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AI & machine learning.
Production, not demos.

We design and ship ML systems end-to-end — data pipeline, training, evaluation, deployment and observability. We pick use cases where the business case is measurable and the model is auditable.

SVC.02

AI & machine learning.

Apply AI where it changes the business case — not as a buzzword. Models that ship, observably, with measurable ROI.

PyTorch TensorFlow LLMs MLOps Vector DBs
// What we deliver
LLM integration & RAG
Model training & fine-tuning
Computer vision pipelines
NLP & document AI
Predictive analytics
Recommender systems
MLOps & model deployment
AI governance & assurance
// How we work

Our approach to
ai & machine learning.

We start with the decision, not the model. Then we build the eval harness, then we ship.

STEP 01

Problem before model

We don't start with a model. We start with the decision your model needs to support — and the cost of being wrong.

STEP 02

Evaluation harness

Before any production deploy, we build the eval set and the harness. You see model performance against your real data, not benchmarks.

STEP 03

Drift detection from day one

Every production model is observed. Drift, latency, cost, and human-override rates are first-class metrics.

STEP 04

Governance & traceability

Every prediction is traceable to model version, training data lineage, and decision logs — defensible for regulated industries.

// What you get

Outcomes,
not output.

We measure success by the business outcomes we unlock — not by tickets closed or hours logged.

Faster operational decisions backed by evidence, not gut feel.
Reduced manual effort on repetitive analytical tasks.
Defensible model governance for regulated industries (finance, defense, aerospace).
// Adjacent capabilities

Related services.

These often ship together with ai & machine learning.

SVC.01

Custom software.

Explore →
SVC.03

Cloud & DevOps.

Explore →
SVC.08

Data & migrations.

Explore →

Have a AI/ML project to scope? Let's talk.

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