Generative AI Solutions

Generative AI Solutions covers the design and build of LLM-backed product features — chat copilots, retrieval-augmented search, structured extraction, agentic workflows, and content generation that has to stay on-brand and on-policy. We treat an LLM as a programmable component, not a magic box. That means deterministic scaffolding around a probabilistic core: typed inputs and outputs, retrieval that you can inspect, evaluation suites that catch regressions, and prompts versioned the same way you version code.

This service is right when the value is locked behind unstructured data — long PDFs, support transcripts, internal wikis, contracts, product catalogs — or when the cost is locked behind a manual writing or judgment task that an LLM can do in seconds. It is also the right fit for in-product copilots, where the bar is consistency, latency, and the ability to ground every answer in a citable source rather than hallucinated text.

The SoftUs difference here is discipline. Most LLM projects fail because nobody measured them. We build the eval harness before we build the feature: a labeled set of prompts and the expected behavior, run on every change, surfaced as a regression score. We pick the smallest model that meets the bar, not the most expensive one. We design fallbacks for when the model is uncertain. We instrument cost and latency from day one so the unit economics still work when traffic ten-x.

We are also model-agnostic. We have shipped on OpenAI, Anthropic, Google, open-weights Llama and Mistral, and on private deployments inside customer VPCs when data residency required it. The architecture is built so swapping the underlying model is a one-line change, not a rewrite. You leave the engagement with a feature that works today and a system that survives the next model release without breaking your roadmap.