Stop encoding business logic

Every if/else you hardcode is a Slack message from someone who won't be here tomorrow.
Read from a rule library instead.

You're building features from conditional logic that lives in Slack threads, JIRA tickets, and the heads of domain experts. Each one feels like debt because it is. FieldRules makes that logic queryable, versioned, and attributed — so you build on top of structured judgment instead of reverse-engineering intent from code reviews.

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Integration roadmap

Five ways the rule library will plug in.
Slack and Jira first. The rest as pilots ask for them.

We're building FieldRules from the Slack and Jira surfaces first, because that's where the reasoning lives today. These five integration modes are the roadmap for how the rule library will reach the rest of your stack. Each one is how an engineer will read from — or react to — confirmed rules once it ships.

Shipping first Slack capture + query bot — lets practitioners confirm and query rules in the place they already work. Jira / Linear rule block — renders confirmed rules directly in the ticket that generated them.
01 — Query APIRoadmap
Ask before you build
Will let an engineer check whether a rule already exists before implementing a new feature — versioned, queryable, authored by the person who understands the exception.
02 — GraphQLRoadmap
Introspect your own logic
Planned as the surface that lets features query the rule library at runtime and expose business logic to users instead of hiding it behind opaque decisions.
03 — Agent SDKRoadmap
Govern AI through reasoning
The integration that will let your agents read from the same rule library your humans use. Same reasoning, same governance, different caller.
04 — Feature Flag IntegrationRoadmap
Safe rollout of judgment
Will couple rule publication to feature flags so you can test a rule in production under controlled traffic and roll back if the reasoning needs revision.
05 — Webhooks + EventsRoadmap
React to rule changes
When shipped, outbound events will fire whenever a rule is updated — so your system can recalculate decisions, generate audit logs, or trigger review workflows.
The Thesis

Your codebase is a mirror of unspoken knowledge.

Every feature has an implicit rule set: the edge cases that tripped you up, the customer asks that changed your mind, the assumptions you encoded as guards. Those rules are not in your code. They live in the brains of people who are solving problems faster than documentation can capture them.

When you externalize that logic, something surprising happens. Your code becomes cleaner. Your engineers stop being archaeologists. And when a rule changes, you don't deploy—you edit a field that a domain expert wrote.

The harness compresses knowledge. Code doesn't. A knowledge base is searched; a skill graph is traversed—by domain, by author, by BECAUSE chain. That's the governance layer you want sitting between your product and the model: structured enough to query, attributed enough to audit, alive enough to evolve without a deploy.

From Code to Judgment

What you're replacing.
What you gain in return.

// Trial sites with rapid enrollment and low headcount = high churn if (site.enrollmentSpeed > 7 && site.coordinatorCount < 3 && daysActivated < 14) { // But this logic lives nowhere. It's in a Slack message // from Sarah, the ops director. What happens when // Sarah has different data next month? assignSpecialist(); scheduleCheckIn(); }
What FieldRules gives you instead
⚡
Trial Operations · onboarding playbook
Identified from Slack · Trial site automation
IF
A trial site submits their first enrollment within 14 days AND coordinator headcount is below 3
THEN
Assign a dedicated specialist and schedule a 48-hour check-in before the site goes live
BECAUSE
Sites this size with fast enrollment timelines are statistically high-dropout in weeks 3–5. Early intervention cuts churn by over half — but only if flagged before the standard onboarding flow locks in.
Only humans write this
S
Ops Director
v2.1 · Confirmed

The logic is now queryable. Before you hardcode the next check, ask: does this rule already exist? Is it being updated by product? Have domain experts flagged exceptions?

It's authored by the person who thinks it. Sarah doesn't wait for PRs. She edits the BECAUSE field. You get the signal.

It lives somewhere permanent. When Sarah leaves, the logic doesn't. It's versioned, attributed, and connected to the decisions your product makes.

The Integration Model

One library.
Many callers.

You're not replacing your feature flags. You're not rebuilding your decision engine. You're adding a layer that makes your human judgment accessible to every part of your system—humans, agents, and machines all read from the same rule library.

"The reasoning is yours. The code should say so."

Stop reverse-engineering intent.
Write systems that know their own rules.

We're onboarding a small number of teams manually. One pilot customer at a time. If the timing is right for you, let's talk.

No deck. No demo-ware. We start with a conversation.