Operational teams
State what should happen.
Define conditions, exceptions, and desired outcomes in terms that remain connected to the work.
Stop turning business decisions into software projects.
Too often, process changes and automation end up in the engineering backlog. Inferal removes that handoff.
Your decision becomes the automation. Define what should happen and when. Inferal connects it to your data and applies it continuously.
Policy decisions represented as work moving through an engineering kanban board. Hover or focus a card to lift it. Select a card to inspect its decision. Drag a card toward another lane and release it to return the card to its current workflow position. Use Flip to view the decision data server behind the board.
Every operational decision that becomes a software project joins a backlog: specification, implementation, deployment, and maintenance. That translation is the problem. Tap the chevron to see the alternative on the flip side.
Inferal is a live operational system. You install governed operational models close to the data they use. Each model evaluates changing facts, acts continuously, and preserves the rules and evidence behind every outcome.
BacklogBacklog
04DoingIn progress
00DoneDone
00We have mistaken translation work for software engineering.
AI cuts the cost of writing code. The software lifecycle remains.
Language models can shorten the path to a first implementation. But the code still becomes another source of truth to review, deploy, secure, observe, update, and keep aligned with changing business intent.
Translation≈ $0Lastshours Someone decides what should happen, under which conditions, and why it matters to the business.
Capacity≈ $470/ five-person hour1Lastsdays People explain, challenge, and interpret the decision together. Shared context becomes conversation and notes.
Capacity≈ $25–$50/ ticket1Lastsa few hours The agreement is compressed into a unit of work: a request, acceptance criteria, and a place in the backlog.
Capacity≈ $3.8K/ developer-week1Lastsmonths The request becomes precise enough to implement. Ambiguity narrows, but business rationale starts to fall away.
Capacity≈ $16K/ developer-month1Lastsmonths if (condition.matches(context)) { await execute(requiredAction); }Capacity≈ $49K/ developer-quarter1Lastsyears The implementation enters a specific environment with configuration, dependencies, versions, and release constraints.
Cost≈ $59K–$85K/ developer-year2Lastsuntil retired Software keeps accruing obligations after release. Every change must preserve business intent while the technical world beneath it keeps moving.
Make the rule the source of truth.
This is what data-native means: rules, relationships, and constraints remain explicit and connected to the live facts they govern. Inferal evaluates them continuously and sends the resulting actions to the systems and agents that carry them out.
The people who know the work can finally speak for it.
Risk and governance
Keep intent inspectable.
Inspect what governs a decision and trace the facts and rules behind every action.
Engineering
Build the foundations.
Build the algorithms and core capabilities unique to the business. Operational teams own the changing rules around them.
The result is less software between a decision and its execution.
This is an operational model.
The rules, facts, relationships, and policies you just saw form a live, governed representation of how the business works. Ontologies give that model shared meaning across systems and agents.
Every outcome keeps its why.
Because the rule stays connected to the data it evaluated, every outcome retains the exact instruction, evidence, and moment behind it.
Decision receipt
Record 0142
Delivery exception
A late delivery was caught while there was still time to respond.
What changed
A delivery is now expected late.
The vehicle’s latest position moved the expected arrival from 16:42 to 17:18.
What should happen
Late deliveries require intervention.
If the expected arrival is later than 17:00, open an exception and notify the consignee.
What happened
The exception was opened.
The consignee notification was requested automatically.
- Recorded at
- 14:32:08, when arrival changed to 17:18
- Policy in force
- Late-delivery response, effective 12 June
- Evidence at that moment
- Vehicle position at 14:32, arrival 17:18, delivery due 17:00
What people see in Inferal.
“In an era where source code became a commodity, being able to get exactly the data you want in real-time is the moat you need to develop powerful agents. Inferal Relay provides this moat for us.”
“If the data is the source of truth, then business decisions should be made as close to it as possible. Inferal lets you push your business rules right to the data itself.”
See what others sayHide what others say
“We monitor DNA sequences, protein data, antibody assays, and need to act on new relationships continuously. Inferal executes rules close to the database, giving us a fast way to alert AI agents and researchers.”
“Clearly the future for rule-based agentic behavior for all verticals. Everyone else is using agents in a reactive way and Inferal is using them proactively.”
“What stands out about Inferal is how directly their approach translates into faster execution and lower complexity.”
“The database sees everything going in and out, but it usually just stores it. Inferal turns those changes into instant actions without adding a bunch of extra systems.”
From the Inferal journal
View all writing ↗Which business process is waiting on engineering?
Start with one that changes too often, crosses too many systems, or lives in too much code.
The data-native operating system for agents.