Capgemini connects enterprise AI engineering with the processes that must run it

Capgemini helps organizations turn AI capabilities into working enterprise systems. Its offer includes strategy, custom generative AI, software engineering and RAISE, a modular accelerator for agent development and operation. The buying decision is therefore about delivery responsibility as much as software. A useful engagement should explain how company knowledge becomes a maintained application, who checks its outputs and who operates it after the first demonstration.

01 / ProductConnect strategy, custom applications and agent operation

Capgemini’s generative AI portfolio spans strategy, custom enterprise solutions, customer experience and software engineering. These are different entry points into a services engagement. A buyer with an approved use case needs different help from a leadership team still deciding which business process to change. Clarify that starting point before comparing proposals.

RAISE is presented as a modular foundation for accelerating, building, integrating and operating agentic AI. Capgemini describes customizable agents, connections to applications and data, and lifecycle governance. Those descriptions support examining RAISE as a delivery accelerator. They do not establish a standard downloadable product with one universal set of entitlements.

The Custom Generative AI for Enterprise offer organizes work around data, model, business experience, and testing and trust. This is a useful way to separate responsibilities: source preparation, model behavior, the user’s task and validation all need attention. Better generation alone cannot repair conflicting operating instructions or an unclear approval process.

02 / AudienceLook for a problem that crosses organizational boundaries

Capgemini is most relevant when applying AI requires coordination among a business team, data owners and engineering operations. A manufacturer may have maintenance knowledge spread across manuals, work orders and specialist staff. The difficulty is assembling the right evidence for a specific asset and then maintaining that evidence as equipment and procedures change.

Its AI strategy service covers use-case selection, business cases, technology options, operating processes and people. That can help when the organization has many ideas but no agreed priority. The useful output should be a decision and its assumptions, with a clear route to implementation, rather than a long list of possible AI applications.

Compare the IBM blueprint when data and governance tooling are the main selection questions. The Microsoft blueprint is relevant when the organization already has a Microsoft application and cloud environment. A service provider can help implement either kind of stack; distinguish the delivery partner decision from the underlying technology licenses.

03 / WorkflowA proposed maintenance knowledge service for one asset family

Consider a maintenance team preparing technicians for a recurring equipment fault. This proposed workflow produces a source-backed briefing for qualified staff, rather than instructions that automatically control machinery. Choose one asset family and an existing maintenance owner. The first deliverable is an agreement about which documents and records may support the briefing.

Begin with approved manuals, service bulletins and closed work orders. Record model numbers, revisions, effective dates and the equipment to which each instruction applies. A bulletin for a similar-looking machine may be dangerously irrelevant. The retrieval layer needs those distinctions in its metadata so the model does not have to infer equipment compatibility from prose alone.

Use the custom enterprise approach to design an application that answers a bounded question: what approved evidence should this technician examine for this fault on this asset? Have it return source passages and the reason each was selected. It should explicitly say when the asset identifier or manual revision is missing, rather than fill gaps with general maintenance knowledge.

RAISE’s integration and operation capabilities provide a topic for the technical workshop. Ask the delivery team to demonstrate how the application connects to the document repository and maintenance system, applies the caller’s access rights and records a source revision. Treat these as acceptance criteria for the proposed implementation, not as capabilities proved by a portfolio diagram.

Separate generating the briefing from updating a work order. Initially, allow technicians to read the briefing and open the original evidence. A later write action should identify the exact record, proposed change and authorized operator. This makes it possible to assess retrieval usefulness before granting an agent operational authority that is difficult to undo.

Build a representative evaluation set with duplicate manuals, superseded bulletins, unfamiliar fault codes and records that describe unsuccessful repairs. Ask maintenance specialists to judge whether the briefing identifies the relevant evidence and preserves uncertainty. Counting fluent answers would miss the central failure mode: a persuasive recommendation drawn from the wrong equipment version.

The software engineering offer describes platform engineering, validation gates, observability and measurement across delivery. Apply those principles to the briefing application itself. A source update or model change should trigger targeted evaluation before release. Someone should own the regression cases after the implementation team hands over the system.

04 / PricingPrice the engagement and its dependencies separately

The consulted service pages direct readers to contact Capgemini or request a demonstration. They do not establish public per-seat or per-token rates for RAISE or a fixed price for a custom application. A quote should therefore be tied to a defined project and operating scope. A cloud model’s public tariff would not describe the cost of the complete Capgemini engagement.

For the maintenance example, separate document preparation from application development and continuing operation. An apparently inexpensive pilot may rely on a manually cleaned document set that no one has budgeted to maintain. Ask the proposal to identify those preparation tasks and show how new manuals and service bulletins will enter the production process.

Also identify technology charges paid directly to other vendors. Hosting, model use, search infrastructure, software licenses and monitoring may sit inside or outside the service agreement. Confirm the arrangement rather than assuming one bundled charge. The useful comparison is the cost of maintaining an accepted briefing workflow, including specialist review and source upkeep.

05 / DistinctionsEngineering continuity matters more than a catalog of agents

Capgemini’s combination of strategy, custom solutions and software engineering is relevant when the hardest part of AI adoption is the transition between stages. A use case can be attractive in a workshop yet fail because its evidence is inaccessible or its owner cannot maintain it. An integrated delivery conversation can expose that problem before implementation begins.

RAISE’s modular positioning also gives buyers a way to ask which components are reusable and which are specific to their process. For maintenance knowledge, a document connector might be reused across asset families, while the compatibility rules and evaluation examples remain domain-specific. Treating both as generic would hide the work that makes the application dependable.

The product page describes flexibility across cloud, on-premises and hybrid environments. Read that as a claim to investigate for the proposed configuration, not a guarantee of effortless portability. Require a concrete account of which components can move, how data and configuration are exported, and what remains tied to a particular hosting or service arrangement.

06 / QuestionsMake the handover and evidence boundaries explicit

What does the customer receive at the end of each phase? For the maintenance service, useful artifacts include source mappings, evaluation cases, configuration, deployment instructions and a clear responsibility map. A functioning demonstration is insufficient if the operating team cannot reproduce a release or diagnose an incorrect source match.

Which parts of the application rely on model customization, and which use retrieval from maintained sources? The custom AI page discusses company knowledge, but a particular solution still needs an explicit design. Frequently changing maintenance instructions may be easier to govern as versioned source material than as knowledge hidden inside a model adaptation.

How will Capgemini’s claimed benefits be assessed locally? The RAISE page includes vendor-reported outcomes and broad industry claims. They are not forecasts for this project. Agree a baseline using the organization’s own briefing preparation time, incorrect-source incidents and reviewer effort, then compare the pilot against those measures with the same task definition.

07 / DecisionChoose a partner against a deliverable the business can inspect

Capgemini is a meaningful candidate when enterprise AI needs software engineering and organizational change alongside model selection. Specify a small service whose output has a clear owner and observable quality. That makes the proposal easier to assess and reduces the temptation to buy a broad transformation promise before understanding the required work.

For maintenance knowledge, the desired result is a briefing that points qualified staff to current, applicable evidence. If the pilot can show that result and an operating team can maintain it, the organization has a basis for expansion. If the source material remains inconsistent, resolve that dependency before increasing the number of agents or users.