TBW / AI solutions

AI Solutions

Design an AI portfolio that can be governed and operated.

Discuss your architecture

AI Solutions: Business need to Approved data to Architecture to Model and retrieval

The business problem

Disconnected pilots, uncontrolled data flows and unclear ownership make production adoption difficult.

Unclear data and model boundaries
Disconnected systems and ownership
Production controls added too late

Explore AI security and governance →

What TBW provides

Strategy and roadmap

Designed around your environment, requirements and operating constraints.

Architecture across private, local, cloud and hybrid models

Designed around your environment, requirements and operating constraints.

Enterprise LLMs, RAG, agents, automation, voice, integrations, training and support

Designed around your environment, requirements and operating constraints.

Architecture and workflow

AI Solutions workflow: Business need to Approved data to Architecture to Model and retrieval

  1. Business need
  2. Approved data
  3. Architecture
  4. Model and retrieval
  5. Integrations
  6. Governed operation

Architecture is selected after reviewing the actual data, identity, operations and integration boundaries. See enterprise integration →

A representative workflow

A team evaluates where a request can go before connecting it to approved enterprise data. This illustration describes a design pattern, not a deployed customer case study.

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Illustrative operator and approval gateway: Human operator to Approved gateway to Bounded action to Audit trail

Technologies involved

Technology categories below are examples for architecture discussion, not a vendor partnership or an assertion that every tool is deployed.

LLMsretrieval systemsAPIsidentity platformsobservability tools

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Implementation methodology

  1. Discover requirements
  2. Assess data and access
  3. Architect and prototype
  4. Integrate and secure
  5. Validate and operate

Consulting and training →

Security and governance

Classify data; authenticate users; apply least-privilege model and tool access; review retention and residency; test prompt injection and unsafe retrieval; log meaningful decisions; require human approval for sensitive actions. The controls and assurance required depend on your environment.

Review AI security →

Related services

Browse all AI solutions →

Frequently asked questions

Which deployment model fits?

The answer depends on data classification, latency, capacity, operations and policy. Hybrid designs may separate workloads.

How are access and data boundaries defined?

Start with a data inventory, named owners and role-based permissions. Test the actual flow before production and revisit access as systems change.

What is the next step?

Bring a bounded use case, constraints and existing architecture to a consultation. No deployment or result is assumed before assessment.

Plan the next architecture decision

Discuss the workload, data boundaries, integrations and operational ownership with TBW.

Request a consultation

Consultation path: Requirements to Architecture to Review to Next steps

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