60 posts tagged with "Solution Engineering"

Case C parent. A multi-country banking group issues an “enterprise agentic AI” RFP: aggressive timeline, outcome guarantees on cost reduction, broad liability for model outputs, vendor-shaped specs, unclear data residency. Partners want the logo. Pursuit cost is high. This parent states the decision pattern; expanded articles show qualification/independence and commercial realism.

Case C · Expand II (parent: Bid/no-bid overview). Translate Conditional pursue into commercials that are deliverable—or walk away cleanly.

Case C · Expand I (parent: Bid/no-bid overview). Run structured qualification and independence before heavy proposal investment.

This hub is the parent entry for three AI solution engineering case-study series. Each series has a short parent overview and expanded articles you can study in depth. Use the same checkpoints on every case: signal → qualify → reframe → shape → engineer → prove value → land.

Enterprise generative AI engineering is no longer limited to writing prompts and connecting an application to a large language model. A production AI system must combine software engineering, governed data access, model routing, retrieval, tool execution, evaluation, monitoring, security, cost control and continuous delivery.

Databricks addresses these requirements through an integrated set of capabilities covering the complete GenAI lifecycle: querying foundation models and agents, building custom and low-code agents, connecting agents to governed tools, preparing structured and unstructured data, implementing retrieval with AI Search, deploying agents and applications, governing traffic through Unity AI Gateway, tracing with MLflow, evaluating and monitoring quality, operationalising through LLMOps, and delivering governed experiences through Genie One.

Deploying a large language model is not simply a question of choosing between an open-source model and a commercial API. It is an enterprise risk decision involving:

• What information the system will process.
• Where that information will travel.
• Who can access the model, prompts, outputs and logs.
• What actions the model can perform.
• How the organisation will detect failures or attacks.
• Which party is accountable when something goes wrong.
• What evidence can be presented to auditors, regulators, customers and executives.

Case A parent. Meridian Insurance (composite) faces rising cost-to-serve. The board wants a GenAI plan this quarter. The COO sponsors; the CRO fears hallucination, privacy and audit gaps. Knowledge is fragmented; there is no enterprise evaluation framework. This overview states the decision and outcome. Expanded articles cover discovery, solution/commercial design and delivery.

Case A · Expand III (parent: Meridian overview). After preferred bidder: contract, land the team, measure value, expand only when earned.

Case A · Expand I (parent: Meridian overview). From market signal through discovery to a written problem reframe.

Case A · Expand II (parent: Meridian overview). Shape the offer: options, TOM, architecture, value case, pitch and negotiation.