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Technology capabilities

End-to-end engineering,
one delivery team

End-to-end technology engineering across software, data, AI/ML, and real-time systems —
cloud-native architectures and scalable methodologies built to last.

From idea To production
  1. Discover
  2. Design
  3. Build
  4. Test
  5. Deploy
  6. Scale
01

Enterprise Software Engineering

Cloud-native and API-driven systems designed for scalability, reliability, and performance.

JavaScalaPythonFastAPIMicroservices
Cloud
AWS, Azure, GCP
Methodologies
Cloud-native architecture, API-first development, distributed systems, scalable and fault-tolerant design
02

Data Engineering & Governance

Data platforms for large-scale processing, streaming, integration, governance, lineage, and compliance.

DatabricksApache SparkKafkaApache PulsarAirflow
Architecture
Medallion architecture, data lakes, streaming and batch processing
Capabilities
Data integration, data governance, data lineage, data quality, auditability, and large-scale data processing
03

AI & Machine Learning Engineering

End-to-end AI/ML solutions covering predictive models, intelligent applications, automation, and production AI pipelines.

PythonLangChainLangGraphXGBoostLightGBMCNNYOLO
AI
LLM applications, RAG, AI agents, predictive analytics, computer vision
Infrastructure
Pinecone, FAISS, model and pipeline observability
Methodologies
End-to-end ML pipelines, model evaluation, monitoring, and production deployment
04

Real-Time Conversational AI

Real-time voice and video systems with STT, TTS, VAD, AI agents, and low-latency conversational experiences.

WebRTCPipecatDaily APIDeepgramCartesiaElevenLabsSarvam
Capabilities
Real-time voice and video, speech-to-text, text-to-speech, voice activity detection, AI voice agents
Methodologies
Low-latency pipeline optimization, multi-vendor architecture, scalable real-time session management

Engineering approach

How we work

We work with an API-first, microservices, cloud-native engineering approach, with emphasis on scalability, security, performance, reliability, and maintainability.

  • →Automation-driven delivery and CI/CD workflows
  • →Observability-focused implementations for production monitoring
  • →Distributed systems design with fault-tolerant patterns
  • →Clear handover from MVP to production, and continuous improvement after

Talk to our engineers