Engineers who ship AI to production
We have shipped AI/ML in production for banks, insurers and enterprises since the GPT-3 era — before "GenAI" became a marketing label. Our AI/ML engineers are full-stack: they own model selection, MLOps, evaluation, drift monitoring and the integration into your operating systems.
Models & frameworks
- Generative AI / LLMs — OpenAI GPT-4 / GPT-4o, Anthropic Claude (Opus, Sonnet, Haiku), Google Gemini, AWS Bedrock, Azure OpenAI, Llama, Mistral.
- Orchestration & RAG — LangChain, LlamaIndex, Haystack, custom retrieval pipelines with hybrid search.
- Classical ML — PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM.
- Vector & search — Pinecone, Weaviate, Qdrant, pgvector, OpenSearch, Elasticsearch.
- MLOps — MLflow, SageMaker, Vertex AI, Kubeflow, Weights & Biases.
- Data engineering — Airflow, dbt, Spark, Kafka, BigQuery, Snowflake.
Where we deploy
- GenAI agents & copilots — underwriter co-pilots, customer service agents, RFP responders, internal knowledge agents.
- ML pricing & rating engines — insurance pricing, credit risk, dynamic loan terms, churn prediction.
- Intelligent document processing — KYC, claims, policy documents, contracts. OCR + LLM hybrid with human-in-the-loop review.
- Fraud & anomaly detection — banking transactions, claims, identity, behavioural patterns.
- Predictive analytics — churn, default, NPS, capacity planning.
How we engineer it
- Models trained or fine-tuned on your data, deployed in your VPC or ours.
- MLOps from day one — versioned datasets, evaluation pipelines, drift monitoring, rollback.
- Compliance-aware — bias testing, explainability (SHAP, LIME), regulator-ready audit trails.
- Real evaluation harnesses — not just "vibes-based" demo passes.
Want to engage us?
See our AI / ML Development practice for build engagements, or AI / ML Consulting & Planning for strategy, ROI and MLOps readiness before a line of code.
