SaaS & Technology
AI features your support team can explain
Adding AI to a product that already has customers is a different problem from building one. There is a data model you cannot casually change and a support team who will field whatever ships.
Use cases
Where AI pays off here
New AI products
From definition to a first sellable release, including billing and admin tooling.
AI in an existing product
Assessment of what is there, then capability added behind a gated release process.
Agent-operable products
MCP interfaces so your product can be driven by an agent, not only by a person.
Binding constraints
- Multi-tenant isolation
- Per-customer usage and pricing
- Release quality gating
- Support explainability
Engagements
How we typically start
- 01Core AI
AI Product Development
AI-native products, and AI features inside products that already have customers.
- 02Core AI
End-to-End AI Development
First workshop to an operated system, one accountable team. Agents, retrieval, custom models.
- 03Operate
Managed Edge & Model Fleet
Drift, retraining, OTA rollout, device health and uptime reporting, under an agreed SLA.
Related proof: An agent-operable search intelligence platform
Start with a call, then a costed plan
Thirty minutes on the problem, the site and the constraints. If it looks like a fit, the next step is a four-week AI Pilot at a fixed price, with acceptance criteria signed before any code is written.