Celebal Technologies

celebaltech.com

Celebal is a technology consulting and delivery provider rather than solely a software vendor or research business. Buyers can engage it for advisory work, such as data roadmaps and migration planning, or implementation work spanning enterprise data infrastructure, applications and deployed ML solutions. Its commercial AI services explicitly cover generative AI customization, predictive analytics and ML lifecycle tooling. These services are distinct from its AICXM product offering. Procurement should separate assessment, implementation, product charges and operational responsibilities rather than assume they form one standard package. ^[1] ^[2] ^[3] ^[4] ^[5] ^[6]

Services and delivery

Generative AI implementation and customization

Implementation. Builds customized enterprise AI solutions, including retrieval-augmented generation, multilingual voice assistants, agentic workflows, document summarization and code generation. The service also covers enterprise-system integration and LLM fine-tuning using proprietary datasets. ^[3]

Structured machine learning and predictive analytics

Implementation. Implements demand forecasting, predictive maintenance, supply-chain optimization and pricing optimization solutions. Its demand-sensing offering combines internal and external data with Databricks analytics and Power BI dashboards; other use cases include equipment monitoring and historical-data forecasting. ^[4]

MLOps and LLMOps implementation

Implementation. Implements repeatable model training, validation and deployment pipelines, model and data versioning, and performance and drift monitoring. Supported platforms include Azure Machine Learning and Databricks. The offering includes LLMOps workflows and LLM performance assessment, without establishing round-the-clock managed support. ^[5]

Data strategy and governance advisory

Advisory. Develops actionable data roadmaps and governance models, alongside cloud-spend analysis and resource sizing. This is advisory work for planning data investments; platform deployment and infrastructure configuration are separate implementation deliverables. ^[1]

Cloud data-platform implementation

Implementation. Implements cloud data platforms on Azure, AWS and GCP. Documented scope includes warehouse and data-lake deployment, infrastructure configuration and automated cost-monitoring setup. This establishes data engineering delivery, not AI model implementation by itself. ^[1]

ETL and ELT modernization

Implementation. Migrates enterprise data warehouses to data lakes and modernizes data pipelines. Deliverables include Databricks migration, pipeline architecture, pipeline development and migration, streaming ingestion and governance. ^[7]

Cloud migration assessment and planning

Advisory. Assesses infrastructure and database workloads through discovery and dependency analysis, then develops migration roadmaps. Buyers can distinguish these planning deliverables from the migration execution also described on the service page. ^[2]

Cloud migration implementation

Implementation. Executes database and infrastructure migrations, including VMware and Hyper-V workloads, with post-migration performance and security optimization. This is documented implementation scope rather than cloud strategy alone. ^[2]

Application modernization and delivery automation

Implementation. Modernizes applications through rehosting, replatforming, refactoring, rearchitecture and rebuilding. Implementation scope includes microservices, Docker and Kubernetes containerization, API-led connectivity, CI/CD pipelines, infrastructure automation and monitoring. ^[8]

AICXM customer-experience product

Software product. Packaged AI customer-experience solution with self-service bots, sentiment analysis, agent assistance, knowledge management and post-call dashboards. Its product page describes an associated implementation method; licensing, hosting and service packaging require clarification. ^[6]

Company details

Industries and client fit

The supplied cases establish delivery in telecommunications contact centers and banking workflows. U Mobile provides a named reference for speech analytics, while the bank remains unnamed. These references are useful for buyers evaluating call-quality monitoring, compliance review or low-code service-request automation; they do not establish equivalent experience across every advertised AI use case. ^[9] ^[10] ^[11]

Track record

For U Mobile post-call analytics, Celebal reports deploying Amazon Transcribe, SageMaker NLP models, Step Functions, Lambda and OpenSearch dashboards, with CRM/ticketing integration, access controls and encryption. Models detected sentiment, intent and compliance deviations. Celebal reports that evaluation time fell from days to minutes. ^[9]

A second U Mobile case covers live-call analytics with English/Bahasa Malay transcription, SageMaker models, automated escalation alerts, OpenSearch and Airflow. Celebal reports a 30% reduction in average handling time and a 25% improvement in first-call resolution. The supplied notes do not establish the measurement methodology; this is another project for the same customer, not a second independent client reference. ^[10]

For an unnamed bank, Celebal reports deploying InstaServices within a secure Azure environment using Power Apps, Power Pages, Dataverse, Power Automate, Power BI and Azure API Management. The documented scope supports low-code implementation and process transformation, but does not establish AI delivery for that project. ^[11]

Scope and limitations

Named-customer AI evidence in the supplied research is concentrated in two U Mobile projects. Named deployment references for RAG, LLM fine-tuning and individual predictive use cases remain unverified.
ML monitoring and optimization are documented, but contractual support hours, SLAs, incident ownership and retraining obligations are not established.
AICXM licensing, hosting arrangements and the separation of product charges from implementation services are unspecified.
The supplied evidence does not establish project pricing, typical engagement size, delivery locations or staffing commitments.

Questions to ask

Who owns architecture, production deployment and acceptance, and what deliverables are included in each project phase?
Which comparable customer references can be contacted for the specific AI, migration or application scope being purchased?
What model-evaluation criteria, data-residency requirements and cloud dependencies will govern the solution?
Who owns source code, prompts, training data outputs and fine-tuned models?
How are product fees, implementation costs and ongoing support separated, and what SLAs and retraining obligations apply?

Overview