Put a copilot inside your SaaS in weeks, not quarters.
In-app assistants that understand your data model, take actions in your UI, and make your product the one users don't want to leave.
What is an AI copilot for SaaS?
An AI copilot for SaaS is an in-app assistant that understands the current user, account and screen, answers questions about their data, and executes actions in your product through natural language. Eazyware builds copilots as a service layer over your existing APIs, with retrieval, action framework, evaluations and per-tenant metering so you can sell it as a premium tier.
| Service line | AI-Powered Product Engineering |
|---|---|
| Engagement | Scoped build with milestones |
| Duration | Quoted after scoping; typically 8–16 weeks |
| Starting price | $19,500 |
| Typical range | $19,500 – $63,000 |
| Deliverables | 5 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
Your competitors are shipping AI features. Your users expect one. Your roadmap has no room and your team has never built one.
How do we approach it?
The copilot is built as a thin layer over your existing product, not as a new product beside it. We start with a walkthrough of your data model and API surface and a list of the ten jobs users would ask a copilot to do, ranked by time saved; the first release does the top three well. Context, the current account, user, role and screen, is passed on every request so questions can be short. Every action maps to an existing permission-checked API call and is shown as a proposal before it executes. Metering is written to your billing system so the copilot can be a plan tier. A private beta with accounts your customer success team chooses shapes the second release.
What do clients use it for?
- Natural-language reporting inside a CRM or analytics tool
- Drafting and summarising inside a workflow product
- Command-palette actions across the app
- Proactive alerts and next-step suggestions
Is it the right fit?
Good fit when
- SaaS companies with an existing API surface
- Products with rich per-account data
- Teams planning a premium AI tier
Probably not when
- Apps without APIs the copilot can act through
- Marketing-only chat widgets
What do we build?
- Context-aware assistant that knows the user, account and current screen
- Natural-language actions inside your workflows
- Smart generation: emails, summaries, reports and drafts
- Proactive insights and nudges
- Embedded UI components: sidebar, command palette, inline suggestions
- Usage analytics and per-tenant AI billing
What you get
- Copilot service and UI components
- Action framework
- Evals
- Analytics
- Billing hooks
How does the engagement work?
- 01
Product walkthrough and data model
- 02
Top ten copilot jobs
- 03
Architecture
- 04
Build
- 05
Beta cohort
- 06
General availability
What does good look like?
A copilot users open weekly because it saves them the clicks they hate: the report that used to need an analyst, the bulk change that used to take an afternoon, the draft that used to start from blank. Adoption and actions-executed on a dashboard, an eval score per release, AI cost per account inside the plan's margin, and a component your front-end team can drop on new screens without us.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | Sprint Zero in 10 days, then a fixed-scope build | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed scope, milestone billing, INR or USD | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
Copilots fail when they are chat widgets with no context and no actions, when they can do things the user cannot, when their cost is unknown until the bill, and when they are scoped so broadly that they do nothing well. We solve each by design: context on every call, actions through your permissions, metering from day one, three jobs first.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Copilot adoption and weekly active use
- Actions executed vs answers only
- Retention lift in the beta cohort
- AI cost per account
Which technologies do we use?
- Your stack plus a Node.js AI service
- Function calling
- pgvector
- React components
Who does the work?
An AI engineer, a front-end engineer who works inside your codebase, an architect for the action framework and metering, and a designer for the panel and proposal patterns.
What do you need to bring?
API documentation and a staging environment of your product, a product owner who can rank the copilot's jobs, customer success input on beta accounts, and a decision on whether the copilot is a paid tier. Your front-end team's conventions so components fit.
Frequently asked questions
Will it work with our existing backend?
Yes. We integrate via your APIs, no rewrite.
Can we charge for it?
Yes. We build metering so you can price it as an add-on tier.
We built one already and it's weak.
We audit and rebuild the retrieval, actions and evals, usually the missing pieces.
Where does this fit?
AI Copilot Development for SaaS is part of our AI-Powered Product Engineering line. Not sure yet? Start with Sprint Zero, a ten-day discovery whose fee is credited to this build. See all pricing or talk to an engineer.