AI Use-Case Strategy & Investment Case
Turn a portfolio of pilots into a funded, sequenced roadmap the board can hold to account.
AI & Automation
Generative AI, RAG & Agentic Workflows
Assistants, copilots and agents built with defined scope, measured accuracy and a complete record of what they did.
Overview
Generative AI is moving from individual productivity into institutional workflows. Assistants, copilots and agents can improve speed and service quality, but only when they are grounded in approved content, limited by controls and integrated into the process they support.
Capmark helps institutions design and deliver generative AI workflows that are grounded, controlled and auditable. We build retrieval over approved content, agents with defined scope and permissions, human checkpoints where accuracy matters, and evaluation that measures output quality before and after go-live.
Around the model, we automate the deterministic steps: approvals, exceptions, hand-offs and records. Where a workflow does not need a model, we say so and build the simpler automation instead.
What we do
We build assistants that ground answers in approved documents and cite sources a reviewer can check. Access controls are inherited from the source systems, so users only see content they are allowed to see.
We design agents inside a defined control envelope: approved tools, explicit permissions, human approval for material actions, failure handling and a record of every step. The limits are enforced in the system design.
We test output quality before and after go-live using curated test sets, adversarial testing and re-testing after prompt, data or model changes. Results are documented so risk, compliance and business owners can review performance.
We automate the steps around the model with conventional, testable software: hand-offs, approvals, exceptions, records and system updates. The model is used where judgement is needed, not where rules are enough.
We define the purpose, limits, model-risk treatment, human oversight, testing evidence and approval path before build begins. Sign-off is planned into delivery, not left as a late-stage blocker.
What to expect
A Senior Practitioner leads from day one. The first weeks test the use case against data readiness, risk appetite, infrastructure and control requirements, so weak cases stop early.
For viable cases, we design the workflow, evaluation framework and governance route before production build. Delivery then runs through controlled testing, go-live, runbooks and evidence packs.
Engagements range from use-case design to controlled production delivery, with optional post-deployment support.
Initial engagements run from design to controlled production, with optional post-deployment support.
01
Assess
Establish the current state, the constraints, the risks and the value at stake.
02
Define
Shape the target model and the business case with the executives who own the outcome.
03
Mobilise
Stand up the team, the plan and the governance around the outcome.
04
Implement
Design, build and test the change, with the business alongside.
05
Sustain
Cutover, hypercare and handover, so the business runs it under its own control.
The same five stages on every engagement, led by senior practitioners end to end. How we work
Client result
Proven in the work, not the pitch.
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The World's Largest Alternative Asset Manager · Financial Crime Transformation
The firm partnered with us to convert regulatory findings on AML control deficiencies into a resilient financial-crime framework: customer risk assessment, screening, enhanced due diligence and AI-assisted alert reduction, remediated to completion under independent scrutiny.
- 5
- Interlocking initiatives directed
- AI/ML
- Screening false positives reduced
- 2 clouds
- Azure and AWS delivery
- Evidenced
- To independent monitors
Read the case study
Insights
Related thinking.
Get in touch
Talk to a Partner about Generative AI, RAG & Agentic Workflows.
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