Applied AI, built into your business — by a team that ships it.
Arvya is a product company. We are also builders. Beyond our deal-execution platform, we partner with finance and mid-market teams to design, build, and put real AI into production around the workflows that actually run your business — the same way we build our own product.
What we do
The best place to apply AI is a real business with real workflows.
New AI apps chase greenfield problems. We think the opportunity is the opposite: established companies with years of data, real customers, and expensive manual work that could not be automated even three years ago. That is where AI creates the most value — and where it is hardest to get right.
We build custom AI systems for the work that does not fit a SaaS product: the cited answer that has to be correct, the operational bottleneck buried in email and documents, the process a team rebuilds by hand every week. We started in private capital and finance, and the method travels to any team serious about putting AI into production.
What we build
Production systems, not prototypes.
Workflow agents
AI that runs a recurring operational workflow — support and inbound triage, sales follow-up from call notes, reporting, CRM hygiene — drafting the work for a human to approve.
Document & data extraction
Invoices, onboarding packets, diligence documents, contracts, data rooms — extracted into structured, cited records that flow into the systems you already run.
Internal knowledge systems
Cited answers from your own documents, email, and history — retrieval, knowledge graphs, and memory layers your team can actually trust, instead of asking the one person who knows.
CRM & systems automation
Live activity turned into approved writeback for Salesforce, DealCloud, HubSpot, and firm-specific systems — evidence attached to every field, human approval before every write.
Meeting & communication intelligence
Transcripts and call notes turned into structured updates, follow-ups, briefs, and compliance-ready records — mapped to your conventions, not a generic summary.
Evaluation harnesses
The unglamorous thing that makes all of the above reliable: automated test suites that measure how often the system is right, where it fails, and whether the next model is better.
How we work
We treat AI as an engineering problem, not a magic trick.
Evals before models
The first thing we build on any engagement is an end-to-end evaluation harness — an automated test suite that measures how often the system is right, where it fails, and how often it makes something up. That is how we make AI a real engineering problem instead of a demo, and how we can swap the underlying model when a better one ships.
One workflow at a time
We start with a single painful workflow, get it into production, and earn the right to expand. Narrow and shipped beats broad and stuck. Every workflow connects to the real systems of record and closes the loop with the people who own the work.
A human approves every move
Sensitive actions — emails, CRM writes, document and tracker edits, outbound updates — are prepared as drafts and reviewed by a person before anything happens. Every action leaves an audit trail. Trust is the product, not an afterthought.
Reviewed across models
We build with the frontier and check our own work against it. Cross-model review is standard practice, not a nice-to-have — so what we ship holds up when it meets your data, your edge cases, and your compliance team.
How we work together
Engagements shaped around the outcome.
We are a services team that avoids the linear consulting model. We would rather tie our work to the value it creates. Where that fits, we can structure the relationship accordingly.
Fixed-scope build
A defined problem, a defined outcome, a defined price. We scope one workflow, ship it into production inside your environment, and hand you something your team can run and extend.
Embedded build team
Our engineers work shoulder-to-shoulder with yours on the AI roadmap — designing evals, building workflows, and transferring the method so your team keeps compounding after we step back.
Outcome-aligned partnerships
For the right partners — including private equity firms and their portfolio companies — we structure engagements around the value created, not just hours billed. If we change the economics of the business, we want to share in the upside.
Product + build
The product and the build reinforce each other.
Our platform — Deal Brain memory, knowledge graphs, retrieval, approval queues, and workflow agents — is what lets us move fast on a build. And every engagement sharpens the platform against real work.
Some teams start with the product. Some start with a project and grow into it. Either way, you get a system your team can trust, run, and own — not a slide deck.
Explore the Arvya product →Frequently asked questions
How long does the first workflow take?
Typically 30 to 45 days from kickoff to a production workflow with real users: one week mapping how the work actually runs, two weeks connecting systems and building the human-reviewed AI step with an eval harness, one week training the team and measuring against the baseline.
How is this different from AI consulting?
Consulting sells analysis; we ship systems. Every engagement is scoped to a deployed workflow with a measured outcome, built into the tools you already run. If the assessment finds no workflow worth deploying, we tell you not to continue.
Do you only work with finance firms?
We started in private capital and finance, and the method travels. PE-backed portfolio companies and mid-market businesses in software, services, and healthcare administration are common engagements — anywhere relationship-heavy, document-heavy, approval-sensitive work runs the business.
Who owns what you build?
You do. Builds run inside your environment, connect to your systems, and are documented and handed over so your team can run and extend them. Embedded engagements explicitly transfer the method as we go.
How do you keep AI safe in regulated work?
Sensitive actions — emails, CRM writes, document edits, anything client-facing — are prepared as drafts and approved by a person, with an audit trail on every action. No customer data is used to train models, and deployments can run inside your own cloud tenant.
Have a workflow that should be AI, and isn’t yet?
Bring us the messy, high-value work. We will tell you honestly whether AI is the right tool — and if it is, we will build it with you.
Start a conversation