Add AI Features to Your Existing SaaS Product
We integrate practical AI capabilities — chat, search, retrieval, and automation — into your existing product without rebuilding the stack. Ship one focused AI feature in weeks, not quarters.
AI Features We Build
Six practical AI capabilities that improve user experience and reduce operational load in real products
AI Chat Integration
Add a context-aware AI chat interface to your product — powered by your own data, not generic answers from a pretrained model.
- Product-context aware responses
- Conversation history & memory
- Guardrails & content safety
Retrieval-Backed Product Assistant
Help users find answers from your product docs, help articles, and knowledge base — instantly, without filing a support ticket.
- Doc ingestion & indexing
- Semantic retrieval with citations
- Inline UI integration
AI-Assisted Support
Reduce support load with an AI layer that handles common questions from your docs and ticket history before escalating to a human.
- Knowledge base ingestion
- Ticket deflection analytics
- Human escalation fallback
Workflow Automation with LLMs
Automate repetitive tasks — data extraction, document summarization, classification, routing — that currently require manual effort.
- Document & email parsing
- Classification & tagging
- API integration & routing
Search & Recommendations
Replace keyword search with semantic search, and add AI-powered recommendations for content, products, or next actions.
- Semantic search across content
- Personalized recommendations
- A/B testable ranking
AI Onboarding Assistant
Guide new users through your product with an intelligent onboarding copilot that answers questions from your own product documentation.
- Product-specific knowledge base
- Step-by-step guided flows
- Progress tracking integration
When to Add AI to Your Product
The right AI feature solves a real user or operational pain — not just adds "AI" to your marketing page
SaaS Needing AI Chat
Your users want to query your data or product in natural language. A simple FAQ chatbot won't cut it — they need context-aware answers.
Large Support Load
Your support team is overwhelmed with repetitive questions that your docs already answer. A well-scoped AI layer can deflect repetitive tickets when knowledge quality and escalation paths are designed properly.
Internal Search That Fails
Employees can't find what they need across Confluence, Drive, or internal tools. Semantic search surfaces the right document instantly.
Repetitive Manual Tasks
Your team spends hours extracting data from documents, categorizing emails, or routing tickets. LLM-based automation can handle this.
Content & Recommendations
You have a content-heavy product and want to recommend relevant articles, courses, or products based on what the user is doing.
Workflow Automation
You have multi-step internal workflows that could be streamlined with AI extraction, classification, and automated handoffs.
Best Fit For
- you already have a product and want to add one useful AI capability without rebuilding the stack
- the feature needs to live inside an existing user workflow, dashboard, portal, or operational tool
- you want to test one well-scoped AI feature before expanding into a larger roadmap
- frontend, backend, prompt design, and rollout all need to move together
Not the Right Fit When
- you primarily need a shared knowledge assistant or private document Q&A system over docs and SOPs
- the product problem is still unclear and there is no concrete feature or workflow to improve
- you want AI as a homepage badge rather than a feature tied to user value or operational leverage
- the work is broader product rescue or modernization rather than a targeted AI integration
If you need a knowledge system over documents and SOPs first, see AI / RAG Knowledge Systems.
How a Feature Sprint Works
A focused, four-step process designed to ship one AI feature well — rather than plan ten and ship none
Feature Scoping
Define the AI feature, user journey, data requirements, success metrics, and acceptable latency/cost tradeoffs
LLM & RAG Selection
Choose the right model, retrieval strategy, prompt approach, and integration pattern for your use case
Integration Design
API design, prompt engineering, context management, safety guardrails, and backend integration plan
Build & Deploy
Implementation, evaluation, staged rollout to real users, monitoring, and iteration on quality
AI Integration Technology Stack
We integrate with your existing backend — adding AI capabilities without replacing what already works
AI & Models
Data & Storage
Infrastructure
How to Get Started
We recommend starting with one well-scoped AI Feature Sprint — ship something real, learn what works, then expand
AI Feature Sprint
Ship one well-scoped AI feature end-to-end — from integration design to production deployment
- Feature scoping & design
- Full implementation
- Tested & deployed to production
- Monitoring & iteration plan
Full AI Integration
Comprehensive AI strategy and multi-feature implementation across your product
- AI roadmap for your product
- Multiple feature sprints
- Integration testing & monitoring
Ongoing AI Development
Continued iteration and improvement of AI features as models and APIs evolve
- Regular feature sprints
- Quality & accuracy improvements
- New model & API updates
Success Stories & Client Projects
Products and platforms we've built and shipped for clients worldwide, including AI-adjacent proof like Refactored.ai and DiscoveredBy.
Bough Digital
UK-based digital marketing agency platform with campaign management, analytics, and workflow-heavy product delivery.
See more work
Refactored
Interactive Python learning platform with AI-assisted interview feedback and guided product workflows.
See product proof
PRO Music Tutor
Premium online learning platform with workflow-heavy product experiences and user-facing delivery.
See more work
CREDITABLE
Employee financial wellness platform with dashboards, operational flows, and full-stack product delivery.
See portfolioReady to Add AI to Your Product?
Start with a free discovery call. We'll scope the right first AI feature and propose a sprint to ship it.