Ship AI features without hiring an AI team.
We act as your AI full-stack team, from first prototype to production features your customers pay for.
The challenge
What slows SaaS teams down
AI roadmap pressure
Customers expect AI features while your team is busy with the core product.
Back-office load
Billing, support and admin tasks eat engineering time.
Integration requests
Every customer wants your product connected to their tools, and now to AI agents.
What we build
AI solutions for SaaS & Startups
AI features in your product
Copilots, summarization, semantic search and agents.
About AI Full-Stack Development →MCP Server DevelopmentAn MCP server for your product
Let customers use your product from Claude and other AI clients.
About MCP Server Development →Customer & Partner PortalsCustomer portal & admin
Billing, usage and support tools your team controls.
About Customer & Partner Portals →AI Automations & WorkflowsProduct-led automations
Onboarding emails, usage alerts and CRM updates that run themselves.
About AI Automations & Workflows →Example workflow
From sign-up to activated customer
- 1
User signs up
Account and workspace created automatically.
- 2
AI onboarding
An assistant guides set-up based on the user’s goals.
- 3
Usage tracked
Key events flow into your data warehouse.
- 4
Team alerted
Sales and success see who needs a nudge.
SaaS & Startups FAQs
Can you work inside our codebase?
Yes. We join your repository, follow your conventions and ship through your CI.
Do you build MVPs?
Yes. We build AI MVPs with logins, billing and the one AI feature that proves the idea, then hand you code your next hires can work on.
What does a first project with Widle look like?
It starts with a free 30-minute call about the SaaS process that costs your team the most. We follow up with a fixed-scope estimate, build in short cycles with a weekly demo, test on your real data and hand it over with documentation.
Industries
Other industries we serve
Have an AI feature on your roadmap?
Book a free 30-minute call. We’ll look at the feature, your stack and your data, and suggest where to start.
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