Production ML built for measurable outcomes.
Production ML that holds up in the business.
We remove the common blockers before scale.
Common gaps we close
- ROI alignment beyond the demo.
- Data quality and lineage clarity for production.
- Monitoring and drift visibility from day one.
How we deliver outcomes
- We start with a business metric and decision.
- We build systems designed for real operations.
- We own delivery end-to-end, including monitoring.
What you get
Short, clear, measurable outcomes with production-ready delivery.
Business-first ML
Every model drives a decision and a metric.
Production delivery
APIs, pipelines, monitoring, and governance included.
Explainable and safe
Explainable by default with guardrails for high-stakes decisions.
Fast validation
Validate value early, scale with evidence.
Where we deliver outcomes
Common production use cases with fast validation and deployment.
Document Processing & Data Extraction
Turn PDFs, scans, emails, and forms into structured data that flows into CRM, ERP, or BI systems using OCR and NLP.
Fraud, Abuse & Anomaly Detection
Identify fraud, abuse, and abnormal behavior in transactions, claims, orders, or logs using anomaly detection and graph-based ML.
Predictive Maintenance & Asset Health
Anticipate equipment failures and asset degradation from sensor and telemetry data to reduce downtime and optimize maintenance schedules.
Forecasting & Capacity Planning
Forecast operational load (calls, orders, shipments, energy demand, warehouse volume) to plan capacity and workforce with confidence.
Process Bottlenecks & Automation
Expose process bottlenecks from system event logs and automate repetitive steps using process mining with RPA and ML.
Visual Quality Inspection
Detect defects, damage, labeling errors, and safety violations in images and video using computer vision inspection models.
Featured case studies
Production ML systems built for measurable outcomes.
Jet Tracker
Public ATC audio to tail numbers, ownership inference, and destination likelihood for market signals.
- Real-time feed of identified business jets from public radio traffic
- Aircraft-to-operator resolution based on historical flight patterns
USDDub
Canonical market data and signals that stay consistent across sources and time.
- Normalized market data across providers
- Dashboards that stay consistent over time
Internal LLM Copilot (Policies + Tools)
A secure assistant that answers questions and triggers internal tools with guardrails.
- Search and Q&A with citations
- Tool execution for internal workflows
What you get early
Fast validation before scale. Delivery timelines are set to scope and integrations.
First 30 days
- Data and feasibility audit
- Impact map with top 1-2 use cases
- Baseline metric and success definition
- Delivery plan with scope and dependencies
First 90 days
- Working proof with measurable baselines
- Integration plan + MLOps checklist
- Decision gate based on evidence
- Clear path to production delivery
Small team. Full ownership.
We are a partner team with end-to-end ownership.
Define the decision and metric
Align on what changes and how success is measured.
Audit data and constraints
Check feasibility, data quality, and constraints.
Build and validate a proof of value
Baseline results with clear decision criteria.
Ship to production
APIs, pipelines, integration, and monitoring.
Iterate on outcomes
Improve based on real performance in the field.
Who ships with you
Team composition adapts to the project, but the core pod stays tight and senior.
Built for real-world risk
Governance is built in for ML that touches operations.
Data security first
Access control, isolation, and compliance-friendly setups.
Explainability by default
Transparent decisions with extra guardrails for high-stakes workflows.
Monitoring and drift control
Quality checks, alerts, and rollback plans.
Clear ownership
Handover, documentation, and long-term support.
FAQ: straight answers
Clear answers to support your decision.
What engagement model do you offer?
A dedicated partner pod accountable for outcomes from discovery through production.
How long do projects take?
Timelines are set to scope and integrations, with clear 30/90-day milestones.
How do you price engagements?
Outcome-led scope with fixed milestones, with time-and-materials available when needed.
Who owns the IP and the models?
You do. Full transfer, documentation, and handover.
How do you handle sensitive data?
Strict access controls, isolation, and compliance-friendly setups.
Do you integrate with our stack?
Yes. APIs, pipelines, and MLOps are part of delivery.
Do you use LLMs and classic ML?
We use the simplest effective approach, including LLMs, classic ML, and rules where they fit.
Do you support after launch?
Yes. Monitoring, retraining, and ops support.
Align on the outcome.
Start shipping impact.
Tell us the outcome you want to improve and we'll reply with a clear plan.