LLM features
Summaries, drafting, classification and natural-language interfaces inside your app.
AI Feature Integration
Add AI to the product you already have
Smart search, chat, summaries, document processing and predictions — integrated into your existing platform through clean APIs.
- Since 2014
- Full code & IP ownership
- NDA on request
Your existing appTicket #8841
Customer reports duplicate charges after updating the payment method on the mobile app…
✦ Summarise with AISummary: duplicate charge after card update · priority high · suggest refund
Feature flags
Smart summaries
Semantic search
Voice input
POST /api/ai/summarise820 ms
- ModelClaude
- FallbackReady
No rebuild neededAPI integration
Overview
AI features, no rebuild required
You do not need to start over to benefit from AI. Most products can gain intelligent features — search that understands intent, automatic summaries, data extraction, recommendations or an in-app assistant — by integrating AI services into the existing architecture.
We audit your platform, find the features with the highest impact and add them through secure, well-tested APIs. Your current stack stays in place, and new AI capabilities are released gradually behind feature flags.
AI Feature Integration at a glance
- LLM featuresSummaries, drafting, classification and natural-language interfaces inside your app.
- Semantic searchSearch that understands meaning, not just keywords, across products, content and documents.
- Document intelligenceExtract data from invoices, forms, IDs and contracts into structured fields automatically.
- Voice AISpeech-to-text, voice commands and voice agents for web, mobile and telephony.
What we offer
AI features we integrate
Intelligent features, zero rebuild
Semantic search
Search that understands meaning, not just keywords, across products, content and documents.
Document intelligence
Extract data from invoices, forms, IDs and contracts into structured fields automatically.
Voice AI
Speech-to-text, voice commands and voice agents for web, mobile and telephony.
Recommendation engines
Personalised product and content recommendations that increase engagement and sales.
Predictive analytics
Forecasts, churn prediction and anomaly detection from your historical data.
Capabilities
How we integrate
Engineering depth that keeps your product fast, secure and easy to evolve.
- Platform audit to find high-impact AI opportunities
- Integration through REST / GraphQL APIs and webhooks
- Works with Laravel, PHP, Node.js, React and mobile apps
- Background queues for long-running AI tasks
- Feature flags for gradual, safe rollout
- Fallbacks when an AI service is slow or unavailable
- Caching and model routing to control cost
- Personal data masking before data leaves your systems
- Evaluation suites for every AI feature
- Logging, tracing and cost monitoring
- No downtime for your existing users
- Documentation and handover to your team
Our approach
How we think about every project
Strategy first
We start with your business goals, users and constraints, and turn them into a clear scope, architecture and roadmap before writing code.
Solid engineering
Clean architecture, code reviews, automated tests and secure coding practices keep your product fast, stable and easy to extend.
Predictable delivery
Short sprints, regular demos and transparent communication mean you always know what is done, what is next and what it costs.
Technology
The stack we use
Proven, well-supported technologies chosen for your requirements.
- OpenAI
- Anthropic Claude
- Google Gemini
- Azure OpenAI
- AWS Bedrock
- Whisper
- Deepgram
- ElevenLabs
- Twilio
- pgvector
- Pinecone
- Qdrant
- Elasticsearch
- Meilisearch
- REST & GraphQL
- Webhooks
- Laravel queues
- Node.js workers
- LangSmith
- Langfuse
- Sentry
- Grafana
How we work
A clear process from idea to launch
- STEP
Opportunity audit
Review your product, data and users to identify where AI adds the most value.
- STEP
Prioritise
Rank features by impact and effort and agree on the first release.
- STEP
Prototype
Validate quality with your real data before integration.
- STEP
Integrate
Build the feature into your platform with tests, fallbacks and flags.
- STEP
Release
Roll out gradually to users and monitor quality and cost.
- STEP
Deploy & monitor
Track usage, accuracy and spend, and keep improving.
We interviewed shops in many countries, and finally we got some professional help in making a decision. We decided to go ahead with Webcarving in India. It is really the best decision we have made so far for the business.
Mike BozorgiCTO, OrcaParts
Deliverables
What you get
- AI opportunity audit and roadmap
- Integrated, tested AI features
- API documentation
- Evaluation test sets
- Monitoring and cost dashboards
- Handover documentation
Frequently asked questions
Do we need to rebuild our product to add AI?
No. In most cases AI features are added through APIs and background services while your existing system keeps running.
Do we need a lot of data?
Not necessarily. Many features use pre-trained models plus your existing content. Predictive models need historical data, which we assess during the audit.
Will our users experience downtime?
No. Features are developed in staging and released gradually behind feature flags.
What happens if the AI service fails?
We build fallbacks, timeouts and retries so your product keeps working, and log every failure for review.
How is our data kept private?
We mask personal data where possible, use enterprise API terms and can deploy models in your own cloud when required.
Keep exploring
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Learn moreHave an idea? Let’s build it together.
Tell us about your project and get a free consultation with a clear plan, timeline and estimate.