We build custom AI solutions.

Use the contact form at the end of this page or email [email protected]. We reply within 1 business day, schedule a discovery call to understand your requirements and, if AI is a good fit, send you a quote.

Yes. We deploy models inside your own VPC (AWS, GCP or Azure), on your on-prem servers, or in fully air-gapped environments with no reliance on third-party APIs, so your data never leaves your infrastructure. For PharmaBrain we ran a quantized Mixtral 8x7B model fully offline on local hardware.

Pricing depends on scope, so we quote each project after understanding your requirements. Well-defined problems are run as fixed-scope projects, usually starting with a proof of concept (typically 4–6 weeks). Ongoing work is handled by a dedicated engineering team on a retainer. If you are not sure where to start, our AI Opportunity Audit takes 3–5 business days.

Less than you might expect. Deep learning models traditionally required very large training datasets, but foundation models such as GPT, Llama and Stable Diffusion let us build AI solutions for a wide range of applications with very little or no labeled data.

No. In machine learning it is virtually impossible to achieve 100% accuracy, and accuracy depends heavily on the amount and quality of your data. For most models we build, we reach accuracy comparable to or better than a trained human reviewer.

Yes. We build custom floor plan analysis systems that detect walls, rooms, doors, windows and symbols, read dimensions and annotations, and calculate room areas and quantities from multi-page PDFs and blueprints. Results come back as structured data you can feed into estimating, compliance or valuation workflows.

You do. Source code, model configurations and trained weights we build for you are your intellectual property, so there are no per-seat licenses to rent. We sign an NDA whenever you need one.

For PropTexx we built listing image intelligence that tags property features and flags compliance issues, plus AI virtual staging that preserves room geometry. For Finance Lobby we built an offering memorandum parser that extracts underwriting data from brokerage documents.

Yes. We provide support and maintenance including fixing runtime bugs, scaling infrastructure to meet traffic demands, and continuously monitoring the performance of machine learning models in production.

We focus on building custom models for your use case rather than wrapping third-party APIs. That usually means better performance on your data, lower running costs at scale, and the option to run everything on infrastructure you control.