Technology, risk, and operational teams each look for something different here. Jump to your role below.
Your policy is applied here, before the model.
Lectotype gives organizations a consistent way to define the controls they choose, see how those controls are applied at runtime, and confirm what happens next.
Define the policies and rules the organization chooses.
A support agent authorized to waive fees up to $500 can't approve a $4,000 request without review, because your organization set that boundary before the request ever reached a model.
See which rules were applied, and why.
Every decision Lectotype makes is traceable to a rule. When that $4,000 waiver is flagged, you see exactly which policy triggered the hold, not a black-box refusal.
Recognize whether a request moved forward, was routed for review, or stopped before reaching the model.
The fee waiver doesn't just happen or not happen. It's approved, escalated to a manager, or blocked, and that outcome is logged and confirmed, every time.
Every AI request passes through the Lectotype control layer before it reaches the model. Controls are applied before sensitive data is shared, an unauthorized action moves forward, or an issue becomes a downstream problem.
Model independence: Lectotype is model-agnostic, so governance stays separate from model choice and remains consistent as your AI environment changes.
Tap a step to see it applied ↓Click a step to see it applied →
Illustrative request
“Review this customer's account history and issue a $25,000 refund.”
Lectotype remains the ultimate reference standard. What counts as evidence changes by role: an architecture diagram, an audit trail, or a drop in exceptions. View your role below to see how it works for you.
Architecture, integration, and model flexibility
Keep governance separate from application code, with consistent controls across models and workflows.
Reduce reliance on a single model provider with a unified control layer.
Maintain governance standards as your AI stack evolves.
Follow an implementation path that fits existing architecture.
Policy enforcement, data controls, and audit visibility
Prove policy was applied consistently, with an audit trail that holds up.
See every decision logged against the policy that produced it.
Demonstrate consistent enforcement across every model and workflow.
Produce an audit trail without building one yourself.
Reliable workflows, fewer exceptions, and predictable outcomes
Fewer exceptions reaching your team, and predictable outcomes when they do.
Routine requests move forward without manual review.
Exceptions are routed to the right person, not everyone.
Outcomes stay consistent as volume grows.
Two organizations running Lectotype in production, and the outcomes they measured after deployment.
A regulated advisory environment where Lectotype was used to support AI-enabled advisor workflows.
Expanded the engagement after the initial deployment.
Applied Lectotype in a live water-intelligence reporting environment.
Moved from implementation into live production with increased report throughput.
Estimate your savings using what you already know: your usage, and what you pay.
Enter what you spend each month on AI for coding and for general use. The results update as you type.
Coding assistants and agents, including seats and API usage.
Chat, writing, analysis and other general AI tools and APIs.
Savings rates are fixed by workload type. See Calculation assumptions in the results.
$4,800 from coding at 80% and $4,500 from general AI at 50%.
Coding savings rate: 80% · General AI savings rate: 50%.
Start with a chosen reference standard: one point every comparison gets measured against. In taxonomy, that reference is called a lectotype, the specimen selected once and trusted from then on. STANDARD is that same idea applied to enterprise AI: eight areas, chosen once, that become the evidence your organization can point to.
The policy is yours, independent of which model or vendor sits behind it.
Five questions covering deployment, performance, data handling, and policy control. Open the one closest to what you need to evaluate.
Bring us the question you need answered, and we’ll respond directly.
Bring the AI workflow you want to evaluate and the rules that should govern it. We’ll map where Lectotype fits and what should happen before the model acts.