PRACTICE / TECHNOLOGY
Models that land — not demos.
AI engineered for production.
We design and build AI systems that integrate into real operating environments. Predictive models, language interfaces, computer vision, and decision tooling — each tied to your domain data, your risk tolerance, and the metrics you actually report on.
- 01Machine Learning Models
- 02Natural Language Processing
- 03Computer Vision
Machine Learning Models
Custom ML models trained on your data to automate decisions, predict outcomes, and surface patterns at scale.
DELIVERED WITHPython · scikit-learn · PyTorch
Natural Language Processing
Assistants, retrieval, summarisation, and classification systems tuned to your domain language and policy.
DELIVERED WITHTransformers · LangChain · spaCy
Computer Vision
Inspection, detection, and visual-data pipelines — from prototype model to deployed inference service.
DELIVERED WITHOpenCV · YOLO · TensorRT
Predictive Analytics
Forecasting models that surface risk, demand, and opportunity before they hit the operating layer.
DELIVERED WITHXGBoost · Prophet · Databricks
Generative AI
LLM-powered copilots and content systems tied into your knowledge base, with guardrails and audit trails.
DELIVERED WITHOpenAI · Anthropic · LangChain
MLOps & Deployment
Training, evaluation, deployment, and drift monitoring pipelines built for the operating reality of models.
DELIVERED WITHMLflow · Kubeflow · Vertex AI
02 / ENGAGEMENT SPINE
How an artificial intelligence engagement actually runs.
Five phases — each with a clear deliverable so the progress is checkable, not vibes. Phases overlap in practice; the rail is sequence, not gates.
- 01
Discover
Map the decision the model is meant to support. Audit data availability, label quality, and the risk envelope.
- Use-case brief
- Data audit
- Guardrails frame
- 02
Design
Choose the model family and the evaluation that will tell us it works. Define where the human stays in the loop.
- Architecture
- Eval harness
- HITL design
- 03
Engineer
Build the pipeline end-to-end — ingestion, training, inference, monitoring, and the feedback loop.
- Training pipeline
- Inference service
- Monitoring
- 04
Deploy
Ship behind feature flags with rollback, latency budgets, and an on-call runbook for the first ninety days.
- Canary rollout
- Runbook
- SLO targets
- 05
Operate
Continuous evaluation, retraining triggers, and drift detection. Models age — the operating posture acknowledges it.
- Drift monitoring
- Retrain triggers
- Quarterly review
03 / TOOLCHAIN
What we reach for on artificial intelligence engagements.
Tools are choices, not commitments — substitute per your environment. The grouping below is the shape of the stack, not a vendor list.
LANGUAGES
- Python
- TypeScript
- Go
- Rust
FRAMEWORKS
- PyTorch
- TensorFlow
- Hugging Face
- LangChain
- LlamaIndex
CLOUD
- AWS Bedrock
- Azure ML
- Vertex AI
- Modal
DATA
- Snowflake
- Databricks
- BigQuery
- Pinecone
- Weaviate
OPS
- MLflow
- Weights & Biases
- Kubeflow
- Datadog
- Langfuse