AI Features Integrated
Avg Lift in User Engagement
Typical First Feature Live
Integrations Without a Platform Rewrite
AI Integration Services
Practical AI capabilities added to your product without disrupting what already works.
Recommendation Engines
Personalized listings, products, and content based on behavior and context.
Predictive Analytics
Lead scoring, churn prediction, demand forecasting, and pricing models on your historical data.
Intelligent Search
Semantic and natural-language search that understands intent, not just keywords.
Document & Image Processing
OCR, extraction, and classification for contracts, invoices, inspections, and listings.
Conversational Interfaces
Support bots and in-app assistants connected to your data and business logic.
MLOps & Monitoring
Pipelines, model versioning, and drift monitoring so AI features stay accurate over time.
Integration Hurdles
We Clear
Adding AI to a live product raises hard questions about data, latency, cost, and trust. We answer them with engineering, not guesswork.
Messy or Scattered Data
We build the pipelines and clean-up steps that turn raw records into features models can use.
Latency in User Flows
Caching, async processing, and right-sized models keep AI features fast enough for real-time use.
Fitting Into Legacy Code
AI is delivered as services behind clean APIs so it plugs into your stack without a rewrite.
Cost Control
We choose between hosted APIs and in-house models based on volume, and monitor spend continuously.
User Trust
Explanations, confidence indicators, and easy overrides help users trust and adopt AI features.
Our Integration Approach
Small, measurable steps that add value fast and build the foundation for more.
Opportunity Audit
Review your product, data, and user journeys to shortlist AI features by impact and feasibility.
Opportunity Audit
Review your product, data, and user journeys to shortlist AI features by impact and feasibility.
Data Readiness
Assess data quality and access; set up pipelines and storage where gaps exist.
Build & Validate
Develop the model or service, test against real data, and validate results with your team.
Integrate & Ship
Connect through APIs, add UI, feature flags, and monitoring; release to a subset of users first.
Measure & Expand
Track impact metrics, retrain as needed, and roll out the next feature on the roadmap.
AI & ML Stack We Integrate
AI APIs
ML Frameworks
Search & Vectors
Vision & Documents
Data Pipelines
MLOps
Why Integrate AI With TedIT
We know your systems as well as we know the models, so integration is smooth and the results are measurable.
Full-Stack Fluency
We work across frontend, backend, and data, so AI features fit naturally into the product.
Measured Impact
Every feature ships with a metric and an experiment plan so you can see the lift.
Right-Sized Models
We use the simplest model that delivers—sometimes a rules engine beats an LLM.
Privacy-Conscious
Data minimization, anonymization, and secure processing are part of every design.
Great Fit For Products Like
Marketplaces & Listings Platforms
Real estate and services platforms that benefit from smart matching, search, and pricing.
SaaS Dashboards
B2B tools where forecasts, anomaly alerts, and natural-language queries add clear value.
Operations-Heavy Businesses
Companies processing documents, images, or support requests that AI can classify and route.
Frequently Asked Questions
Do we need a lot of data to use AI?
Not always. Many features—semantic search, document extraction, assistants—work with foundation models and your existing content. Predictive models do need historical data, and we assess that upfront.