Financial Services
Built for financial services firms where model risk, compliance documentation, and operational accountability have to be designed in from the start.
Sound Familiar?
Financial services firms have invested in AI. Most of the gaps are in the governance, documentation, and operational infrastructure around the models.
When regulators arrive or an exam is scheduled, teams spend weeks pulling evidence from disconnected systems. Decisions made months ago are impossible to reconstruct with confidence.
Models are running in production, but the validation documentation, ongoing monitoring, and explainability layer required for SR 11-7 and model risk management frameworks are incomplete or informal.
Credit decisions, exceptions, and policy overrides move through email and verbal approvals. There is no structured record of who approved what, under what criteria, at what time.
Leadership sees a lagging view of where the business stands. Exception rates, policy breaches, and concentration risk surface in monthly reports, weeks after decisions that could have been caught earlier.
What We Build
01
We build the technical foundation that makes AI governable: decision logging, model monitoring, explanations for each output, and the challenge documentation that model risk management frameworks and examiners expect.
02
Approval chains, exception handling, and policy override workflows that produce a structured audit trail as they run. Every decision is logged, timestamped, and linked to the policy that authorized it.
03
We replace fragmented manual reporting with structured data pipelines and real-time dashboards, giving leadership accurate visibility into exception rates, concentration risk, and portfolio performance while there is still time to act.
In financial services, AI projects usually run into trouble over the governance around the model. We build that governance first, so the AI you deploy is something you can defend to an examiner.
Client Work
Healthpeak Properties — Healthcare REIT
Healthpeak manages one of the largest healthcare real estate portfolios in the country. Despite the scale, their lease approval and legal document workflows ran almost entirely on email, shared drives, and phone calls, with no reliable visibility into deal status and no structured record of decisions.
Read the full case studyWhat We Built
LeaseIQ: three connected workflow systems covering lease approval, legal document review, and tenant improvement tracking, with approval tiers set by deal size and electronic signatures through Documentum.
Outcome
Real-time visibility into every active transaction across the portfolio. Bottlenecks became visible and manageable. Deal teams could analyze, forecast, and approve parameters at every stage.
Intelligence Opportunity
Automated risk scoring on deal terms, AI-assisted detection of clause deviations, and negotiation pattern analysis, built on the structured deal data the platform already holds.
Start Here
If you are working out where AI fits, the Blueprint is a short readiness assessment. Your people each spend 10 to 15 minutes in an anonymous interview, and we come back with a scored report covering the governance gaps to close, the first workflows worth taking on, and what has to be in place before any development begins. The fee is credited toward implementation.