Services
AI engineering services with evidence you can inspect.
From consulting and custom AI development to edge vision and long-context systems — engagements start with operating constraints and end with a reviewable delivery path. Public models and research artifacts support capability claims; client case studies appear only when disclosure is approved.
Service pages describe what we can deliver and how we work. They do not invent client logos or ROI. Where we have public proof — Hugging Face models, Arena challenges, engineering notes — we link to it.
AI Development
Build production AI systems — models, pipelines, and integrations — with evaluation discipline from day one.
View serviceAI Consulting
Technical discovery, architecture, and evaluation design for teams that need clarity before they scale spend.
View serviceComputer Vision
Edge-ready detection and vision programs for cameras that already exist — with public models you can inspect.
View serviceGenerative AI
LLM applications, retrieval, and long-context systems — including Continuum1-9B when extreme context is the constraint.
View serviceAI Agents
Agent workflows with tool use, evaluation, and operational guardrails — capability offerings, not invented client portfolios.
View serviceAI Research & Development
Scoped R&D: custom models, distillation, long-context research, and feasibility studies with exit criteria.
View serviceCustom Software
Software and product systems that surround AI — APIs, apps, and platforms built for maintainability.
View serviceProduct Engineering
Ship AI features into products with UX, MVP discipline, and production quality bars.
View serviceData Engineering
Pipelines, datasets, and evaluation corpora that make AI systems measurable and maintainable.
View serviceCloud & DevOps
Inference infrastructure, MLOps, and edge deployment paths for AI systems.
View serviceTechnology Consulting
Broader technology advisory adjacent to AI — architecture, modernization pathfinding, and technical due diligence.
View serviceBring us the hard AI problem.
Tell us about constraints, data, and what success looks like in production.