Tiger Analytics

tigeranalytics.com

Tiger Analytics combines technology advisory with implementation and operationalization services focused on data and AI. Buyers can engage it for roadmaps and platform decisions, model and application development, or production ML deployment and maintenance. Its delivery evidence goes beyond software accelerators: published cases describe customer-specific assistants, NLP classifiers and data-platform rebuilds. TigerML and Insights Pro should be evaluated separately as tools or customizable solutions, rather than treated as proof of consulting delivery or assumed to be conventional software subscriptions. ^[1] ^[2] ^[3] ^[4] ^[5] ^[6]

Services and delivery

Predictive modeling and decision systems

Implementation. Builds predictive models for pricing, fraud risk and customer attrition; forecasting for demand, sales and costs; recommendation systems; and constrained optimization for inventory, scheduling and logistics. ^[2]

Language AI application engineering

Implementation. Integrates ready-to-use language models or builds custom models for conversational bots, intelligent search, semantic question answering, query-to-insights applications and code generation. ^[7]

Generative AI readiness and roadmap advisory

Advisory. Assesses AI readiness, prioritizes use cases and develops transformation roadmaps, adoption plans, governance materials and change-management guidance before implementation. ^[8]

Generative AI platforms and production deployment

Implementation. Develops foundational GenAI platforms, reusable agent components, interfaces and orchestration through production deployment. Operationalization scope includes CI/CD, drift detection, performance monitoring, traces, audit trails and human feedback. ^[8]

Production ML engineering

Implementation. Moves ML models from development into production through deployment engineering, delivery-pipeline automation and monitoring. The offering also includes custom GUI-based applications for ML solutions. ^[3]

ML operations maintenance

Managed services. Provides ongoing maintenance capability and DevOps-as-a-Service for ML environments. Buyers need to establish support hours, incident responsibilities and service levels in the engagement contract. ^[3]

Custom ML platforms and AI products

Implementation. Assembles custom platforms and configurable ML solutions with experiment spaces, model registries, deployment protocols, monitoring and downstream API integration. ^[9]

Data and AI application engineering

Implementation. Builds web and mobile interfaces, dashboards, custom visualizations, decision suites and low-code/no-code workflows. Delivery includes UX prototyping and cloud-native APIs and microservices within its data and AI application scope. ^[10]

Data and analytics technology strategy

Advisory. Advises on analytics roadmaps, data strategy and platform selection, including infrastructure for data integration, storage, processing and analytics. This is advisory scope, not evidence of migration implementation. ^[1]

Microsoft Fabric data-platform modernization

Implementation. Documented delivery includes rebuilding a legacy SQL Server, SSIS and SSRS environment on Microsoft Fabric, with OneLake integration, metadata-driven pipelines, Great Expectations validation, Power BI reporting and Azure DevOps release automation. ^[11]

TigerML toolkit

Software product. A reusable toolkit and accelerator for AI/ML workflows, also described as an end-to-end MLOps platform. The toolkit is distinct from Tiger’s separately documented implementation services. ^[5]

Insights Pro conversational analytics solution

Software product. An LLM-powered customizable solution for questions, joins, metrics and visualizations. The page describes access to underlying code and retained intellectual property; contractual licensing and ownership terms require verification. ^[6]

Company details

Technology and platforms

The generative AI service page names Azure, AWS, GCP, Databricks, Snowflake and on-premises deployment environments. These are documented service environments, but compatibility with a buyer’s chosen models, platform configuration and operational constraints still needs confirmation. ^[8]

Track record

For OSM Thome, Tiger reports building ASK OSMTHOME using Azure OpenAI and Azure Cognitive Search to retrieve safety-management procedures from structured and unstructured content. The assistant was integrated into safety and engineering workflows. Its reported 98.8% efficiency gain lacks sufficient calculation detail in the supplied evidence for procurement benchmarking. ^[12]

For an unnamed UK bank, Tiger describes intent clustering and resolution classification using BERTopic, UMAP and fastText. It reports processing 200,000 call and 60,000 chat transcripts, with over 80% intent accuracy and over 70% resolution accuracy. The customer identity and detailed evaluation design remain undisclosed. ^[4]

An unnamed US Fortune 100 P&C insurer case spans discovery workshops, 23 identified use cases, an execution roadmap and subsequent ML implementation for call quality and customer intent. Separately, Nature’s Path Organic Foods provides a named reference for Microsoft Fabric modernization rather than AI model delivery. ^[13] ^[11]

Scope and limitations

Published case outcomes are Tiger Analytics’ reports, not independently verified results. Several clients are anonymous, and evaluation methods are not fully disclosed.
The MLOps evidence supports maintenance capability but does not establish standard managed-service SLAs or 24/7 coverage.
Application engineering evidence is centered on data and AI applications; it does not establish unrestricted enterprise-software implementation scope.
The Nature’s Path case distinguishes delivered controls from future candidates such as Purview integration, sensitivity labels and row-level security. Those candidates should not be treated as completed delivery.

Questions to ask

Can you provide named references matching our use case, platform and production scale?
Who owns model evaluation, deployment, monitoring, retraining and incident response after launch?
What acceptance tests, human-review requirements and failure-handling procedures will be contractual deliverables?
Which model, cloud and on-premises configurations are supported for our proposed architecture?
What support hours, SLAs, handover documentation and ongoing operating costs are included?
What rights will we receive to accelerator code, custom code, models and subsequent modifications?

Overview