AI Features Inside Your Product
AI capability your customers would notice if you removed it.
01
In short
What Kramiva means by AI features inside your product
Kramiva adds AI features to products that already exist: search and summarisation over a customer's own data, drafting and suggestion interfaces, classification, and assistants inside an application. The work covers feasibility, model selection, the interface for a system that is sometimes wrong, an evaluation suite, and modelling cost and latency per user before rollout.
- Who it is for
- Software companies with an existing product and real users, where an AI capability could plausibly change retention or pricing — and where shipping a chat box nobody uses is the actual risk.
- Discipline
- AI and Automation
02
Scope
What we build, and what is included.
What Kramiva can build
- Search and summarisation over a customer's own data
- Drafting, suggestion, and autocomplete interfaces
- Classification and extraction inside existing workflows
- In-product assistants with citations and undo
- Evaluation suites and quality dashboards
What a typical engagement includes
- Feasibility, model selection, and a cost and latency model
- Interaction design for uncertainty: drafts, citations, undo, escalation
- Feature engineering inside your existing codebase and conventions
- Evaluation harness and regression suites
- Staged rollout, feature flagging, and monitoring
03
Delivery
How this one actually runs.
04
Questions
The things you are about to ask.
- Can AI be added to an existing product?
- Yes — that is what this service is. Kramiva works inside your existing codebase and conventions rather than building a separate system beside it. The feasibility stage comes first and can honestly conclude that the feature is not worth building, which is cheaper than finding out after a quarter of engineering.
- Which models does Kramiva build on?
- Whichever fits the constraint. Selection is driven by evaluation on your specific task, plus latency, cost, and any data-residency requirement. The integration is built so the model can be swapped without rewriting the feature, because the sensible choice changes every few months.
- How do you measure whether it is actually good?
- With a task-specific evaluation set built from your real data, scored before launch and re-run on every change. Without that, quality is an opinion that shifts with whoever demonstrated it most recently.
Related service
Explore AI automation and assistants
Workflow automation and assistants grounded in your own documents and systems — scoped against a measured process, not a demo.
Talk to us about AI features inside your product.
A short brief gets you a real reply from a founder within one business day — an honest read on fit, scope, and budget.