01 · LLM Copilots
Domain-tuned
assistants
Domain-tuned assistants for plant operators, legal teams, analysts, and field engineers. Retrieval-augmented, evidence-cited, with an auditable response trail.
We design and deploy AI that lands in production. LLM-powered copilots, predictive models, and ML pipelines — selected, built, evaluated, deployed, and monitored by a senior team that owns the system through its first year of operation.
Domain-tuned assistants for plant operators, legal teams, analysts, and field engineers. Retrieval-augmented, evidence-cited, with an auditable response trail.
Failure prediction, anomaly detection, and forecast models trained on operational data — vibration signatures, telemetry, transaction streams, claims histories.
Training, evaluation, deployment, and monitoring infrastructure. Reproducible runs, registered models, and rollbacks. The boring scaffolding that keeps AI in production for years.
Production wiring for Anthropic Claude, OpenAI, Cohere, and Azure AI Foundry — with prompt caching, tool use, structured outputs, and fallback policies.
When foundation models aren't the right answer: classical ML, gradient boosting, deep learning, time-series, and reinforcement-learning pilots — when warranted.
Offline and online evals, golden sets, human review loops, red-teaming, and policy compliance — built into the deploy pipeline, not as an afterthought.
If we can't write the kill-criteria, we can't justify the build.
Every AI engagement at Droz begins with a one- to two-week problem-framing exercise. The goal is to identify the decision the AI will change — a maintenance plan, a triage routing, a claim adjudication, a procurement scoring — and the cost of getting that decision wrong today.
From there, we work backwards to data, model selection, integration, and rollout. Most engagements eliminate one or two false starts in this phase: the workload that "sounds like LLM" is actually a structured rules engine; the prediction task that "needs deep learning" actually wants gradient boosting on better features.
The output of discovery is a written brief: the decision, the baseline, the candidate approaches, the data, the rough ROM, and the kill-criteria. Signable; reviewable; auditable.
Foundation model orchestrates the workflow; custom models handle the deterministic sub-tasks. We design both halves and the contract between them.
Reproducible pipelines in Databricks, SageMaker, or Azure ML. Versioned datasets, registered features, tracked experiments. Every model has a lineage.
Offline evals on a golden set + held-out tests; safety / red-team evals; statistical confidence intervals on the headline metric. No model is promoted that hasn't passed.
Canary releases. Shadow runs. Blue-green for online models. Batch refresh for forecast models. Every deploy is reversible in one click.
Drift detection on inputs, outputs, and downstream decisions. Quality dashboards. Human-review queues for low-confidence predictions. Alerts that wake the on-call.
An AI system is only useful when it lives inside the operator's workflow. We integrate models behind a versioned API, with structured logging, role-based access, and an evidence panel the operator can audit.
On the LLM side, we ship tool-use contracts, output schemas (Pydantic on the backend; Zod on the frontend), prompt caching, and graceful fallbacks. On the ML side, we ship batch predictions to your data warehouse, online inference behind an API gateway, and a feature store the model trusts.
We also build the human surface — the dashboard, the alerts, the override controls, the review queue. The point is decision support, not decision replacement.
Anthropic (Claude), OpenAI, Cohere, Azure AI Foundry, AWS Bedrock.
Databricks, Azure ML, SageMaker, Vertex AI.
PyTorch, scikit-learn, XGBoost, LightGBM, transformers, sentence-transformers, LangGraph (where the orchestration warrants it).
Snowflake, PostgreSQL + pgvector, Pinecone, Weaviate, Parquet, Apache Spark, Airflow, Kubeflow.
Cross-references — Full partner profiles · Full technologies · Cloud underlay.
Production LLM copilot for plant operators. Retrieval over equipment manuals, work orders, and condition history. Audit log mandatory.
Production ML models predicting bearing, motor, and rotating-equipment failures from vibration, thermal, and ultrasound signatures.
A Protected-B-pattern triage assistant for a regulator. RAG over policy documents, human-in-the-loop on every recommendation.
Six representative sectors below. The full list of 17 lives on the Industries overview.