Professional Services Enterprise AI Platform Global

Every team’s AI, one governed platform, now a $100M partnership.

A global growth and creative consulting firm with over $200M in annual revenue unified its AI onto one governed platform, then turned that platform into a $100M partnership.

$33.3M /yearAverage annual value, modeled evenly across the 3-year term
~$128,000Average value per working day, modeled evenly across the term
3Workbenches in the initial product roadmap
01 · Client

Global scale. AI adoption running team by team.

A global growth and creative consulting firm, working across 15 offices for some of the largest consumer, healthcare and financial brands in the world.

AI had already proven its value inside the firm. It had never been unified into one platform.

Annual revenue$200Mn+
Employees600+
Global footprint15 offices, three continents
FunctionGrowth & creative consulting
OwnershipIndependent, privately held
ClientsGlobal consumer, healthcare & financial brands
02 · The problem

Where the fragmentation was costing them.

Every team found real value in AI. But every team built it differently.

01

No shared platform

Multiple AI models operating independently, with different teams following different approaches for similar work.

02

Knowledge stayed local

Valuable consulting expertise developed inside individual engagements was difficult to reuse elsewhere.

03

Workflows evolved on their own

AI workflows developed independently across teams, limiting consistency across the consulting organization.

04

Governance became harder to see

Decentralized adoption made oversight and operational visibility increasingly difficult.

03 · Native limits

What a shared platform actually had to do.

Adding another AI tool wasn’t going to fix a governance problem. Four requirements mattered more than any single feature.

The firm didn’t need another point tool. To run AI across the whole organization, 4 things had to be true together.

01

Support every model, not one

Give consultants access to multiple frontier and open-source models through one governed interface.

02

Make institutional knowledge reusable

Surface the firm’s best frameworks and methodologies for every engagement, not just the one that created them.

03

Standardize without slowing teams down

Bring consistency to consulting workflows while preserving flexibility for different kinds of work.

04

Govern from a single layer

Role-based access, centralized administration and auditability, from day one.

04 · What Lyzr built

One enterprise AI platform, built to operate the whole firm’s AI.

Working closely with the firm’s teams, Lyzr designed and implemented a centralized enterprise AI platform that serves as the operational layer for AI across the organization.

Platform Core
Multi-model architecture
Consultants use the model best suited to each task through one governed interface.
Multi-agent orchestration
Supports complex use cases that rely on several agents working together.
Persistent context & memory
Workflows build on previous interactions instead of starting from zero.
Governance Layer
Role-based access control
Centralized administration and strict data separation across teams.
Auditability
Every action logged and traceable across the platform.
Standardized workflows
Similar consulting activities follow repeatable, consistent processes.
05 · Architecture

Deployed entirely inside the firm’s own environment.

No new infrastructure to stand up. The platform runs inside the firm’s existing environment, authenticated through its existing SSO.

Model access is provisioned per user permission. Every agent and application deploys inside the same environment, so governance and security stay aligned with the firm’s existing standards.

Platform architecture - inside the firm's own environment, a consultant signs in through the firm's existing SSO for scoped access; in Build they describe a need, agents are assembled on the firm's frameworks and an application is deployed inside the same boundary; in Run the end user signs in with the same enterprise identity, agents execute under the same controls and a logged, traceable response is returned; usage is metered per user and a governance bar covers role-based access, centralized administration, audit trail and data separation - nothing leaves the firm's environment.
06 · Controls

How the platform keeps output governed.

Every application built on the platform inherits the same governance backbone.

Grounding
Every output traces back to the firm’s actual guidelines and knowledge base, not a generic model.
Guideline enforcement
Conflicts with brand or firm standards are flagged, not silently resolved.
No silent overrides
When something conflicts, the system asks for direction instead of guessing.
Access control
Role-based permissions and strict data separation across teams and engagements.
Audit trail
Every action and model interaction logged for oversight.
07 · Re-imagined workflow

From individual tool use to a shared operating model.

Before: every team ran its own models, workflows and frameworks. After: one platform, common workflows, shared knowledge.

AfterThe agents are already built. Everyone starts from the same set.
One shared repository behind all four functions, entered the same way every time.
Client engagementWhich consultingfunction?THE SHARED REPOSITORYpre-built and standardizedPick the one you needpurpose-built for the taskREADY TO DEPLOYEnter the promptthe brief, in plain languageThe agent runs itsame architecture every timeStrategy out, then to the clientthe same structure on every engagementevery engagement enters hereGOVERNANCEOne control layer across every step: role-based access, audit trail, client data isolated per engagement
BeforeEveryone built their own agents.
Every team found value in AI. Each of them found it independently.
Client engagementWhich consultingfunction?Research · StrategyBrand · ContentChoose a modeleach function on a different oneFOUR SEPARATE PATHSBuild your own agentfrom scratch, every engagementRun the engagementanalysis, frameworks, messagingDeliver to the clientreports, decks, recommendationsCan anyone elseuse that agent?noIt stays with its authorindividual prompts and documentsthe next one builds it againNO COMMON PLATFORMDecentralized adoption made governance and operational visibility increasingly difficult
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08 · Results

What changed.

The internal platform became the foundation for a three-year strategic partnership.

MeasureBeforeAfter
AI adoptionTeam-by-team, inconsistentStandardized on one common platform
Knowledge reuseLocked inside individual engagementsDiscoverable and reusable enterprise-wide
Workflow consistencyVaried by teamRepeatable across similar consulting activities
GovernanceDifficult to see across decentralized useCentralized, auditable oversight
AI’s role in the firmAn individual productivity toolA firm capability, and a client-facing product
$100MJoint go-to-market value, three-year partnership
600+Consultants on one governed platform
3Workbenches in the initial product roadmap
09 · What’s next

From platform to product roadmap.

The platform is now the foundation for a growing roadmap of AI-powered workbenches, starting with Campaign Marketing, Agentic Workflow Design, and Brand Management, extending the firm’s consulting expertise directly into clients’ hands over the life of the partnership.

3-year, $100M joint go-to-market partnership, underway

Got a use case in mind?

Platform, engineers and governance all in. We’ll map your workflow against the same four tests this one had to pass.