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Artificial Intelligence and Machine Learning

From prototype to production-grade, accountable AI — applied to problems that move real numbers.

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Overview

Most AI never reaches production. Ours does.

Industry tracking is consistent on this: somewhere between 10% and 15% of AI initiatives make it from notebook to durable production system. The reasons are almost always the same — fuzzy use cases, data that wasn't ready, no plan for monitoring drift, and no story for the auditor when the model behaves unexpectedly. None of those are research problems.

We treat AI as engineering. We pick use cases where the ROI is measurable, we build the data pipeline and evaluation harness before the model, and we ship with the full MLOps loop in place: versioning, CI for models, monitoring, retraining, and human-in-the-loop where the stakes warrant it. For LLM-based systems, we add RAG architecture, guardrails, and the evaluation tooling that distinguishes a demo from a production application.

Responsible AI is not a slide. We align programs to the NIST AI Risk Management Framework, the EU AI Act risk tiers, and ISO/IEC 42001 where it applies — so the system you launch is one your legal team, your customers, and your regulators can actually defend.

Engagement at a glance

  • Use-case triage before model work
  • MLOps in place at v1, not v2
  • RAG & agentic patterns for GenAI
  • NIST AI RMF & EU AI Act aligned

~13%

of ML projects reach production (industry avg)

6–12 wks

First model in production

Drift

monitored on every model, by default

NIST AI RMF

framework-aligned engagements

What we deliver

From use-case triage to retraining loop

AI Strategy & Use-Case Triage

Portfolio scoring on value vs. feasibility, ROI modeling, and a make-buy-fine-tune decision per workload. We kill bad ideas early, on purpose.

Classical ML Engineering

Forecasting, classification, recommendations, anomaly detection. Feature engineering, baselines, evaluation harnesses, and the boring data-cleaning that actually moves model quality.

LLM & GenAI Applications

RAG with re-ranking, function-calling agents, structured output, evaluation suites, and guardrails (prompt-injection defense, PII redaction, output filters).

MLOps Platforms

Versioned data + models, CI/CD for ML, feature stores, deployment with shadow / canary patterns, model registries, and drift-detection on every prediction surface.

Computer Vision & NLP

Document understanding, OCR + extraction, image segmentation, sentiment / intent classification. Built on open models where they fit; fine-tuned where they don't.

Responsible AI & Governance

Bias / fairness audits, model cards, datasheets, red-teaming, and the documented control set NIST AI RMF, ISO/IEC 42001, and the EU AI Act each expect.

How we work

A phased, outcome-driven approach

01
Triage

Value / feasibility

02
Data

Pipeline + labels

03
Model

Baseline → tuned

04
Evaluate

Offline + online

05
Deploy

Shadow → canary → GA

06
Monitor

Drift, retrain, audit

Stack

Open frameworks, frontier models, your data — never the other way around

Languages

Python, R, SQL

Frameworks

PyTorch, TensorFlow, JAX, scikit-learn

MLOps

MLflow, Kubeflow, Vertex AI, SageMaker

GenAI

LangChain, LlamaIndex, DSPy

Vector DBs

pgvector, Pinecone, Weaviate, Qdrant

Models

Anthropic, OpenAI, Gemini, Llama, Mistral

Governance

Model cards, datasheets, evals

Frameworks

NIST AI RMF, EU AI Act, ISO/IEC 42001

Outcomes

What good looks like

Accuracy / F1

On hold-out and online splits

Time-to-production

Weeks, not quarters

Drift coverage

Every prediction surface monitored

$ per prediction

Inference cost tracked as a first-class metric

FAQ

Common questions

Default to API-hosted frontier models for most use cases — the cost / quality / liability math wins. Fine-tune when your domain language or task is genuinely different from anything public; the bar is higher than people expect. Train from scratch almost never makes sense outside a small set of foundation-model labs.

RAG with strict grounding, structured output schemas, evaluation suites that gate every release, and explicit "I don't know" pathways. For high-stakes domains, human-in-the-loop on the surface that takes action. There's no clever prompt that replaces these — they all need to be present.

Use enterprise contracts with no-training, no-retention clauses; PII redaction at the boundary; private deployments (Bedrock, Vertex, Azure OpenAI) when contracts aren't enough; and isolation tiers for data classification. The architecture is straightforward — what matters is verifying it end-to-end.

Most enterprise systems fall into the "limited risk" or "high risk" tiers. High-risk systems (recruitment, credit, critical infrastructure, biometrics) trigger mandatory risk management, data quality, transparency, and human-oversight obligations. We map your systems to the tiering and the implied control set so the gap is visible — and small — before enforcement deadlines hit.

Industries we apply this in

Other services that often pair with this

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Got an AI use case that needs a sober second opinion?

A 30-minute review with our practice lead. We'll tell you whether to ship it, scope it down, or kill it — and what the smartest next step is.