AI Engineering & Platform Development
Comprehensive AI engineering: LLM, RAG, AI agents, fine-tuning, computer vision, predictive analytics, MLOps, and AI infrastructure. From strategy to pr...
4-8 weeks from prototype to production, depending on complexity
What We Deliver
LLM Integration & RAG Pipelines
Retrieval-augmented generation systems built on your data, with vector databases, embedding pipelines, and semantic search: deployed behind your own infrastructure.
AI Agent Development
Custom AI agents for internal automation, customer support, code review, and data analysis: with guardrails, human-in-the-loop review, and accuracy monitoring.
Model Fine-tuning & Training
Fine-tuned models on your domain data for specialized tasks : classification, extraction, summarization: with benchmark evaluation and production deployment.
AI Platform Architecture
End-to-end AI platform design: inference APIs, model serving, orchestration pipelines, monitoring, and cost optimization for production AI workloads.
Computer Vision
Object detection, image classification, OCR, and video analytics with YOLO, EfficientNet, and Vision Transformer. Deployed on edge devices and cloud.
Predictive Analytics & Machine Learning
Time series forecasting, anomaly detection, customer segmentation, and recommendation systems with XGBoost, LightGBM, and ensemble models.
MLOps & AI Infrastructure
Automated training pipelines, model registry, A/B testing, drift monitoring, and CI/CD for ML. Deployed on Kubernetes with Kubeflow or MLflow.
AI Strategy Consulting
AI readiness assessment, use case discovery, ROI analysis, and implementation roadmap. Build vs buy decisions grounded in your actual data.
Conversational AI & Chatbots
LLM-powered chatbots with multi-channel integration (Web, WhatsApp, Telegram), multi-turn context handling, and backend system connectivity.
Data Engineering for AI
ETL/ELT pipelines, data lakes, feature stores, and unstructured data processing. Data prepared and ready for AI training and inference.
AI Security Testing
Prompt injection testing, adversarial attack simulation, model extraction testing, and data leakage assessment. Remediation report with implementation roadmap.
Speech & Voice AI
Speech-to-text (STT), text-to-speech (TTS), voice agents, and voice sentiment analysis with Whisper, ElevenLabs, and custom models.
Multi-Agent Systems
Multi-agent architecture with role assignment, inter-agent communication, and complex task orchestration. Deployed with LangGraph, CrewAI, or custom orchestration.
Vector Database & Search
Vector database solutions with Pinecone, Weaviate, Qdrant, and Milvus. Optimized embedding pipelines, hybrid search, and semantic caching for production RAG.
Edge AI & On-Device ML
AI model deployment on edge devices: TensorFlow Lite, ONNX Runtime, CoreML. Optimized for low latency and offline operation.
Synthetic Data Generation
Generate synthetic data for model training when real data is scarce or sensitive. Preserve statistical distribution and characteristics of original data.
AI Cost Optimization
Inference cost optimization: quantization, pruning, distillation, caching, and model selection per task. Reduce AI operational costs by 40-70%.
Our Process
AI readiness assessment and use case discovery (Week 1)
Solution architecture and model selection (Week 2)
Prototype development and benchmark testing (Week 3-4)
Integration with existing systems and data pipelines (Week 5-6)
Production deployment with monitoring and guardrails (Week 7-8)
Performance monitoring and iteration (Ongoing)
Deliverables
- ✓AI readiness assessment and use case recommendation
- ✓Production-ready AI pipeline or agent deployed
- ✓Model evaluation report with benchmarks
- ✓Integration documentation and API specs
- ✓Monitoring dashboard for accuracy and performance
- ✓Training documentation for your team
Tech Stack
Frequently Asked Questions
What ROI can we expect from AI engineering?
Most clients see 40-70% reduction in manual processing time within the first quarter. We build a projected ROI model before writing any code.
Do you use open-source models or commercial APIs?
Depends on the use case. We evaluate both based on latency, accuracy, cost, and data security requirements. For sensitive data, we deploy open-source models on your infrastructure.
How do you prevent AI hallucinations in production?
Guardrails, human-in-the-loop for critical tasks, RAG with verified data sources, and continuous accuracy monitoring against your real data.
Do you do computer vision?
Yes. Object detection, OCR, video analytics with YOLO and Vision Transformer. Deployed on cloud or edge devices depending on requirements.
Is AI operational cost optimized?
Yes. Quantization, pruning, caching, and model selection per task. Clients typically reduce inference costs by 40-70% after optimization.
Pairs Well With
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Schedule a free technical consultation to discuss your engineering needs. No commitment, just an honest conversation about your product.