AI Use-Case Strategy & Investment Case
Build and prioritise an AI portfolio against business value, feasibility, data readiness, control requirements and total cost.
AI & Automation
Generative AI, RAG & Agentic Workflows
Build useful generative AI applications with defined scope and control.
Overview
What this means for your business.
Generative AI can support research, information retrieval and business workflows when the application is designed around a clear user need and an appropriate control environment.
Capmark builds retrieval over approved content, assistants and agent workflows with defined permissions, evaluation and human review where required. We assess retrieval, fine-tuning and vendor choices in the context of the application.
We also design the approvals, exceptions, records and deterministic automation surrounding the model, using simpler approaches where they better meet the requirement.
What we do
The expertise we bring.
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 team shaped around the work.
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.
Engagements may begin with a focused prototype or with a defined production requirement.
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
Relevant client experience.
Asset & Wealth Management · Financial Crime
Financial-crime controls rebuilt for the world's largest alternative asset manager
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.
- 100%
- AML findings closed and evidenced to the independent monitor
- 3
- Control domains rebuilt: customer risk assessment, name screening and enhanced due diligence
- 5
- Interlocking initiatives directed
Read the case study
Insights
Related perspectives.
Get in touch
Discuss Generative AI, RAG & Agentic Workflows.
Tell us what you are working through and the expertise you need.