Add AI to Your SaaS
Without Rebuilding It
Most SaaS teams spend 6 months adding AI features that should take 6 weeks. We know the integration patterns, the architecture decisions, and the shortcuts that don't break things.
What's Actually Getting in the Way
Users drop off before they see the value
Your onboarding flow gets them to the dashboard. After that, they're on their own. Most users never make it to their first "aha" moment — and you lose them to churn before month two.
Support is drowning in tickets AI could handle
Tier-1 support — password resets, feature questions, billing queries — is manual, repetitive, and expensive. Your team is spending hours on work that an AI could resolve in seconds.
You don't know someone is churning until they're gone
Your analytics shows when users left. It doesn't tell you they were about to leave two weeks before they cancelled. By the time you see the warning signs, it's too late to intervene.
Adding AI means touching your whole architecture
Or so it feels. Most AI feature requests get shelved because nobody wants to own the rabbit hole of LLM integration, token management, streaming responses, and prompt versioning on top of an existing codebase.
AI Systems We Build for SaaS
In-App AI Copilot
A natural language assistant embedded directly in your product — helps users complete tasks, find features, and get answers without leaving the interface.
AI-Powered Onboarding Flows
Personalised onboarding sequences that adapt to user behaviour and guide each user toward their first meaningful outcome faster.
Churn Prediction Models
ML models trained on your usage data that surface at-risk accounts before they cancel, so your success team can intervene at the right time.
Smart Search & Discovery
Semantic search across your product data, docs, and knowledge base — surfaces the right answer regardless of exact keyword match.
AI Support Triage
Classify, route, and auto-resolve Tier-1 support tickets. Your team handles the complex cases; AI handles the repetitive ones.
Usage Intelligence Dashboards
Intelligent analytics that surface the signals that matter — not just what users did, but why it happened and what to do next.
From First Call to Production
Discover
We map your current workflows, identify where AI creates real ROI, and validate the highest-impact use case with a working prototype — usually within 2–3 weeks.
Build
We architect and build the production system: data pipelines, model integration, API layer, and UI. Milestones are visible. No black boxes.
Measure
We instrument everything — accuracy, latency, usage, and business impact. You know what the AI is doing and whether it's working. Then we iterate.
Relevant Services
Frequently Asked Questions
How long does it take to add AI to an existing SaaS product?+
Depends on what you're building. An AI support triage layer on top of your existing ticketing system can go live in 3–4 weeks. A fully embedded in-app copilot with custom data grounding typically takes 8–12 weeks. The biggest variable is your data — how structured it is, how accessible it is, and how much domain context the model needs to be useful.
Will AI features break our existing architecture?+
Not if they're designed correctly. We build AI features as additive layers — they sit on top of your existing API, read from your data, and surface results through your existing UI. We don't rewrite your product to add AI. We add AI to what you've already built.
Do we need to fine-tune or retrain models regularly?+
Usually no. For most SaaS use cases — search, support triage, onboarding guidance — RAG (retrieval-augmented generation) is more practical than fine-tuning. Your product knowledge stays in your database; the model retrieves it at runtime. When your content updates, the AI is automatically current. Fine-tuning is worth the overhead only in specific cases, and we'll tell you honestly if yours is one of them.
What's the difference between bolt-on AI and actually useful AI?+
Bolt-on AI is a chat widget with a GPT-4 API key that your users try once and ignore. Useful AI is grounded in your product data, understands your domain, and does something your users can't easily do themselves. The difference is rarely the model — it's the data architecture, the UX decisions, and the integration depth. That's where we focus.
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Ready to Ship Your First AI Feature?
Tell us where you want AI to move the needle in your product. We'll scope it, validate it, and give you a clear path to production.
Get in Touch