Field engineering for AI
FDE AI Implementation
Arvya brings a founder-led, field-engineering motion to financial firms: workflow discovery, integration with the stack the firm already owns, verified memory buildout, and deployment in the firm's own tenant.
Book a Working SessionFDE AI implementation means embedding with the customer's workflow, understanding the real operating path, and shipping software that works inside production systems. Arvya uses this motion for deal firms: map the workflow, connect the CRM, notetaker, and data room the firm already uses (configured per deployment), build the verified deal memory, ship the approval-first workflow, and prove it with receipts and a measured baseline rather than a demo.
// who this is for
Financial firms that need a technical AI implementation partner, not only a SaaS login.
Teams with complex data sources, custom CRM schemas, approval requirements, and adoption risk.
Operators who want AI deployed into the actual work path, in their own cloud tenant, rather than left as a disconnected demo.
The problem
- Most AI pilots fail after the demo because nobody owns the workflow integration.
- Financial services workflows require permissions, auditability, source citations, and human approval before writes.
- The best first use case is firm-specific (its schema, its stack, its process) and cannot be solved by a horizontal template.
What Arvya does
- Works directly with the firm to define the workflow, sources, users, approval path, and output format.
- Builds the reusable verified-memory layer underneath the first workflow, so every later workflow starts smarter.
- Ships production software with proof built in: cited outputs, approval queues, and a read-back receipt on every write to a system of record.
// how it works
Start with one painful workflow and one clear owner; measure the baseline before building.
Prototype against real data boundaries with source citations from day one, in the firm's own tenant.
Deploy, measure usage against the baseline, tune outputs, and expand to adjacent workflows.
// works with
Frequently asked questions
What is founder led AI implementation?
It is a hands-on field-engineering motion: the team that builds the product also learns the customer's workflow, builds against real system constraints (the actual CRM schema, the actual approval path, the actual cloud tenant), and deploys software users adopt because it fits how they already work.
Why does Arvya use this model?
Because deal firms differ in exactly the places that matter: CRM schemas, notetakers, data rooms, approval rules, cloud environments. A field-engineering motion configures Arvya to each firm's stack (bring your own CRM and notetaker, Azure native or Google Cloud per deployment) while compounding one reusable verified-memory platform underneath.
// related
AI Implementation for Financial Firms →
Arvya helps financial firms implement AI inside real workflows: one verified deal memory, cited outputs, human approved writeback with receipts, deployed in the firm's own cloud tenant.
Financial Services Knowledge Graph →
Arvya builds verified, cited knowledge graphs for financial firms: per-deal memory today, connected into firm-wide cross-deal memory, with every fact keeping its source.
Deal Brain →
The Deal Brain is Arvya's verified memory for each live deal (every fact cited to its source) and the foundation of the Company Brain, the firm-wide cross-deal memory in development.
AI Deal Execution Team →
Arvya is deal management rebuilt AI native: one platform, one verified deal memory, every execution workflow a view over it: approval-first, receipted, inside Microsoft 365.
One live mandate