Startups
PatternAI-native from the first release
Building the product on an AI-native architecture from the start rather than retrofitting it onto a conventional one later.
AI-native products, built to ship.
Some problems are not an internal workflow but a product. We design and build AI-native software end to end, and we do it as operators: CovenantFlow, our covenant management platform, came out of this practice.
The business problem
AI-native products fail at the same seam as AI projects.
The demo earns the meeting and then the product has to hold up against real customer data, real permissions and real support expectations. Teams that treat the model as the product discover that the surrounding system — onboarding, entitlements, exception handling, observability — is most of the work and none of it was scoped.
What that looks like
- A compelling prototype with no path to a multi-tenant deployment
- Model behaviour that cannot be evaluated as the product changes
- No answer for the customer asking where their data goes
- Support and exception handling designed after the first incident
What Catalyze builds
What we build
The product and the system underneath it, by the same team.
- 01
Product definition
Working backwards from the operational job to be done, with the exceptions scoped as product surface rather than edge cases.
- 02
AI-native architecture
Multi-tenancy, entitlements, model routing and evaluation designed in, because retrofitting any of them means a rewrite.
- 03
Rapid prototyping
Early working software and iterative user feedback loops, so value arrives before the end of the engagement.
- 04
Evaluation harness
Measurable behaviour as the product changes, so a prompt or model change is a tested change rather than a hopeful one.
- 05
Production operations
Observability, incident paths and cost attribution from launch, not from the first outage.
Architecture
From prototype to platform
The stages where AI-native products usually stall, and what has to exist at each.
01Operational Problem
The job to be done, sized by where cost, risk and time actually accumulate.
02Working Prototype
Real software against real data early, so feedback is about the product and not a mockup.
03Product Core
The workflow, data model and interfaces the product is actually about.
04Platform Concerns
Multi-tenancy, identity, entitlements, residency — the things that cannot be added later.
05Evaluation
A harness that makes model and prompt changes measurable rather than anecdotal.
06Operations
Observability, support paths, cost attribution and an owner for production performance.
Example use cases
Where this applies
Our own product is the clearest example.
Private Equity
PatternProductising a portfolio capability
Turning an internal capability that works in one company into software that can be deployed across several.
Technology & integration
Integration surface
Products still have to meet the customer's environment. Named systems are those Catalyze publicly works with.
Named platforms
- Salesforce
- Workday
- ServiceNow
- AWS
Product platform
- Multi-tenancy
- SSO / SCIM
- Role-based access
- Usage metering
AI layer
- Model routing
- Evaluation harness
- Retrieval
- Guardrails
Operations
- Observability
- Incident paths
- Cost attribution
- Release management
Case studies
Proof in production.
Related insights
Our thinking on this.
Questions
What buyers ask us about AI Products.
Do you build products for clients, or only your own?
Both. CovenantFlow is our own, and it exists because the practice that builds client products also builds ours — the same architecture, the same evaluation discipline, the same accountability for production.
Who owns the intellectual property?
That is agreed at the start of the engagement rather than assumed, and it depends on whether we are building your product or extending one of ours. We would rather have the conversation before the contract than after.
How quickly can we see working software?
Our delivery method puts a working prototype against real data early, because feedback on a mockup tells you very little about an AI-native product. The precise timeline depends on data access, which is usually the long pole.
How do you stop model changes from silently breaking the product?
An evaluation harness built alongside the product. Prompt and model changes are tested against a maintained set of cases, so a regression shows up in the pipeline rather than in a customer's workflow.
Tell us about the workflow. We'll tell you whether AI Products is the right place to start.