuous improvement. Our engineers have deployed models across healthcare, fintech, legal tech, and e-commerce: environments where accuracy directly impacts revenue and compliance. We treat model development the same way we treat software engineering: version-controlled, tested, reviewed, and observable in production.
What we deliver in this practice.
Custom ML model development & training
We build bespoke models trained on your proprietary data and evaluated against your actual business KPIs, not benchmark accuracy on public datasets. Every model ships with documented performance baselines, failure mode analysis, and a retraining schedule tied to data freshness requirements.
Natural Language Processing
Classification, entity extraction, summarization, and retrieval-augmented generation (RAG) pipelines for document-heavy workflows. We handle multilingual requirements, domain-specific vocabularies, and the prompt engineering that makes LLM integrations reliable rather than impressive-in-a-demo.
Computer vision & image recognition
Object detection, segmentation, OCR, and quality inspection systems deployable on edge devices or cloud infrastructure. We optimize for inference latency and model size based on your deployment constraints, whether that is a factory floor camera or a mobile app.
MLOps pipelines
CI/CD for models including automated training, evaluation, and deployment with feature stores, drift monitoring, shadow deploys, and A/B testing harnesses. We build the infrastructure that lets your data science team ship model updates with the same confidence your engineers ship code.
AI-powered automation
Agent runtimes, workflow orchestration, and internal-tool augmentation that replace manual processes with intelligent automation. We focus on high-ROI automation: the repetitive, error-prone tasks where AI reliability is already proven, not speculative moonshots.
Predictive analytics
Forecasting, anomaly detection, and propensity scoring with properly calibrated probability outputs your business teams can actually trust. We build the data pipelines, feature engineering, and dashboards so predictions reach decision-makers in time to act on them.
This service is a good fit when…
You have a manual, repetitive process that is consuming analyst or operator time and you suspect ML could automate 80% of it.
Your data science team has built promising models in notebooks but cannot get them into production with monitoring and reliability.
You are evaluating LLM or RAG-based features for your product and need engineers who have shipped these systems before, not just prototyped them.
You need a vendor-neutral assessment of where AI will actually move the needle in your business versus where it is hype.
Boring tech where possible. Exotic where it earns it.
Four phases. No discovery deck purgatory.
Discovery
We map the business outcome before we touch a model. In a two-week scoping phase, we interview stakeholders, audit available data sources, and define success metrics that tie model performance to business impact. You walk away with a written problem statement, feasibility assessment, and project plan.
Data audit
We evaluate data quantity, quality, labeling strategy, and privacy posture. This phase produces a data readiness report covering gaps, bias risks, and collection recommendations. If labeling is needed, we set up annotation pipelines and quality assurance processes with your domain experts.
Baseline + iterate
We start with the simplest model that beats your current heuristic or manual process, then iterate with better features and architectures. Every experiment is tracked with versioned datasets, hyperparameters, and evaluation results. You see weekly progress reports with clear accuracy and latency benchmarks.
Production wiring
We deploy the inference service with full observability: latency tracking, prediction logging, a golden evaluation set for regression testing, and an on-call runbook for your team. Handoff includes architecture documentation, retraining instructions, and a 30-day support window.
Concrete deliverables.
AI & Machine Learning: common questions
- What AI development services do you offer?
- LLM-powered features and applications, retrieval-augmented generation (RAG) over your own data, AI agents that automate multi-step work, and custom machine learning models such as classification, forecasting and computer vision, each delivered with a production inference service, monitoring and an MLOps pipeline.
- How long does an AI development project take?
- Most engagements run 6 to 16 weeks with one ML lead and one or two engineers. A focused RAG or LLM feature sits at the short end; a custom model with data pipelines, retraining and drift detection sits at the long end.
- Can you take our notebook models or AI prototype to production?
- Yes. A common starting point is a model or prototype that works in a notebook but has no serving, monitoring or reliability. We turn it into a versioned inference service with documented performance baselines, alerting on accuracy degradation and a retraining schedule.
- Which models and tools do you use?
- Whatever fits the problem: OpenAI and Anthropic models for LLM work, open models from Hugging Face where data must stay in-house, pgvector or Weaviate for retrieval, and PyTorch, MLflow, BentoML and Ray for custom models and serving.