We don't build apps.

Building a frontier R&D team takes years and rare talent. We put a more experienced team on the problem from day one, at 50–60% of the cost of building one. We take on the hardest technical unknowns first. Then we help you recruit, train, and onboard your own team and mentor them until they no longer need us. The full technology transfer is planned from the start.

Yes. Full intellectual property transfer is core to how we work: custom models, weights, codebases, and documentation all become entirely yours, with no licensing strings attached.

It stays with you. We deploy on infrastructure you own, including local GPU servers, so nothing leaves your perimeter. If you cannot describe the problem without confidential details, we sign an NDA before you do.

It depends on how deep the research goes. Most engagements start with an MVP in a few months, then move to the production-ready system. We plan in stages of a few months, each ending in an interim delivery you can run and judge yourself.

We size the team your project needs, then multiply it by how long the engagement runs. An engagement comes out at 50–60% of what an equivalent in-house team would cost.

We start with consulting and feasibility work to validate the approach before heavy investment. Our process is built around rigorous hypothesis testing, so we redirect early rather than burning budget on a dead end.

Through the AI Partnership. Our researchers track new papers, models, and releases for you and filter out what does not touch your business. You get curated briefings: what changed, whether it matters to you, and what we would do about it. People write them, not an agent.

Problems where a standard API and a few prompts are not enough: uncertainty, scale, reasoning, complex data, or an architecture that does not exist yet. If your challenge is unprecedented and domain-deep, that's exactly where we do our best work.

Whichever fits the job. We are model-agnostic: OpenAI, Claude, Gemini, Llama, Mistral, and DeepSeek are all in our stack. Where it fits, we compress an open model to run on your own hardware.

Two reasons. We build code-level agents on models selected and compressed for the job, and deploy them on local GPU servers you own. Every optimization is scored on cost alongside accuracy, so the cheaper design is found during the work.

We do not rely on the model being right. Every answer passes human-grounded validation, purpose-trained small models, and rule-based guardrails. An ontology layer then checks it against formal constraints. That is neuro-symbolic AI: the neural part proposes, and the symbolic part rejects anything logically inconsistent.