AI

Most AI projects don’t fail on models. They fail on data no one trusts.

We start where the pilots stall: the pipelines, the schema, the access controls. Then we ship the assistant, the agent or the forecast on top of them — into production, with evaluation you can audit.

Softrear builds production AI systems — assistants, retrieval, document extraction, forecasting and agents — starting from data quality and access control rather than model selection, and shipping with an evaluation harness the client owns.

The diagnosis

Two-thirds of the AI work we are called into is data work wearing a different hat.

The model is rarely the constraint. The constraint is that nobody can say where a field came from, when it was last correct, or who is allowed to see it — and an AI surface makes every one of those weaknesses visible to a customer instead of to an analyst.

So we start at the pipelines and the access controls, and we tell you when the honest answer is to fix the data and revisit in six months.

Retrieval → reasoning → action

Entry offer

AI readiness assessment

Two weeks, fixed scope, and about a third of them end with us recommending against the project as scoped. That is the point of running one.

Book the assessment
  1. Data lineage and quality audit across the systems in scope
  2. Three use cases scored on value, feasibility and risk
  3. A reference architecture and an evaluation plan you keep
  4. Yours whether or not we build it.

Tell us what’s stuck.

A senior engineer reads every one of these. If we’re not the right fit we’ll say so, and point you at someone who is.

Start the conversation

No spam, no drip sequence. A senior engineer replies within one business day.