What Sets Authentica Apart: A Framework for Evaluating AI Platforms

Walk the floor at any logistics tech conference and you’ll hear the same pitch from a dozen AI vendors: “custom workflows in days,” “no-code automation,” “AI agents that understand your business.”

The problem isn’t that these claims are false. It’s that they’re table stakes. Every serious AI platform can deploy workflows quickly. Every one offers some form of low-code configuration. The real question is: what happens after the demo?

At Authentica, we’ve validated AI workflows in critical supply chain operations—freight audit, trade compliance, invoice automation. We’ve learned that the technology itself is rarely the failure point. What kills AI projects is the gap between what vendors promise and what they’re willing to guarantee.

Here’s our framework for evaluating AI platforms, and how we’ve built Authentica to close those gaps.

The Table Stakes (Everyone Has These)

Custom workflows in days. Yes, we can deploy custom AI workflows quickly. So can most of our competitors. Rapid deployment is an industry expectation, not a differentiator. If a vendor can’t get you from kickoff to pilot in days, not months, they’re already behind.

No-code/low-code configuration. Same story. The tooling has matured. What matters isn’t whether you can configure workflows without engineering—it’s whether those workflows actually work reliably in production.

Human-in-the-loop oversight. Every enterprise AI platform includes approval gates and human review. The question is whether those controls are architectural (built into the system) or cosmetic (bolted on to satisfy procurement checklists).

These capabilities matter, but they won’t differentiate your evaluation. Here’s what will.

The Real Differentiators

1. Dedicated Engineering Support Options

Most vendors will help you during onboarding. Then they hand you documentation and a support ticket queue.

We offer options to embed dedicated engineering resources in your implementation. Not a customer success manager who escalates to engineering—actual engineers who understand your workflows, your systems, and your edge cases.

Why? Because we’ve learned that the first 90 days determine whether AI becomes infrastructure or shelfware. The problems that kill deployments aren’t technical limitations—they’re configuration gaps, integration edge cases, and workflow nuances that only surface in production. Having engineers embedded in that process catches issues before they become failures.

It’s aligned incentives. We succeed when you succeed, so we invest in making that happen.

2. Flexible Billing

Enterprise software loves annual contracts. They’re great for vendor revenue predictability and terrible for customer risk management.

We offer flexible billing because we believe you shouldn’t have to commit to a year of payments before you know if something works. Pilot projects shouldn’t require procurement theater. Scaling up should be a decision you make when you’re ready, not when your contract renewal comes due.

This flexibility is surprisingly rare. Most AI platforms at scale require annual minimums or multi-year commitments. We understand that the platform shift that is AI requires new approaches.

3. Anti-Hallucination Engineering

Every AI vendor acknowledges that language models can hallucinate. Few have built systematic solutions to prevent it.

We call our approach Harness Engineering. It’s a constraint layer that sits between the AI model and your business operations:

Ontology binding: Agents can only output data that conforms to your business schemas. They can’t invent fields, fabricate values, or reference entities that don’t exist in your systems.
Deterministic guardrails: While the underlying model is probabilistic, the harness enforces deterministic rules around what actions are allowed, what approvals are required, and what outputs are valid.
Context compilation: Instead of stuffing everything into a chat log, we compile focused context windows that show agents exactly what they need—and nothing that could confuse them.
Custom queries, not just RAG: RAG (retrieval-augmented generation) is useful—but why limit a highly intelligent AI to similarity search? We enable custom queries constrained by authorization rules, so agents can ask precise questions about your data while respecting permissions and data classification. The result: structured answers from your systems, not just “find similar documents.”

This isn’t just “better prompting.” It’s architectural. The harness makes hallucination structurally difficult, not just statistically less likely.

4. OrgBench™: Benchmarks That Actually Matter

Standard AI benchmarks measure whether a model can pass generic tests—coding challenges, reading comprehension, abstract reasoning. These metrics are useful for model development but useless for operational deployment.

OrgBench™ evaluates AI performance against your definition of correct. We build benchmark suites anchored to your specific workflows:

Your documents: Real invoices, BOLs, contracts—the actual inputs your workflows process
Your policies: What decisions should be made under your rules, tolerances, and escalation criteria
Your outcomes: Historical results you can verify against, not synthetic test data

This gives you confidence before deployment (“the system performs at 97.3% accuracy on your freight audit criteria”) and continuous assurance after (“performance has drifted 2.1% this month—here’s why”).

When your auditors ask “how do you know this is working?”, you have evidence they’ll accept. When your CFO asks about ROI, you have measured outcomes tied to real business results.

5. Change Management as a Product Feature

Here’s the hardest truth about AI deployment: technology is the easy part. The human and organizational factors are what determine success.

Your employees have legitimate concerns about AI. Will it replace them? Will it make them look incompetent? Will they be blamed when it fails? These concerns, unaddressed, become passive resistance. Passive resistance kills adoption. Dead adoption means no ROI.

We’ve built change management into the platform itself:

Empirical Human-in-the-Loop (HIL) monitoring: We track false positives and false negatives systematically, not just as error logs but as inputs to continuous improvement. When the AI makes mistakes, we know which humans caught them and what they did to fix them.
Workforce re-skilling programs: We don’t just deploy AI—we help your team develop the skills to work alongside it. This includes understanding what the AI does, how to override it effectively, and how to recognize when it’s performing well versus poorly.
Continuous improvement cycles: The HIL data feeds back into the system. Workflows get better over time because human judgment is captured and incorporated, not ignored.

Most AI vendors treat change management as someone else’s problem—yours, or a consulting firm’s. We think that’s a mistake. The vendors who will win in enterprise AI are the ones who take responsibility for the full adoption curve, not just the technology.

What This Means for You

If you’re evaluating AI platforms—whether for supply chain operations, finance automation, or any other domain—here’s the framework:

Table stakes (everyone should have these):

Fast workflow deployment
Low-code configuration
Human oversight capabilities
Basic security and compliance

Differentiated commitments (ask specifically about these):

What engineering support is included, and for how long?
What are the contract terms? How flexible is the billing?
How do they prevent AI errors—architecturally, not just statistically?
How will they measure success against your criteria, not generic benchmarks?
Who handles change management? What’s included?

The vendors who can answer these questions with specifics—not generalities—are the ones building for enterprise reality, not demo theater.

We’re demonstrating these capabilities at Manifest 2026 in Las Vegas. If you’re attending and want to see how these differentiators work in practice, reach out to schedule time.