Services

Will AI find your content when buyers are looking for a solution?

In the US, 750,000 marketing professionals rely on tools in the marketing technology sector, which is expected to grow from $0.5T in 2025 to $2.4T by 2033.

AI is disrupting this space.

Buyers use AI engines from ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews/AI Mode, and Microsoft Copilot for product recommendations and summaries.

To reach buyers, sellers are using generative engine optimization (GEO) and answer engine optimization (AEO). These products are strong in monitoring and weak in recommending marketing solutions to reach the buyer.

Better tools exist. Machine learning (ML) engineers are using tools to test thousands of queries and AI responses. Although ML engineers and sellers may at first seem very different, they both try to solve the same problem.

Retrieval Lab

Oppkey's Retrieval Lab tests whether content is well-structured for AI use. It aligns ML-engineering-grade diagnostics with sales campaigns: the same retrieval tests engineers run on RAG pipelines, translated into editorial actions - rewrite this title, split this page, add frontmatter, publish a FAQ, create a glossary, add internal links.

Retrieval Lab fills a gap between ML engineering tools and sales tools.

AI Probability Signals

Current AI technology is based on probability. Retrieval Lab understands the theory behind AI internal probability scores and provides probability signals as marketing scores.

Although Retrieval Lab does not know the confidential process for how a specific service like ChatGPT ingests information, the system makes assumptions on the process based on public research AI algorithms and formulas.

For example, to generate a Hybrid Signal, Retrieval Lab calculates a retrieval score and translates it into marketing metrics. The score is a number such as 64/100. The input is a piece of text, d. The question is q. BM25 means Best Match. This is combined with a similarity algorithm. Retrieval Lab has developed dozens of proprietary algorithms based on optimizing public research algorithms for marketing workflows. Two simplified examples are shown below.

Score(d,q) = λ · BM25(d,q) + (1 − λ) · CosineSimilarity(d,q)

Retrieval Lab uses a different blend to estimate visibility with its Eval Signal.

Visibility(d,q) = α · SemanticMatch(d,q) + β · CitationFrequency(d)

Using online forums and front-line technical support, Oppkey turns developer questions into documentation, product feedback, partner intelligence, and trust. We operate open, searchable technical community spaces, test and verify issues before responding, and convert recurring questions into content your team can reuse.

Forums run on proven platforms such as Discourse: open-source software built for long-form chat, mailing-list style workflows, tags, search, and threaded conversations. That gives developers a durable place to ask, answer, and search - without locking your community into a closed vendor stack.

Oppkey supplies the operational layer that makes the forum valuable: moderation judgment, technical explanation, front-line support, testing, summaries, issue triage, forum and video responses, and executive-level signal extraction. We identify use cases, partners, ecosystem apps, and expansion signals as they surface in real conversations.

The business result is developer trust, partner discovery, product signals, and new market narratives - intelligence you can act on, not just activity metrics.

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