AI strategy & integration
Identify the highest-value operating problem and map the system, data, and decision boundary around it.
AI integration / LLM / Machine learning
We take AI from prototype to production, and build the software and data systems around it.
BusinessSoftwareDataModels
- Operations backend
- Active system
- Pixi-EQ
- Active system
- Commerce systems
- Production pattern
- PyThinker Code
- Labs
Connect AI to the way your business actually works.
Each engagement connects operating context to the models, data, and software needed to act.
View all servicesExplore AI strategy
LLM applications & agents
Retrieval, tools, orchestration, guardrails, and human review for useful model-driven workflows.
Machine learning & data
Pipelines, features, evaluation, forecasting, and decision support tied to real operating data.
Custom software
Product interfaces, APIs, workflows, and integrations that make the model layer usable.
Production reliability
Observability, cost controls, failure handling, security boundaries, and iteration after launch.
Selected work.
Each project is framed by its operating problem, technical boundary, and current status.
Backend systemsActive system
Operations backend
A Go backend for CRM and ERP workflows. Customers, orders, inventory, and invoicing live behind one API instead of drifting apart across tools.
View operations backendAgentic RAGActive system
Pixi-EQ
Answers questions from your own documents with cited sources: query planning, multi-step retrieval, grounded generation, and answer verification.
View Pixi-EQBackend infrastructureProduction pattern
Commerce systems
Connects pickup and checkout workflows to Go services, PostgreSQL state, durable jobs, webhooks, and idempotent integration boundaries.
View commerce systemsAI product conceptLabs
PyThinker Code
Explores a coding companion for precise generation, systematic debugging, optimization, and explanation across Python, TypeScript, React, and Go.
View PyThinker CodeWe move from problem → system → production.
Each stage makes ownership, evidence, and system boundaries explicit before scope expands.
Discover
Define the operating problem, decision owner, users, data, constraints, and success evidence.
Design
Set the system boundary, workflow, model authority, human review, and failure behavior.
Build
Connect models to dependable software, data, tools, and interfaces.
Evaluate
Measure task quality, latency, cost, robustness, security, and unhappy paths.
Deploy
Release with explicit ownership, observability, recovery, and rollback paths.
Operate
Use production evidence to improve the system and retire what no longer creates value.
Technical depth in service of a clear outcome.
Business context first.
Architecture starts with the operating decision, its owner, and the conditions that make action useful.
Evidence before adoption.
Models are evaluated against task fidelity, latency, cost, robustness, and failure behavior before they become policy.
Explicit system boundaries.
Data access, model authority, software responsibilities, human review, and failure ownership stay visible.
Production is the product.
Deployment, observability, security, cost control, and iteration are part of the delivered system.