Ensemble & deep learning architectures
Gradient-boosted ensembles, deep tabular networks, temporal transformers, and graph neural nets. Architecture selection driven by data structure, not hype — every model shipped with calibrated uncertainty.
Services — AI/ML Solutions
AI and ML architectures that move the metrics your leadership reports on.
We design and ship AI/ML for organizations where precision matters — from ensembles and transformers to RAG and agentic systems, with the rigor that holds up under regulatory, engineering, and executive scrutiny.
8–16w
typical engagement length, staffed entirely with senior practitioners.
100%
of systems ship with documentation, evaluation harnesses, and monitoring.
100%
of consultants have shipped models in regulated industries.
What we deliver
A modern AI/ML toolkit, applied with judgment.
Classical ML, deep learning, foundation models, and agents — we pick the architecture that matches the data and the decision, and we are honest when ML is not the right answer.
Causal inference & experimentation
Double machine learning, uplift and heterogeneous treatment-effect models, synthetic controls, and rigorous A/B platforms. So leadership acts on cause, not correlation.
Recommendation & decision intelligence
Ranking, personalization, and next-best-action systems — built on two-tower retrieval, sequence models, contextual bandits, and reinforcement learning. Every system designed to improve from production feedback.
NLP, document AI & RAG
RAG pipelines with evaluation harnesses, guardrails, and citation traceability. Built on transformer-based extraction, fine-tuned LLMs, and hybrid retrieval — tuned for precision, not just recall.
Generative, multimodal & agentic systems
Agent architectures with tool use and structured outputs, vision-language and speech models, and orchestration patterns built for measurable outcomes — not demoware. Generative capabilities scoped to where they create defensible value.
Foundation model strategy & optimization
Build, fine-tune, or prompt — we help you make that decision with clarity, not bias. Distillation and quantization to manage cost. Hybrid symbolic and ML decisioning for problems where constraints matter as much as predictions.
How we work
Four phases. One accountable team.
01
Frame the decision and the architecture
We start with the decision being made, the cost of being wrong, and the data modality — then choose between classical ML, deep learning, foundation models, or a hybrid. Most failures are framing failures, not modeling ones.
02
Baseline, then earn the complexity
Strong baselines first — linear, tree-based, retrieval. We move to ensembles, transformers, or agentic systems only when the lift is real, measurable, and worth the operational cost.
03
Evaluate like adults
Backtesting, offline + online evaluation, LLM-as-judge with human review, robustness and adversarial probes, fairness audits, and red-teaming for generative systems — failure modes surfaced while they are still cheap.
04
Ship with monitoring and feedback loops
Every system leaves with a model card, eval suite, decision logs, drift and quality monitors, and the retraining or RLHF feedback loop that keeps it honest as reality moves.
Have a model — or a decision — that needs sharper thinking?
We run scoped pilots that prove the value on a single use case before any platform commitment.