AI · Agents · Machine learning

AI systems built around real work.

From data exploration and prototypes to integrations, deployment, and ongoing improvement, we help organizations turn AI and machine learning into useful software.

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What you can commission

A focused contribution or an end-to-end build

Each engagement is scoped to the problem, available data, existing systems, and the degree of operational reliability required.

AI applications and agents

Conversational assistants and domain-specific AI applications
Retrieval over business knowledge and documents
Tool-using agents connected to operational workflows
Structured generation and multi-step processes with human review
Evaluation of answer quality, reliability, and failure handling

Machine learning and data science

Exploratory data analysis and problem formulation
Data preparation, feature discovery, and feature engineering
Classification, regression, forecasting, and predictive models
Model selection, tuning, and business-aligned evaluation
NLP, computer vision, and deep learning when appropriate

Data, integrations, and application engineering

Data pipelines, databases, and retrieval infrastructure
APIs and integration with existing business systems
Web interfaces that make models useful to staff or customers
Structured outputs, validation, and reliable system handoffs

Deployment and ongoing improvement

Model serving, cloud deployment, and containerized applications
Logging, monitoring, evaluation, and maintenance
Attention to permissions, sensitive data, latency, and cost
Documentation, handoff, and iterative improvement

Managed service · Initial pilot

AI phone agent for tree-service businesses

Call intake and estimate scheduling for tree-service owners who can’t always pick up — set up and managed by us.

See the pilot

Engagement process

Understand, test, integrate, improve

01
Understand the problem and success criteria
02
Assess data and systems
03
Prototype and evaluate
04
Integrate and deploy
05
Monitor and improve

Technology

Chosen for the problem

Modeling

Python, SQL, scikit-learn, PyTorch, TensorFlow

AI applications

Major LLM providers, LangChain, structured generation

Application stack

JavaScript, Flask, FastAPI, Django, relational and document databases

Retrieval & operations

Vector databases, Docker, AWS, GCP

Related work

Different systems, complementary capabilities

Pre-release platform

Spirit Bot

Knowledge-based assistants and brand-voice personalization.

View example

Applied platform · Live implementation

Website City Craftsman / DryHack

Content infrastructure, delivery, and measurement.

View example

Creative prototype · Non-production

Novel Forge AI

Configurable long-form generation and product design.

View example

Have a problem worth building for?

Let’s discuss an AI, agent, or machine-learning project grounded in the way your organization actually works.

Discuss a Project Research inquiry