AI work that holds in finance.
Three finance engagements across shadow AI, production agents, and AI transformation economics.
75Shadow AI cases mapped
60 → 95%FP&A accuracy reached
$20MNOI / NPV uplift modeled
100+Customers reached after rollout
Start with the operating read.
The strongest case is the one that shows the before-state, the evidence, and what changed by the next operating meeting.
Featured case
One operating read across eight acquired entities.
75 shadow AI cases, 20 unauthorized MCP wires, 12 sanctioned tools moved into cadence, and one board-ready AI value/risk view.
Read the case study →Read the proof by shape.
Each story shows the operating shape, the evidence, and what shipped.
AI Audit + AI Engineering + AI GovernanceRead the case →AI Engineering + EvalsRead the case →AI TransformationRead the case →
One operating read across eight acquired entities.
75 shadow AI cases, 20 unauthorized MCP wires, 12 sanctioned tools moved into cadence, and one board-ready AI value/risk view.
6 acquisitions in 24 months, 8 legal entities, heterogeneous tooling, and two PE owners asking for one view of AI value and risk.
75Shadow AI cases
20Unauthorized MCP connections
12Sanctioned tools
#1 / #2Engineering, then GTM
Building AI Trust and Reliability in Financial Services.
60% to 95% FP&A accuracy, 20% fewer false positives, 90+ regression scenarios, and rollout to 100+ customers.
FP&A AI accuracy stalled at 60%. The product needed a stronger context layer, better retrieval, tuned prompts, reviewer agents, and deterministic SQL paths for finance-critical answers.
60 → 95%FP&A accuracy
20%False positives reduced
90+Regression scenarios
100+Customer rollout
AI value, modeled in NOI terms.
$20M modeled 10-year NOI/NPV uplift, 20% lower CapEx, and 6% improved leasing value.
AI capability center stand-up across leasing, CapEx, predictive maintenance. No central data layer, asset-level data siloed across outsourced operators.
$20M10-year NOI / NPV uplift
20%CapEx reduction
6%Leasing value improvement
Match the workstream to the evidence.
Pick the engagement that matches the board question in front of you.
AI Audit
Find shadow AI, unauthorized MCP paths, duplicate tooling, and the board-readable view that decides the next move.
See AI Audit →AI Engineering + Evals
Fix context, retrieval, reviewer agents, SQL fast paths, and release evals until the product is ready for customers.
See AI Engineering →AI Transformation
Turn AI ambition into operating economics: workflow changes, data accountability, and value evidence in finance terms.
See AI Transformation →Start with the 2-week AI Audit.
Leave with the operating read: AI value, AI risk, fluency gaps, owners, and the next funded workstream.