Cloud platforms carrying smart home and device services for subscribers, engineered to grow with the installed base.
- Platform architecture for connected services
- Device, user and service data integration
- Engineering for scale, reliability and secure operation
One harmonized ontology across plans, bundles, devices and partner data, built for AI to consume as readily as your BSS.
- Offer and partner data harmonization at scale
- Semantic and LLM-layer record matching
- Pricing and contract data normalization
AI validation and assisted resolution across care operations, keeping ticket volumes falling while the base grows.
- AI validation before data hits care systems
- Knowledge-grounded support assistants
- Resolution ahead of the ticket
Partner, interconnect and vendor contracts extracted, tracked and surfaced before renewal dates instead of after disputes.
- Contract and term extraction at scale
- Obligation and renewal tracking
- Exception routing to human reviewers
RAG-grounded assistants giving field technicians cited answers from manuals, histories and job data, wherever the job is.
- Copilots grounded in your documentation
- Cited, auditable answers by default
- Built for mobile, on-site use
AI-driven test generation across apps, platforms and integrations, for release confidence at subscriber scale.
- End-to-end test architecture across the stack
- AI test generation for deeper coverage
- Automation woven into release pipelines
Connected Services · Smart Home · Cloud Platform for Smart Home Solutions
The Challenge
- A growing fleet of connected home devices
- Device, user and service data across many touchpoints
- A platform expected to scale with the subscriber base
Our Approach
- Cloud platform architecture for connected devices
- Device and service integration across the ecosystem
- Engineering for scale, reliability and secure operation
Outcomes
Catalog Data · Delivered in Food Services · Catalog Harmonization at Extreme Scale
The Challenge
- The same product described differently across every source
- Manual, time-intensive matching across catalogues and formats
- Complex pricing across millions of combinations
Our Approach
- Document processing capturing product and pricing data at scale
- Attributes normalized to a unified ontology
- Record matching with attribute, semantic and LLM layers
Outcomes
Care Operations· Delivered in Food Services · AI Validation and Support Relief
The Challenge
- Manual, error-prone operational data entry
- Content slow and costly to produce
- Bad data driving support tickets
Our Approach
- AI validation of data before it reaches operational systems
- AI content generated on demand
- Cloud signage and systems integration
Outcomes
Production-Grade Delivery
Human in the Loop, Always
Reliability as a Design Input
Production, Not Pilots
Discover
Design
Build
Run & Scale
Only behind the same discipline as everything else in your stack: staged rollouts, validation gates and human approval on anything that acts.
By working across it, not rewriting it: the harmonized catalog layer and automations plug into the systems you run and feed them cleaner data.
Yes. Assistants are grounded in your knowledge base and deployed in your channels, with a person always one handoff away.
It stays in your environment under your controls, with the audit trails regulated operators expect.
They are designed for the truck and the tower, not the office: mobile-first, and resilient to patchy connectivity by design.