IBM watsonx

As an IBM watsonx partner, we assess your needs and deliver a complete AI ecosystem.

What we offer based on IBM watsonx (End-to-End)

Use-case workshops: Value creation, ROI analysis, risk assessment, “quick win” vs. scalable roadmap.
Data & process mapping: Identifying knowledge silos, bottlenecks, and required system integrations.
Feasibility plan: Architectural options (Cloud / Hybrid / On-prem), security models, estimation, and scheduling.
AI operating model: Roles, responsibilities, change management, and implementation metric systems.
Integrated services: Seamlessly fitting GenAI, data, governance, and automation into business processes.

Foundation models & modeling strategy:
- Selecting the right LLM (IBM Granite, third-party, open source; “bring your own model” flexibility).
- Prompting patterns, guardrail philosophy, and “system prompt” standards.
- Model gateway: Standardizing model access for controlled enterprise usage.

RAG (Retrieval-Augmented Generation) & Knowledge-based GenAI:
- Leveraging enterprise knowledge from structured and unstructured sources.
- Document processing, chunking, embedding, retrieval, and citation-backed responses.
- Capability to process scanned documents.
- Continuous improvement cycles for context and response quality.

Flows / Tool Calling / Agentic App Patterns:
- Tool calling, workflow-style composition, and integration patterns.
- RAG + Tool Calling combinations: “Ask–Search–Decide–Act” chains.
- Rapid Prototyping → Enterprise-grade Production.
- watsonx.ai flows engine: Specifically designed for building Agents, Tool Calling,
  and RAG-based flows.

Example use cases (watsonx.ai):
- Policy/Procedure assistant: RAG-based internal document bot with permissions and audit trails.
- Contract & compliance analyzer: Identifying risk points, references, and summaries.
- Sales proposal generator: Based on corporate catalogs, CRM data, and pricing rules.

Data access, integration & unified foundation:
- Hybrid, open data lakehouse approach: Analytics + AI on the same core.
- Accessing and interpreting structured and unstructured data in any environment.
- Unified governance across data sources (access, control, traceability).

Data productization:
- Data product design and “Single source of truth” support.
- Data quality program integration.

Example use cases (watsonx.data):
- GenAI-ready enterprise knowledge platform: Document store + metadata + searchability + access management.
- Customer service insight pipeline: Consolidating ticket/voice/chat logs for analysis and assistant training.
- Auditable AI Data Path: Data lineage and controlled data delivery.

End-to-End AI Governance:
- Directing, managing, and monitoring AI tools on a single platform across the entire lifecycle.
- Risk management and regulatory compliance (workflows, controls, reporting).
- Managing GenAI assets and ML models via a governance console.

Agent monitoring & production observability:
- Real-time monitoring of agents (output quality, risk, behavior).
- Policies, approval workflows, and auditability.
- Quality metrics, drift/bias detection (at model, app, and agent levels).

Example use cases (governance/compliance):
- EU AI Act / Internal model risk management: Risk categorization, documentation, and approval flows.
- GenAI release pipeline: Ensuring prompts/models/configs only go live after governance checks.
- Audit-ready AI: Automated logging, reporting, and decision trails (the “why” behind the answer).

Building & Running AI Agents:
- Multi-agent orchestration: Collaboration and task delegation between agents.
- Agent & Tool Builder: No-code or pro-code agent construction.
- Pre-built Catalog: Ready-to-use agents and tools for major service providers.
- Built-in governance and observability.

“No Rip & Replace” Integration: Seamlessly fitting into existing workflow/app ecosystems.

Example Use Cases (agents & automation):
- HR: Automating onboarding, equipment requests, and leave management.
- Finance: Pre-approving invoices/expenses and flagging discrepancies.
- IT Ops Copilots: Managing incidents, tickets, and permission requests.

Building and integrating multi-channel virtual agents and chatbots.
LLM-powered conversational search (RAG-based knowledge retrieval).
Human-in-the-loop: Seamless handoff to human agents and final human approval steps.

Code suggestions, explanation, documentation, unit testing, and language translation.
Developer support specifically for IBM documentation.
Modernization focus: Upgrading Java, mainframe, or IBM i environments.

IAM/SSO integration, role-based access, and audit log methodology.
Enterprise integration: Connecting CRM, ERP, Ticketing, DMS, SharePoint, Databases, and APIs.
Hybrid/Cloud/On-prem architectural design and maintainability.
Observability: Logging, metrics, tracing, and cost control (FinOps for AI).

Component selection: Tailoring the mix of watsonx.ai, .data, .governance, Orchestrate, Assistant, and Code Assistant to your needs.
Configuration & deployment: Tenant setup, permissions, project structure, and environments.
Full delivery: From PoC (Proof of Concept) to Pilot to Production, including knowledge transfer and support models.