AI concierges and assistants
Answer customer questions and qualify leads on your site.
Product & Build
AI App Integration
Useful AI features inside your product. AI assistants, concierges, and automations designed around real workflows, not demos.
Capability
Product & Build
Primary outcome
Useful AI features inside your product.
Delivery
Strategy through execution
In short
What this capability does
AI app integration adds useful AI features, such as assistants, document processing, recommendations, and agents, into real products and workflows. Cantellis designs these features around measurable outcomes with cost, quality, and safety guardrails.
Our point of view
AI features should do a job, not decorate a product.
Many AI features are launched because competitors have one. They get a burst of attention, then usage falls off because they do not do a specific job better than the old way. Useful AI starts with a task: answering a repetitive question, extracting data from documents, recommending a next step.
We design AI features like any other product feature, with a user, a job, and a success measure. Then we add what AI specifically needs: evaluation of output quality, guardrails for unexpected input, cost controls, and clear handling of what data is sent to a model and what is stored.
This site is our own proof. The Growth Advisor and POC Builder are AI features built on the same principles we apply for clients: server-side processing, no unnecessary storage of visitor input, and outputs designed to lead to a clear next step.
Critical signals
You likely need this if
- 01
Your team answers the same questions daily
- 02
Documents are processed by hand
- 03
Leads need qualification before a call
- 04
You have an AI idea but no clear use case
The problem
What this solves
We turn the underlying constraint into a focused body of work, rather than treating surface symptoms in isolation.
- 01
AI ideas stay stuck as demos.
- 02
Unclear which AI features are worth building.
- 03
Concerns about quality, cost, and data safety.
Common use cases
Where this capability earns its place.
Document and data workflows
Extract, summarize, and classify information automatically.
Recommendation and advisor tools
Personalized guidance like our Growth Advisor.
What you get
Deliverables
- AI feature or assistant
- Prompt and evaluation design
- Cost and safety guardrails
- Usage tracking
How it runs
Our approach
01
Pick the job
Choose one task where AI clearly saves time or adds value.
02
Design
Define prompts, data access, and evaluation criteria.
03
Build
Integrate the feature into the app with guardrails and fallbacks.
04
Evaluate
Measure quality, cost, and usage, then improve.
Lovable and Claude
Excellence at speed
AI is the accelerator, not the product. One operator keeps strategy, build quality, and business context connected.
- 01
Claude and other models are selected per task based on quality and cost.
- 02
Lovable ships the surrounding app, UI, and data in the same sprint.
- 03
Visitor data handling is designed for privacy from the start.
Expertise and outcomes
Capability that changes the work.
Our expertise
- LLM integration and prompt design
- AI-native lead tools in production on this site
- Safe server-side handling of AI requests
Expected outcomes
- AI features people actually use
- Predictable AI costs
- Measurable time or revenue impact
What we see go wrong
Common mistakes to avoid.
Starting with the model
Pick the job first. The right model follows from quality, speed, and cost needs.
No evaluation
Without test cases, you cannot tell if a prompt change made things better or worse.
Calling AI from the browser
API keys and logic belong on the server.
Unlimited usage
AI costs scale with usage. Set limits and monitor spend.
Measurement
How we measure success
- Task completion
- Share of jobs handled without human help
- Output quality
- Score against evaluation cases
- Cost per task
- AI spend per completed job
- Adoption
- Repeat usage of the feature
Questions
FAQ
- Which AI models do you use?
- We choose per task, including Claude and other leading models, balancing quality, speed, and cost.
- Is our data safe?
- AI calls run server-side and we design what is stored, if anything, with you.
- Who does the ai app integration work?
- The operator who scopes the work does the work. No hand-offs to junior teams.
- Do we own everything?
- Yes. Code, content, accounts, and data stay yours with zero agency lock-in.
- How do we start?
- Send a short brief or build a quick POC. We reply with a recommended next step.
Keep reading
Related guides
12 min read
Lovable AI use cases: what we have built and what we can build next
Real Lovable AI use cases from production work: advisors, live POC builders, visibility audits, lead dossiers, agent integrations, and dashboards, plus the AI systems we are ready to build next.
Read guide8 min read
What is Lovable AI app development? A practical guide
Lovable AI app development explained: how the platform works, what it builds well, where human judgment still decides the outcome, and how projects reach production.
Read guideProof, not promises
Try it on your problem.
Describe what is blocking you and get a small working prototype in minutes.
Build a POCTalk to the builder
What needs to move?
Send a short brief. The person who reads it is the person who will do the work.
Send a brief