> ls /services/ --group — pick the problem, not the buzzword
Assistants, retrieval and agents that answer from your data.
Enterprise knowledge is trapped in silos; chat demos collapse under permissions, latency, and hallucination risk.
Customers write in more than one language; single-language bots and translated FAQs lose intent, tone, and trust.
Naive agents amplify risk when permissions and review are bolted on after the demo.
Remove repetitive work between the tools you already use.
Teams glue SaaS tools together by hand, or with fragile zaps nobody owns once they break.
Leads, invoices, and approvals move between CRM, ERP/SAP, email, and spreadsheets by copy-paste.
Invoices, forms, and PDFs are keyed in by hand, and errors surface weeks later.
Cameras and signals turned into alerts people act on.
Stores and warehouses record hours of CCTV that nobody watches until after the loss.
Detection and document pipelines stall between dataset design and shippable inference.
Suspicious transactions and events hide in volume; static rules either miss them or flood analysts.
Models and pipelines that hold up under real traffic.
Models that look good in notebooks fail under drift, cost spikes, and missing ops once real traffic arrives.
Models starve when pipelines are brittle, schemas drift, and SAP/analytics truth is untrusted.
Catalogs and demand plans run on gut feel or popularity lists that ignore context.
Evidence before build, and security before launch.
Papers and prototypes die in the lab when product constraints never enter the experiment design.
Chatbots and agents ship without testing for prompt injection, data leakage, or over-permissioned tools.
Teams have a list of AI ideas but no view of which ones their data, budget, and risk appetite can support.