Compound AI is the deliberate combination of adBrain’s two areas of expertise. The most valuable systems we build are neither purely generative nor purely predictive. They are compound: a language model supplies the interface, reads unstructured evidence, and explains itself, while a calibrated machine-learning model supplies the disciplined numeric judgement. Each covers the other’s weakness.

This is genuine expertise, not a marketing bridge, and it is where running two areas of expertise under one roof stops being an org chart and becomes an advantage.

Why it is a real differentiator

  • The two disciplines fix each other’s failure modes. Language models are fluent but poorly calibrated on numbers and prone to ungrounded answers; decision models are calibrated and auditable but cannot read a contract or explain themselves. Wire a language model’s tool-call into a ranking, pricing, or propensity model, let the model narrate the result, and you get something neither could deliver alone.
  • It needs both disciplines at once. You cannot assemble a compound system by buying a generative product and a forecasting product separately; it takes genuine depth in retrieval and incrementality, agent orchestration and decision models, working together. Most firms are strong in only one of these. adBrain works fluently in both, which is precisely why this expertise is credible for us and not for a single-discipline shop.
  • It is where serious AI is heading. An agent that calls a pricing model; a knowledge assistant standing beside a recommendation engine; a decision loop that uses a language model to extract structured facts from documents before scoring them. These compound patterns keep recurring.

What we can do

  • Language-driven decision systems: a generative interface that gathers context and calls a calibrated model for the numeric decision, then explains it in plain language.
  • Evidence extraction into models, using language models to turn unstructured documents into structured features a decision model can score.
  • Explainable decisioning that gives a natural-language explanation of why a model made a recommendation, price, or prediction, grounded in the actual inputs.
  • Hybrid evaluation: judging both the generated text and the numeric decision behind it, so quality is measured across the whole compound system.

How it shows up in a solution

When an answer depends on a number rather than a passage (“what discount should we offer this customer?”, “how likely is this to convert?”), a purely generative assistant guesses and a purely predictive model can’t converse. Compound AI lets the assistant call the calibrated model and explain the result. See it referenced across Solutions.

Compound AI draws equally from Generative & Agentic AI and Machine Learning & Decisioning, and relies on the secure-and-governed-AI foundation so a model called by an agent is governed exactly as a person would be. See the practices overview.

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