Production-readyAI at scale
Custom AI model development, training pipelines, and MLOps infrastructure — from research to production-grade systems that deliver real ROI.
Full-stack AI engineering
Custom Model Training & Fine-Tuning
Train domain-specific models or fine-tune foundation models on your proprietary data for superior performance.
RAG Pipeline Design
Retrieval-Augmented Generation systems that ground LLM outputs in your knowledge base for accurate, factual responses.
MLOps & CI/CD for AI
Automated training pipelines, model versioning, experiment tracking, and continuous deployment for AI systems.
Data Labelling & Curation
Data collection, cleaning, annotation, and augmentation strategies to build high-quality training datasets.
Model Evaluation & Monitoring
Real-time model performance dashboards, drift detection, and automated retraining triggers.
On-Prem & Cloud Deployment
Flexible deployment options — AWS SageMaker, GCP Vertex AI, Azure ML, or your own on-premises infrastructure.
Research to production
Feasibility Analysis
We assess whether AI is the right solution and define success metrics before writing any code.
Data Engineering
Pipeline design, data collection, feature engineering, and dataset preparation for model training.
Model Development
Iterative model training with systematic experimentation, hyperparameter tuning, and validation.
Evaluation & Testing
Comprehensive evaluation against benchmarks, edge cases, and real-world scenarios.
Production Deployment
Containerised model serving with auto-scaling, A/B testing infrastructure, and rollback capability.
Monitoring & Iteration
Continuous monitoring, feedback loops, and scheduled retraining to keep models performing at peak.
Use Cases
- Custom NLP models for your domain
- Computer vision & image classification
- Predictive maintenance systems
- Demand forecasting & planning
- Sentiment analysis & opinion mining
- Anomaly detection & fraud prevention
- Time-series forecasting
- Knowledge graph construction
Tech Stack
Clear deliverables, honest timelines
Deliverables
- Reproducible training pipeline with versioned datasets and configs
- Evaluation harness with offline benchmarks and online A/B framework
- Model registry, packaging (Docker / TorchServe / vLLM), and rollback plan
- Inference infrastructure on your cloud (SageMaker, Vertex AI, GKE, EKS)
- Monitoring for drift, latency, cost, and quality regressions
- Knowledge transfer sessions so your team can own the system
Typical Timeline
- 1Feasibility & data assessment1–2 weeks
- 2Data engineering & baselining2–4 weeks
- 3Model development & evaluation4–10 weeks
- 4Production deployment & monitoring2–4 weeks
Indicative ranges based on typical engagements. Final timelines are scoped during discovery — we don't commit to dates we can't honour.
Fixed Scope
Tightly-scoped milestones with a flat price — no surprise invoices.
Dedicated Team
A senior engineer or pod working with you weekly on a retainer.
Discovery Sprint
A short paid sprint to de-risk requirements before a larger build.
Questions, answered honestly
Need a custom
AI model?
Let's discuss your data and build an AI system tailored to your business needs.
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