Automate the coordination work, keep control of every step.
Most back-office work isn't hard, just constant: approvals, reconciliation, vendor and partner follow-ups, status checks, data moving between systems. We redesign these workflows around AI agents, with deterministic checks in front of every action and a person for every exception. The model is used only where a cheaper method won't do.
Secular Labs builds AI workflow automation for high-volume, rule-bound business processes. Each workflow runs as a controlled sequence: intake, reasoning, retrieval, model routing, validation and delivery. Every action is checked before it commits, and anything that fails goes to a person.
Workflows that fit well
| Area | Typical workflows |
|---|---|
| Finance operations | Invoice handling, reconciliation, collections follow-up, approvals |
| Vendor and partner coordination | Onboarding, document checks, status chasing, escalations |
| Customer operations | Request intake and routing, order and dispute handling, returns |
| Reporting and data | Pulling data from several systems, plain-English answers, recurring reports |
How we build it
| Stage | What it does |
|---|---|
| 1. Intake | Receive, authenticate, validate and classify the request |
| 2. Reasoning | Decide what the request needs and orchestrate the steps |
| 3. Retrieval | Fetch only the relevant context, with its source attached |
| 4. Model routing | Use the cheapest model that's good enough, with automatic fallback |
| 5. Validation | Check structure, consistency and safety before anything is released |
| 6. Delivery | Return the result through the original channel, logged |
The tools behind each stage are listed on Solutions.
Why this doesn't just swap a labor bill for a token bill
- Deterministic logic first, the model last. Rules decide wherever rules can.
- Routing to the cheapest sufficient model. Most requests don't need a frontier model.
- Retrieval instead of bigger prompts, so there are fewer tokens per call.
- Validation catches bad output before a silent retry doubles the cost.
- Cost tracked on every request, not discovered on the invoice.
A workflow, before and after
| What happens | |
|---|---|
| Before | Operations teams manually reconcile what was ordered, delivered and billed, and discrepancies surface days later. |
| After | Every state change passes a check before it commits, so discrepancies are stopped at the point they occur, not found in reconciliation. |
Evidence, labeled honestly
The same architecture runs in production in our AdTech work, for example real-time invalid-traffic scoring on the live bidding path (see How we work). Applying it to your workflow starts with a measured baseline. Our performance figures are modeled, not yet measured in live client deployments.
Frequently asked
How is this different from RPA?
RPA replays fixed clicks and breaks when a screen or format changes. Agent workflows understand the request, but every action still passes deterministic checks, and failures go to a person instead of being guessed through.
What happens when the AI is unsure?
The step fails its check and is handed to a named person. It's never guessed through.
Can it run on our own infrastructure?
Yes. The stack is open-source and self-hostable, which also helps with data residency.
How long does a first workflow take?
The Audit takes 10–15 working days after data access. A Pilot puts one workflow live, measured against that baseline.
What does it cost?
The Audit starts from $1,500; a Pilot on one workflow starts from $6,000. See engagements and pricing.
Start with one workflow.
Take the free diagnostic (8–12 minutes), or book a 30-minute call. The Operational Leverage Audit starts from $1,500; a Pilot on one workflow starts from $6,000. Each step can be the last one.
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