Be cited, accurately, in the answers your buyers read first.
Answer engine optimization is not a new coat of paint on SEO or PR. It is an operating model for B2B communications that treats AI systems as audiences with their own source preferences, evidence requirements and interpretive logic. We run it as one program across diagnosis, narrative, content, third-party validation and measurement.
Buyers decide before they call you.
- 51% of B2B software buyers start their research with an AI chatbot more often than Google G2, March 2026 survey of 1,076 B2B software buyers
- 69% said an AI chatbot led them to select a different software vendor than they had planned G2, March 2026
- 33% had purchased from a vendor they had never previously heard of G2, March 2026
- 94% of business buyers use AI during the buying process Forrester, State of Business Buying 2026
If your brand is not being cited by AI systems, you may not be in the consideration set for a growing share of your buyers.
A five-stage operating model.
Each stage produces outputs that feed the next. Skipping the diagnosis and narrative stages is the most common reason AEO programs stall.
AI Visibility Diagnosis
We establish a baseline: how ChatGPT, Perplexity, Claude and Gemini describe your company, your category and your competitors today. Prompts mirror real buyer questions at each stage of the purchase journey, from category queries to comparisons to vendor-specific questions.
outcome A clear picture of the gap between how AI systems interpret you and how you need to be interpreted to win.
Narrative Architecture
Positioning says what a company does and for whom. Narrative architecture is a structured system of claims, supporting evidence and category definitions that AI systems can extract, verify against several sources and reproduce consistently.
outcome A source architecture: the foundation every piece of owned and earned content is built from.
Content Infrastructure
Content built for human and machine readers at once: self-contained sections, answers up front, named sources and specific figures, headings phrased the way buyers phrase questions, and one consistent vocabulary for the company, its category and its offerings. Executive thought leadership is treated as its own evidence layer.
outcome Pages and publications that answer the questions buyers ask AI systems, in language that matches your narrative.
Third-Party Validation
Owned content sets out the narrative. Independent sources are what make AI systems trust it. We earn analyst, media, industry-body and peer-review validation, and brief those sources on the category language you use, so the record AI systems retrieve agrees with itself.
outcome Corroboration from the sources your market, and the machines, already trust, using your category framing.
Measurement and Iteration
We track the same prompt set over time so results are comparable, and feed what we learn back into the narrative and the content. A brand that is described inaccurately has a different problem from one that does not appear at all, and each gets a different intervention.
outcome A continuous read on whether AI systems cite you, describe you correctly and send buyers your way.
From baseline to validation.
Baseline Audit
Prompt audit across the major engines, citation source map, entity consistency review and measurement baseline.
Content and Entity Fixes
Entity alignment, refreshed pages and a first set of answer pages built from the narrative architecture.
Third-Party Validation
Earned media with citation value, review presence, a second prompt audit and an ongoing measurement cadence.
AEO and GEO sit inside a larger system.
AEO and GEO help a company appear in an answer. Computational Communications designs the whole information environment from which the answer, and ultimately the decision, is formed. We use AEO and GEO as capabilities inside that system. See the full set of communications services, or read our operating model in detail and the definition of answer engine optimization.
Start with the diagnosis.
An AI visibility diagnosis shows how AI systems describe you today, which sources they cite instead, and where the gap is. We scope the rest from there.