From prototype to production, AINinza engineers design bespoke models, data pipelines, and user experiences that ship - and stay shipped. LLM copilots, computer vision, AI agents, and generative apps built end-to-end by a senior AI team backed by 10+ years of Aeologic engineering.
Built on Aeologic Technologies, shipping enterprise software since 2015.
Across web, mobile, traceability, government, and AI workloads.
Voice agents, NLP query tools, and document intelligence shipping to real users.
Verified across recent AI and engineering engagements.
We build production-grade AI across nine core capability areas. Every system is engineered for your data, your stack, and your operational reality - not a generic template.
Model selection is a tradeoff, not a default. Here are the model families we work with day-to-day, and the criteria that drive the choice on every project.
Most AI projects fail in the production handover, not the lab. Our process designs the handover in from week one - that's why our builds reach launch.
We're framework-agnostic - model and infrastructure choices follow the use case, not the other way around. Here's what we ship with most often.
A sample of AI systems we've engineered end-to-end. Each links to the full case study with the architecture, model choices, and outcomes.
Cross-industry exposure with depth in regulated and operations-heavy verticals.
Most AI projects ship a great demo and never reach production. The reason is rarely the model. It is almost always one of: data that looked clean in a sample but is messy at scale; an integration that was assumed to be straightforward; a stakeholder who saw the demo but never agreed on what "working" means; or an MLOps story that was someone else's problem until launch week.
AINinza's process is built around that observation. The first phase is a paid discovery workshop, in which we audit your data, walk the integration paths, and write down - with you - the specific criteria that will define success. If those criteria can't be met, we tell you before the build starts. Phase two ships a prototype on your real data within two to three weeks, so everyone sees what AI can and can't do before the production budget is committed.
From there, production engineering, deployment, and MLOps are not a separate workstream - they are designed alongside the model. CI/CD, observability, drift detection, and the on-call runbook ship with the system, not after it. At handover, you get the source code, the model weights, the evaluation harness, and a written enablement plan so your engineers own what we built. If you prefer us on the rota, we stay on as an MLOps retainer; if you don't, the system runs without us.
That production-handover focus is what separates a custom AI development partner from a demo shop - and it's why AINinza projects ship.
Deploy production-ready AI agents for support, sales, and operations with human-in-the-loop controls.
Learn moreBuild grounded AI assistants using enterprise retrieval, ranking, and response guardrails.
Learn moreFine-tune GPT-4, Llama, and Mistral on your proprietary data for domain-aligned AI outputs.
Learn moreShare your use case and data landscape. We'll deliver an implementation plan, success metrics, and a launch timeline tailored to your organization - usually within 48 hours.
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