Technology
Recommendations grounded in domain expertise.From day one.
Most AI tools are general-purpose. Chainworthy arrives informed: a platform knowledge base encoding decades of sales, channel, incentive, and loyalty expertise, combined with your Context Library and data. Both inform every recommendation.
Two knowledge sources. Both working on every analysis.
Every recommendation, finding, and Ask Chainworthy response draws from two sources simultaneously. The combination is what makes output consultant-grade.
Platform knowledge base
Built by Chainworthy. Available to every customer.
Proprietary corpus: 30+ years in program design, channel and loyalty mechanics, behavioral science, performance benchmarks. Structured, searchable frameworks the AI retrieves in context. Not a fine-tuned public model. Available from day one without configuration.
What it contains:
- Motivation and engagement theory
- Audience segmentation (B2B channel, B2C loyalty, hybrid)
- Program architecture
- Rewards strategy
- Financial modeling and ROI methods
- Performance benchmarks by program type
- Industry terminology reference
Your Context Library
Built from your documents and data. Private to your tenant.
Upload PDFs, Word, PowerPoint, spreadsheets, and more (see How Context Library works). Chainworthy also learns from your datasets, KPI definitions, goals, recommendation history, and results. No other customer can access it. Precision improves as goals and documents accumulate.
The more Chainworthy learns about your business, the more precisely it can apply the platform's industry expertise to your specific situation. A recommendation generated six months into a customer relationship is more targeted than one generated in the first week, because the Context Library has learned what matters most to this particular business.
The combination is the moat.
Generic AI has no industry expertise and no knowledge of your business. BI has your data but no strategic interpretation layer. Chainworthy retrieves relevant platform expertise and applies it to patterns in your data and Context Library.
When Chainworthy generates a recommendation, it does not simply pattern-match against your data. It retrieves the most contextually relevant expertise from the platform knowledge base and applies that expertise to the specific patterns in your data and Context Library. The result is a recommendation that is grounded in your numbers and informed by proven strategic thinking.
Dual-retrieval architecture (platform expertise + tenant isolation) is protected IP. Chainworthy also holds granted patent protection covering blockchain architecture on the product roadmap. Competitors would need years of curated domain content to replicate the expertise layer.
Where the platform knowledge base came from
The platform knowledge base was not licensed from a data provider or assembled from public web sources. It was built by Chainworthy's founding team from original methodology developed over 30 years of working directly in the incentives, loyalty, channel performance, and sales effectiveness industry.
Chris Galloway, Chainworthy's COO, spent 25+ years as a strategy and design leader in performance improvement, including as EVP Strategy & Design at a leading incentive agency, board officer of the Incentive & Engagement Solution Providers industry association, and published researcher in the Journal of Marketing. The frameworks in the platform knowledge base are the distillation of that career: the pattern recognition, the benchmark knowledge, and the strategic methodology that experienced practitioners develop over decades.
Encoding that expertise into a form that AI can retrieve contextually and apply precisely is the technical innovation Chainworthy built. The expertise itself is the raw material. The architecture is how it becomes actionable at scale.
Expertise you'd normally hire for. Built in from day one.
See how the dual knowledge base changes recommendation quality in a demo using data from your industry.