AI / ML Engineering · Service 01

Intelligence that ships

We build AI products that learn from your data, reason inside your domain, and earn their keep in production. From private LLMs to agentic workflows — no demoware, no science-projects, no model worship.

40+Models shipped
6 wkAvg. POC → prod
3.2×Avg. ROI · year 1
ETY engineer with an AI brain visualization
Training87.4%
System4 / 4 healthy
Inference latency42 ms
What we build

From idea to inference, the whole lifecycle.

Three lanes, one team. We move freely across discovery, build and ship — no waterfall handoffs, no "ask the data team" bottlenecks.

Lane 01 · Discover

Strategy & AI Readiness

We start where the value is — not where the buzzwords are. Workshops, data audits, and a written AI thesis with executable roadmap.

  • AI opportunity mapping
  • Data quality audit
  • Build vs. buy framework
  • Risk + compliance review
  • ROI baseline
Lane 02 · Build

Model + System Engineering

Fine-tuning, RAG, agents, evaluation harnesses, MLOps. End-to-end engineering, not a notebook thrown over a wall.

  • Custom LLM fine-tuning
  • RAG & retrieval pipelines
  • Multi-agent orchestration
  • Computer vision & NLP
  • Eval & safety frameworks
Lane 03 · Operate

Deploy, Monitor, Improve

Shipping is day one, not day done. We instrument, watch drift, retrain on a cadence, and report business outcomes — not just model scores.

  • Inference infra & scaling
  • Cost & latency tuning
  • Drift & quality monitoring
  • Continuous retraining
  • Outcome dashboards
Tech stack & platforms

Boring on the inside. Capable tools

We pick tools that survive the boring middle of a project — well-documented, well-supported, well-loved by the people who use them at 3am.

Foundation Models

OpenAIAnthropicLlama 3MistralGeminiCohereStable Diffusion

ML Frameworks

PyTorchJAXHuggingFacescikit-learnXGBoostONNX

Orchestration

LangChainLlamaIndexLangGraphDSPyTemporalAirflow

Vector & Data

PineconeWeaviatepgvectorQdrantRedisSnowflake

Serving & Infra

vLLMTGITritonRayModalSageMaker

Cloud Partners

AWS BedrockAzure OpenAIVertex AIDatabricksLambda Labs

Eval & Observability

Weights & BiasesLangSmithArizeBraintrustHelicone

Languages

PythonTypeScriptGoRustCUDA
Industry impact

AI's value isn't generic. Neither is ours.

We've shipped AI in seven verticals. The pattern is always the same: pick the boring high-volume task, automate it well, measure dollar impact, expand.

Fintech

Fraud scoring in real time, KYC document understanding, conversational underwriting, and AI copilots for analysts that summarize 800-page filings in 90 seconds.

Fraud detectionDoc AIRisk modeling

EdTech

Personalized learning paths, AI tutors that don't just answer but ask back, auto-grading with rubric explanations, and content gap analyzers for curriculum teams.

Tutoring agentsAuto-gradingAdaptive paths

MedTech

Clinical-note summarization, prior-auth automation, radiology triage models, and HIPAA-compliant chat agents trained on your protocols — not generic medical trivia.

Clinical NLPImagingWorkflow AI
How we work

Five steps from boardroom to production.

No 200-page proposals, no "phase 0" theater. A working AI feature in your hands inside six weeks — then we iterate in public.

01
Week 1
Discovery

Stakeholder workshop, opportunity matrix, success metrics agreed in writing.

02
Week 2
Data & Design

Audit existing data, design retrieval / training strategy, scope the v1.

03
Week 3–4
Build POC

Working prototype with real data, evaluated against agreed metrics. Real, not Figma.

04
Week 5–6
Productionize

Harden the system — guardrails, eval suite, monitoring, infra-as-code. Ship behind a flag.

05
Ongoing
Operate & iterate

Drift watch, weekly evals, quarterly retraining, business-outcome reviews.

Where it pays back

Three deployments that compound.

No splashy launch, no podcast tour — just models that've been in production long enough to be boring, and profitable.

Pattern · Fintech · Decisioning

Collections that actually collect.

Uplift model picks the right intervention (SMS, call, lawyer) per delinquent account. Beats rules + intuition, every cohort.

+22%Recoveries
−14%Cost-to-collect
EconMLXGBoostMLflow
Pattern · Retail · Forecasting

Inventory that stopped guessing.

Hierarchical forecast across 8.4k SKUs × 140 stores. Replaced spreadsheet + intuition workflow.

−31%Stockouts
−18%Working capital
LightGBMProphetFeast
Pattern · Logistics · Vision

QA without a QA team.

YOLO + ViT classifier on the line at a packaging plant. Catches mis-labels, deformed seals, missing caps in real time.

F1 0.94Defect class
3 linesLive
YOLOONNXJetson
Why ETY

Engineers who've shipped what we sell.

40+Production AI models shipped across fintech, retail, logistics, and healthcare.
92%Of models we shipped are still serving production after 12 months.
3.2×Median ROI in year one across applied-AI deployments.
6 wkAverage time from POC to operations dashboard.

Ready to put AI to work?

Book a 30-minute data audit. We'll either map a clear path to a working data platform — or tell you, honestly, where to start before the platform.