In-app copilot
A sidebar assistant that knows the account and the current screen, answers from the customer's own data and takes actions through your existing APIs.
We measure:Weekly active use of the copilot
Copilots inside your product. AI features that justify a higher tier.
For SaaS companies, Eazyware builds the AI features users expect: in-app copilots, LLM-powered generation, retrieval over each customer's own data and natural-language querying, on multi-tenant backends with metering so the features can be sold as a premium tier.
Which problems do we solve in SaaS?
- Users expect AI features and the roadmap has no room
- Single-tenant AI backends that do not scale or meter
- Retrieval that answers from docs but not from the customer's own data
Where AI pays off in SaaS
Three plays we have built for this industry, each with the number we instrument so the result is measured, not claimed.
AI tier you can charge for
Per-tenant metering, budgets and billing hooks so generation and retrieval features become a paid plan instead of a cost centre.
We measure:AI-tier attach rate
Support that reads the account
Agents on chat and email that look up the tenant's plan, usage and tickets before answering, and escalate with context.
We measure:Tickets auto-resolved
What we design for in SaaS
The constraints that shape the architecture before a line of code is written.
- Tenant isolation is non-negotiable: retrieval, prompts and logs are scoped per account.
- Model costs scale with usage, so routing and caching are designed in before launch, not after the first invoice.
- Features ship behind flags to a beta cohort first; evals gate the general release.
- Your existing API surface is the integration layer. No parallel backend.
Have more questions?
Questions we hear most from SaaS teams.
Typically six to ten weeks for a first version integrated with your existing APIs, followed by a beta cohort before general availability.
Yes. We build per-tenant usage metering and billing hooks so AI can be priced as an add-on or higher plan.
No. We work against anonymised samples and staging environments, and design retrieval to respect each tenant's permissions.
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