Finance

Prospecting, sales workflows, third-party data

Building in finance?

How engagements start here

Incomplete data, brittle handoffs, and compliance rules that rule out a lot of tooling. Reliability, auditability, and a data path security can defend are baseline here, not extras.

Common challenges

Stale or incomplete prospect data

Manual research and consolidation that waste rep time

Compliance constraints on tools and data sources

Siloed internal data slowing safe decisions

AI adoption that outruns what compliance can actually sign off on

Outcomes

Reps walking into calls with real context, not cold research

Enrichment that surfaces the right leads earlier

Controls over which data sources enter the workflow and how they move

Capability areas

Prospect enrichment and third-party pipelines

Internal ops tooling for sales and ops

Deal flow and sales workflow platforms

Edge or in-environment processing when firm or client data has to stay put

SOC 2 and ISO 27001 architecture

Engagements

AI Workflow Optimization

AI is already in production. Costs are climbing, performance is uneven, or one frontier model is doing every job. I audit the workflow, run evals task by task, and hand back a system that costs less and holds up better. You keep the eval suite.

AI & Automation Readiness

You want to automate, but data is scattered, systems don't talk, or AI shipped with no harness. You need clean accessible data, addressable systems, guardrails around anything stochastic, and a data path security can stand behind.

Operational Automation

An expensive process in back office, ops, or analyst work that people keep saying should be automated. I map it with the people who run it, measure what it costs today, and decide what should be fully automated, what needs a human in the loop, and what shouldn't be built at all. When we do build, it's with guardrails, and with a data path that can run at the edge when records can't leave your environment.