Proven depth
AI for Financial Services
Fraud detection, risk modeling, and governed ML at transaction scale.
Business outcomes AI can move
- Protect revenue from fraud losses and chargebacks
- Reduce manual review cost per flagged transaction
- Shorten compliance reporting and audit cycle time
- Improve credit decision speed without increasing default risk
AI capabilities
Each capability maps to a business KPI, revenue, cost, risk, or cycle time, not model accuracy on a slide.
Revenue
Credit & risk scoring
Faster, more consistent underwriting decisions that expand approved volume while holding loss ratios flat.
Lead scoring & enrichment
Prioritize commercial and wealth prospects by conversion likelihood, SDRs spend time on deals that close.
Cost
Regulatory reporting automation
Extract, validate, and structure reporting data from disparate systems, fewer analyst hours per filing cycle.
Risk
Real-time fraud detection
Score 100% of transaction volume in sub-second latency, stop losses before settlement, not after review queues backlog.
Production use cases
- Real-time fraud pipelines
- Credit & risk scoring
- Regulatory reporting automation
Frequently asked questions
How do you scope AI for our industry?
Week-one discovery locks business KPIs—revenue, cost, risk, or cycle time—not model vanity metrics. We map your data sources, compliance constraints, and existing stack, then propose a fixed-scope mandate with acceptance criteria or redirect you to a simpler path.
Do you have experience in our sector?
Proven depth sectors (financial services, federal, real estate, telecom, higher education) reflect production systems we've shipped. Growth sectors are selective mandates where agentic and ML patterns transfer with industry-specific tuning. Biotech, insurance, and regulated work often runs under NDA.
What do we get at the end of an engagement?
Deployable code in your repos, monitoring and runbooks, documentation your team can operate, and knowledge-transfer sessions. The Production Standard is contracted in writing—no handoff to a junior bench after the sale.
What's the fastest way to start?
Book a discovery call or start with a bounded diagnostic: $2,500 Postgres audit, insurance claims-fraud diagnostic from $7,500, or the free MLOps readiness assessment. Each path surfaces whether AI is the right tool before a larger commitment.
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Selective intake · 2026
Your next system shouldship to production.
Agentic development from architecture through deployment, with the Production Standard contracted in writing.