Discovery is moving to machines. Decisions are moving to agents. We architect and operate both surfaces so your brand and your operations are not paraphrased away.
Two things are changing at once. Discovery is migrating to generative engines that summarize and recommend without surfacing ten blue links. Internal decisions are migrating to agents that retrieve, reason, and act without a human keystroke. Most organizations are responding with isolated pilots and content gimmicks.
We treat the generative layer as a new operating surface. On the outside, brands need to be cited as authoritative by the engines that now mediate buyer research. On the inside, organizations need agentic decision systems that compress operating cycles by orders of magnitude. We architect, build, and operate both.
Make your brand citable inside ChatGPT, Perplexity, Gemini, and Google AI Overviews. Entity disambiguation, schema, citable content architecture, and authority acquisition.
Multi-agent decision systems for forecasting, pricing, operations, and customer care. Agent graph design, evaluation use, and human-in-the-loop governance.
Production-grade retrieval-augmented generation: chunking strategy, embedding selection, hybrid retrieval, eval use, and grounding governance.
Production LLM features: tool use, structured output, evaluation, observability, cost discipline, and safety. Shipped, not prototyped.
Editorial and product content generation systems engineered for voice integrity, retrieval quality, and human review at scale.
Use-case portfolio prioritization, build versus buy decisions, model selection, data governance, and a risk framework that executives can actually defend.
A new operating surface, grounded in your data and optimized for how machines now discover you.
4 to 6 weeks. Use case portfolio mapped by economic value and feasibility. Discovery audit across LLM citation surfaces. Output is a sequenced build/buy/partner roadmap.
Reference architecture for the prioritized use cases. Evaluation use, grounding rules, observability, and cost guardrails defined before code is written.
We build the agentic systems, RAG layers, content systems, and GEO architecture, working alongside your engineering and content teams. Output is production code, not slideware.
Operating model, monitoring, and compounding improvement loops. Citation share, agent performance, and unit economics tracked as first-class metrics.
AI organic traffic uplift via GEO entity, schema, content, and citation architecture.
Read Case Study →Multi-agent autonomous forecasting and pricing system replacing manual planning cycles.
Read Case Study →Marketing Mix Model agents reallocating budget through causal inference in real time.
Read Case Study →Three questions on whether your GenAI is a demo or a system people depend on.
Making a brand citable inside the answers produced by ChatGPT, Perplexity, Gemini, and Google AI Overviews. Structured data, entity work, citable content architecture, and authority acquisition.
Classical SEO optimizes for ranked links. GEO optimizes for being the answer that the LLM produces, with or without a click. The disciplines overlap on structured data; the content and citation strategies differ.
4 to 6 week diagnostic, then reference architecture and evaluation use, then build and ship. We do not deploy GenAI for its own sake.
Yes. Multi-agent decision systems for forecasting, pricing, operations, and customer care, with evaluation and human-in-the-loop governance built in from day one.
We engineer grounded generative systems, in production, optimized for how machines now discover and cite you.
The Strategic Diagnostic is the entry point.
Apply for Diagnostic