# AI that ships, scales, and keeps delivering.

We engineer AI systems at scale. Predictive ML, computer vision, and LLM and agentic applications, scoped from a real business problem, built for your production environment, and instrumented so ROI is visible from launch through retraining.

## **The production gap**

AI pilots are easy to start and hard to finish. What separates a working sandbox model from a dependable production system is execution discipline: the engineering, data preparation, integration, and MLOps required to move models into production and keep them accurate over time.

### **What we typically see**

- Pilots that stall on production infrastructure, data quality, or organizational resistance.
- Models deployed without MLOps, degrading silently until the damage emerges.
- Use cases that haven't been scoped through engineering, data preparation, and integration.
- Spend on tools and talent disconnected from business outcomes.

## What we build

We are backed by more than a decade of predictive modeling and deep learning experience, the technical foundation that runs underneath everything from classical ML to modern foundation models. We bring the same engineering discipline that produces reliable production models to LLMs, agents, and computer vision.

[**Predictive Modeling**](https://www.onesix.ai/services/artificial-intelligence#design) [**LLMs & Agents**](https://www.onesix.ai/services/artificial-intelligence#build) [**Computer Vision**](https://www.onesix.ai/services/artificial-intelligence#scale)

### **Predictive Modeling**

ML model selection and architecture are driven by the statistical properties of the problem, going far beyond default algorithms, supported by feature engineering, hypothesis testing, and validation with the production infrastructure to match.

- Supervised, unsupervised, and ensemble methods matched to the problem
- Drift detection, retraining triggers, and performance monitoring built in
- Deployed and integrated with the infrastructure to stay accurate over time

**Common use cases:** [Forecasting & Anomaly Detection](https://www.onesix.ai/use-case/forecasting), [Propensity & Risk Modeling](https://www.onesix.ai/use-case/propensity-risk-modeling), [Next Best Action](https://www.onesix.ai/use-case/next-best-action), and [Marketing Effectiveness](https://www.onesix.ai/use-case/marketing-effectiveness)

### LLMs & Agents

Most LLM work stops at the API call. We engineer the full system with retrieval architecture, orchestration, context management, and the monitoring layer that keeps agents accurate as your data evolves.

- RAG, natural language querying, and retrieval architecture built for production
- Agentic workflows with orchestration, guardrails, and context management
- Data and concept drift monitoring so agents execute against current, trustworthy context

**Common use cases:** [Agentic AI](https://www.onesix.ai/use-case/agentic-ai), [Conversational AI](https://www.onesix.ai/use-case/conversational-ai), and [Document Intelligence](https://www.onesix.ai/use-case/document-intelligence)

### **Computer Vision**

Purpose-built models for real operating conditions, trained on domain-specific data, and optimized for the environment where inference actually happens.

- Object detection, classification, and segmentation at production scale
- Optimized for edge, cloud, or hybrid deployment
- Integrated into the workflows where the output gets used

**Common use cases:** [Vision AI](https://www.onesix.ai/use-case/vision-ai)

## How we approach it

### 2-6 months

End to end engagement from use case development to production deployment

### 4 Workstreams

Use case development, model training and validation, MLOps and lifecycle management, application integration

### One Output

Production AI that runs reliably in your environment and delivers measurable business value

We work across the full AI stack, from LLM and agentic applications to predictive models and computer vision, and build the infrastructure to keep everything running after handoff.

## From the field

Innovative thinking. Real outcomes.

### Detecting early cardiovascular risk with AI decision support

### Saving 4-6 hours per proposal with a marketing AI copilot

### Five Platform Highlights from Snowflake Summit 2026

.avif)

### The Production Gap: Why Your AI Agent Needs a Micromanager

## Questions we hear

### What types of AI do you build?

We build across the full spectrum, including predictive models, classification systems, LLM and conversational AI applications, agentic systems, computer vision, anomaly detection, and forecasting. The use case drives the approach.

### Do we need a data foundation in place first?

Generally yes. Reliable, well-modeled data is what every production AI system depends on. If your data layer isn't ready, our Data Foundations engagement can run first or in parallel.

### How do you make sure models stay accurate over time?

Every engagement includes MLOps and model lifecycle management: monitoring, retraining triggers, drift detection, and the metrics that prove production performance. If accuracy slips, you see it before downstream decisions do.

### What's the difference between this and hiring a data science team?

We bring a full team (AI engineers, ML practitioners, project lead) plus reusable accelerators and a delivery process proven across industries. Most clients reach production faster and with less risk than building internally.

### How do you handle LLMs, agents, and generative AI?

We build production LLM applications, agentic workflows, and generative AI systems with the same engineering rigor as predictive models: prompt management, evaluation frameworks, retrieval architectures, and the governance layer that keeps agents reading current context.