AI & ML-Oriented Product Engineering
Transform data products with advanced AI and machine learning capabilities, including NLP, predictive analytics, and adaptive product experiences.
Six AI/ML Capabilities
From NLP and computer vision to MLOps and real-time inference — full-stack AI engineering.
Natural Language Processing
Text classification, sentiment analysis, entity extraction, summarization, translation, and conversational AI.
Discuss Use CasePredictive Analytics
Churn prediction, demand forecasting, anomaly detection, recommendation engines, and customer lifetime value modeling.
Discuss Use CaseComputer Vision
Object detection, image classification, OCR, facial recognition, quality inspection, and visual search.
Discuss Use CaseMLOps & Model Lifecycle
Model training pipelines, versioning, experiment tracking, automated retraining, A/B testing, and drift monitoring.
Discuss Use CaseLLM Integration & RAG
OpenAI, Anthropic, open-source LLMs — fine-tuning, retrieval-augmented generation, function calling, and agents.
Discuss Use CaseReal-Time Inference
Low-latency model serving, feature stores, online learning, edge deployment, and autoscaling inference endpoints.
Discuss Use CaseAI Across Every Industry
FinTech
Fraud detection, credit scoring, algorithmic trading, risk modeling, regulatory reporting
HealthTech
Medical imaging, drug discovery, patient stratification, clinical trial optimization
E-commerce
Recommendations, dynamic pricing, inventory forecasting, visual search, chatbots
SaaS
Churn prediction, usage analytics, feature adoption, support automation, upsell scoring
Logistics
Route optimization, demand forecasting, warehouse automation, last-mile optimization
Manufacturing
Predictive maintenance, quality control, supply chain optimization, digital twins
Five-Step ML Delivery
Use Case Discovery
Identify high-ROI ML opportunities, assess data readiness, define success metrics.
Data & Model Assessment
Data quality audit, feature engineering, baseline models, feasibility validation.
Prototype Development
Rapid PoC with real data, stakeholder demos, accuracy benchmarks, go/no-go decision.
Product Integration
API layer, monitoring, feedback loops, CI/CD for ML, feature flags, rollback.
Evaluation & Iteration
A/B tests, drift detection, automated retraining, business impact measurement.
Fixed-Price AI Packages
From PoC to production platform. GPU costs estimated separately. GST (18%) extra.
ML PoC
- Single use case
- Data assessment
- Baseline models
- PoC demo
- Accuracy report
- 4-6 weeks
Production ML System
- End-to-end pipeline
- MLOps setup
- Monitoring & alerts
- Retraining automation
- API + docs
- 3-month support
AI Platform
- Multiple use cases
- Feature store
- Model registry
- Multi-team access
- SLA guarantees
- Source code
- 12-month support
Have Questions?
It depends on the problem. Some use cases work with 1,000+ labeled examples; others need 100,000+. We assess your data during discovery and can use transfer learning or synthetic data for low-data scenarios.
Both. We start with APIs (OpenAI, Anthropic) for speed, then fine-tune or train custom models when you need cost control, data privacy, or domain-specific performance.
We implement drift detection (data & concept), automated retraining pipelines, champion/challenger A/B testing, and business metric monitoring. Alerts trigger when performance degrades.
Yes. We collaborate with in-house ML engineers, provide architecture guidance, and can handoff with full documentation. We also upskill teams on MLOps best practices.
We optimize for cost: right-sized instances, spot instances for training, serverless inference, model quantization, and caching. We provide cost estimates upfront.
Build Intelligent Products
50+ models in production. <50ms inference. Full MLOps. Fixed-price packages.