Gudakesa AI Services & Solutions
Practical AI for real-world impact — from sensors and systems to decisions and outcomes
We design, build, and run production-grade AI across data, analytics, machine learning, computer vision, speech, optimization, and GenAI. We work across manufacturing, BFSI, pharma, public sector, and ERP-centric enterprises—delivering measurable gains in safety, quality, yield, and cost-to-serve.
Cloud-agnostic by design. We deliver on AWS, Azure, and Google Cloud, and integrate with your existing platforms (Databricks, Snowflake, Power BI, Tableau, etc.). Our default patterns are portable and avoid lock-in.
WHAT SETS US APART
Why Choose Gudakesa?
Our Services
AI Strategy & Value Roadmap
Prioritize use cases, define architecture and guardrails, and create a 6-12-month roadmap with a 90-day pilot plan.
Data Platform & Integration
Ingest, transform, and govern data across AWS, Azure, and GCP — integrating Databricks, Snowflake, Power BI, and Tableau.
Machine Learning & Optimization
Predictive models for forecasting, churn, pricing, and scheduling — with monitoring and drift alerts.
Computer Vision
Edge-ready solutions for defect detection, PPE compliance, OCR, and visual inspections.
Speech & Contact-Center AI
ASR, NLU, translation, and call summarization — powering real-time conversational intelligence.
NLP & Document AI
Extract, classify, and compare text from contracts, invoices, and documents — with semantic search and quality controls.
GenAI & Enterprise Agents
Build RAG assistants and process copilots integrated with enterprise systems and security guardrails.
Industrial & IoT AI
Predictive maintenance, energy optimization, and digital twins — built for advisory and closed-loop control.
MLOps & Platform Engineering
CI/CD for ML, feature stores, A/B deploys, observability, and cost governance.
Responsible AI & Governance
Policy-aware prompts, safety filters, human-in-the-loop, explainability, audits, and data/PII controls.
Representative Outcomes
Predictive Maintenance
Reduced unplanned downtime by 20% and improved productivity by 15% with proactive, ML-driven alerts.
R&D Knowledge Assistant
Accelerated formulation cycles by 35% and saved 800+ hours/month through automated knowledge retrieval.
Real-Time Speech Assistant
Enabled instant transcription, translation, and voice response, improving speed, accessibility, and compliance.
Enterprise Knowledge Chatbot
Delivered accurate, citation-based answers, reducing repetitive queries and boosting self-service efficiency.
Technology Backbone
Ingestion & Streaming
AWS Kinesis/MSK, Azure Event Hubs, GCP Pub/Sub; IoT Core/IoT Hub/IoT Core (GCP)
Lake & Storage
S3/ADLS/GCS; Lake Formation/Unity/Dataplex; Glue/Azure Purview/Data Catalog; vector stores (OpenSearch, pgvector, Elastic, BigQuery Vector)
Processing & Analytics
Glue/EMR/Athena/Redshift; ADF/Synapse/Fabric; Dataflow/Dataproc/BigQuery
ML & GenAI
SageMaker/Bedrock, Azure ML/Azure OpenAI, Vertex/PaLM/Gemini; Lex/Power Virtual Agents/Dialogflow for conversational layers
Apps & BI
QuickSight, Power BI, Looker; AppRunner/Amplify/Beanstalk, Azure App Service, Cloud Run/GKE/EKS/AKS
Frequently Asked Questions
No. We integrate them—QuickSight or Power BI, Databricks or EMR, Snowflake or Redshift—under a governed data platform.
OpenSearch Serverless or Elastic (vector), or relational + vector via pgvector (Aurora/RDS/Postgres). On GCP/Azure we use BigQuery Vector or Cognitive Search with vectors.
Citations by default, Guardrails/safety filters, human review for sensitive flows, data masking, auditable prompts, and model/data drift monitors.