A guardrail that runs after the action is a log entry. We put the check in front: input validation, output constraints, and an action policy that evaluates what an agent is about to do against rules you wrote, before the call is made. Where the stakes justify it, a human approves. Every decision, allowed or refused, lands in the record.
01 / GUARDRAILS
For systems where a bad action is expensive. Input and output validation, action policies that stop a call before it happens, and human checkpoints where stakes justify one.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
Most guardrail work inspects output text. The expensive failures are actions, so the policy evaluates the intended tool call and can refuse it before anything happens.
On Brandiligence a validator enforces the approved-source rule; the model cannot cite outside the index even if instructed to. Prompt-level rules degrade under pressure, and validators do not.
An audit trail of what was allowed is half a record. The refused actions are the ones that prove the layer is doing anything.
Week 1
What must never happen, stated as rules rather than as prompt instructions. A policy that lives in a prompt is a suggestion.
Week 1-2
Input schemas and output constraints enforced in code, so a malformed result fails before it reaches a caller.
Week 2-3
The check runs before the tool call. A guardrail that inspects text after the transfer went through is a log entry.
Week 3-5
Approval routing for the narrow set of actions that warrant it, with enough context in the request for the approver to decide in seconds.
Legal & IPIn production
A fine-tuned LLM with retrieval over a curated trademark and IP library, built so the model can only cite firm-approved authorities and every draft holds a fixed legal structure an attorney can review and sign.
Cloud infrastructureIn production
A DevOps agent that turns plain-English infrastructure requests into validated cloud templates and safely applies them to live AWS accounts, planning against real limits and quotas, validating in a loop, and tagging every resource it creates.
Executive operationsIn production
A 24/7 AI chief-of-staff for founders and leadership teams. An agent that plans, acts, and self-corrects across email, calendar, docs, CRM, and ERP through a secure tool layer, runs recurring workflows end to end, and never exposes a credential.
Production-proven
Built by engineers who've already shipped this in production.
Tuned badly, yes, which is why refusal rates are measured alongside safety. A layer that blocks a third of legitimate work has failed differently but just as completely.
No. Moderation judges text; this judges actions. The question is not whether a sentence is acceptable but whether a transfer, a deletion or a send should proceed.
Schema checks are microseconds; policy evaluation is single-digit milliseconds. Human approval is as slow as your approver, which is why it is scoped narrowly.
That is the point of writing the policy as rules rather than burying it in prompts. One document serves both the reviewer and the runtime.
Multi-step agents for workflows where a wrong action is expensive to undo.
View serviceEvals, routing, and audit trails for teams whose demo cannot survive real traffic.
View serviceA data boundary in front of the model, for teams who cannot send raw records to a provider.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.