How to Create AI Agentic Workflows for Accounting

Most accounting practices don't need another dashboard. They need the month-end close to stop swallowing three days of senior staff time. So if you've been searching how to create AI agentic workflows that actually move invoices, reconcile ledgers, and chase late payers — without hiring two more juniors — this comparison is for you. Two platforms surface constantly in that search: Aiinak and Relevance AI. I've benchmarked both against real bookkeeping work. Here's what the data actually shows.

Both are legitimate. But they solve the problem from opposite ends, and that difference matters more than any feature checklist.

Quick Overview: Aiinak vs Relevance AI#

Relevance AI is, at its core, an agent-building platform. You assemble agents from tools, prompts, and sub-agents, then wire them into a multi-agent "workforce." It's flexible and genuinely powerful. If you have someone technical who enjoys composing systems, you can build almost anything.

Aiinak comes at it from the other side. It ships pre-built autonomous agents for whole functions — Finance, Sales, Support, HR, IT Ops — that perform real actions out of the box: sending emails, updating the CRM, processing invoices, booking meetings. You don't compose an agent from parts. You deploy one, point it at your accounts, and it works.

The honest summary: Relevance AI gives you a workshop. Aiinak gives you a hire. For a busy accounting practice with no spare engineer, that distinction decides everything.

How to Create AI Agentic Workflows for an Accounting Practice#

Before comparing further, it helps to be concrete about what creating an AI agentic workflow actually involves. A real agentic workflow has three parts: a trigger (something happens), a decision (the agent reasons about it), and an action (it changes something in a real system). If a tool only does the first two and hands you a suggestion, that's a copilot — not an agent.

Here's a typical accounts-payable workflow, the kind most firms want first:

• Trigger: a vendor invoice lands in a shared inbox.
• Decision: the agent extracts the amount, matches it to a purchase order, flags duplicates, and checks it against approval thresholds.
• Action: it posts the bill into QuickBooks, routes anything over the threshold to a partner for sign-off, and replies to the vendor confirming receipt.

On Relevance AI, you'd build this. You'd create the extraction tool, define the agent's reasoning steps, connect the integrations, and test the multi-agent handoffs. Expect a few days of setup and tuning for someone who knows the platform. The upside: total control over every step.

On Aiinak, the Finance agent already knows the accounts-payable pattern. You connect QuickBooks and your inbox, set your approval thresholds in plain language, and run it. The platform's pitch is three steps, no code — and for standard finance workflows that's roughly accurate. The trade-off is less granular control over the agent's internal logic.

My practical advice either way: start with one narrow workflow, run it in parallel with your manual process for two weeks, and check the agent's output line by line before you trust it unattended. Agents are good. They are not infallible, and a misposted journal entry costs more to unwind than it saved.

Feature-by-Feature Breakdown#

Pre-built vs custom agents. Aiinak's Finance agent handles invoice processing, payment reminders, expense categorization, and reconciliation prep without configuration beyond your account connections. Relevance AI has templates, but the depth is what you build. For a firm that wants results this quarter, pre-built wins. For a firm with unusual workflows, custom wins.

Real actions. This is the line I'd underline. Aiinak agents execute — they push entries into the ledger, send the chase email, update the client record. Relevance AI agents can also take actions, but you're responsible for connecting and authorizing each tool that makes that possible. Out of the box, Aiinak does more doing; out of the box, Relevance AI does more thinking you've configured.

Integrations. Aiinak lists 25+ native connections — Salesforce, HubSpot, QuickBooks, Slack, Zoom among them. QuickBooks being native matters a lot for accounting practices; it's the difference between an afternoon and a project. Relevance AI integrates broadly too and is strong on connecting custom APIs and tools, which is genuinely useful if your practice runs niche or in-house software.

Built-in apps. Aiinak bundles its own email, CRM, ERP (Tellency), helpdesk, meetings, and a RAG-search drive. If you'd otherwise be stitching together five subscriptions, that consolidation has real value. Relevance AI doesn't try to be your app suite — it's a layer that sits on top of the tools you already run. Neither approach is wrong. It depends on whether you want to replace your stack or augment it.

Ease of deployment. Relevance AI is approachable by no-code standards, but building a reliable multi-agent workflow still rewards a technical mindset. Aiinak's deployment is genuinely lighter for standard cases. Be honest with yourself about who on staff will own this.

AI Capabilities: Where the Real Difference Is#

Both platforms use strong underlying models, so raw "intelligence" isn't the differentiator. Autonomy is.

Relevance AI shines at orchestrated work — multiple specialized agents passing tasks between each other in a defined sequence. If your firm wants, say, a research agent feeding a drafting agent feeding a review agent, that composability is a real strength. It also handles custom tool-building better than most platforms, which developer-leaning teams will appreciate.

Aiinak's strength is standing autonomy. The agent runs continuously against a function and handles the long tail of variation without you scripting each branch. A payment-reminder agent doesn't just send one templated email — it escalates tone over time, stops when payment clears, and logs every touch in the client record. The numbers don't lie here: the value of an agent isn't in one clever task, it's in handling 200 small ones a week that nobody wants to do.

Where should you be skeptical? Both vendors imply more hands-off operation than is wise on day one. Reconciliation that requires judgment — ambiguous transactions, related-party entries, anything touching tax positions — should stay supervised. Industry benchmarks suggest firms typically see 30-50% time savings on routine bookkeeping tasks once an agent is tuned, and that's a strong result. It is not 100%, and any platform implying otherwise is overselling.

Pricing Comparison#

Here's where the two diverge most clearly.

Relevance AI uses a credit-based model with a free tier and paid plans that start low — entry pricing sits in the tens of dollars per month, scaling with usage and team size. For a sole practitioner or a firm that wants to experiment cheaply, that low floor is a genuine advantage. You can test ideas for the price of a lunch.

Aiinak prices per agent: Starter at $499/agent/month for one agent, Business at $2,499/month for up to five agents, and custom Enterprise pricing. There's a 14-day free trial with no card required. That's a higher entry point, plainly.

But compare it to the alternative, not to zero. A part-time bookkeeper in most markets runs $3,000-$5,000 a month loaded. A junior accountant costs more. One Aiinak Finance agent at $499 that absorbs accounts-payable processing and payment chasing is, on that math, roughly 90% cheaper than the equivalent headcount — and it doesn't take holidays or hand in notice mid-busy-season.

So the fair read: Relevance AI is cheaper to start and cheaper to tinker. Aiinak is priced as a labor replacement and tends to be cheaper at the point where the workflow is actually running production work. If your usage on a credit model climbs as agents do real volume, the gap narrows further. Model your steady-state cost, not your trial cost.

Which Is Right for Accounting Practices?#

Choose Relevance AI if you have technical capacity in-house (or a developer-minded partner who enjoys this), you want to build bespoke multi-agent workflows around unusual processes, and you'd rather start with a small monthly spend and grow deliberately. It's a strong fit for firms that see agent-building as a capability they want to own.

Choose Aiinak if you want working autonomous agents for accounts payable, receivables chasing, and client communication without a build project — and you'd like the integrations (QuickBooks especially) and the supporting apps to already be there. For a practice whose scarce resource is senior time, not budget, that speed pays for the higher sticker price within a quarter or two.

One more honest point: support matters when an agent touches your books. Both platforms offer documentation and assistance; Aiinak's Enterprise tier and Relevance AI's higher plans add more direct help. Whichever you pick, get clarity on response times before you trust an agent with live postings.

My recommendation for most small-to-mid accounting practices: if you don't have an engineer, don't try to become one to save a few hundred dollars. The cost of a stalled build is higher than the subscription. Start narrow, prove one workflow, expand from there.

Ready to test this against your own books? Deploy Your First AI Agent on the 14-day free trial, point it at a single accounts-payable workflow, and measure the hours back. Run it beside your manual process for two weeks — then let the numbers make the decision for you.