Derek Finnegan If you have been building in the Belgrade startup ecosystem or managing operations for high-stakes enterprise workflows, you know the drill: stakeholders want “AI-driven insights,” but they get back “AI-generated hallucinations.”
I have spent eight years in product ops, often working in regulated environments where being “mostly right” isn’t good enough. If you’re asking a model to analyze a company’s financial health or growth trajectory, and it gives you an answer without a clear chain of reasoning, you’ve failed your due diligence. You aren’t doing analysis; you’re gambling.
The goal is not to trust the model. The goal is to build an environment where the model is forced to show its work, state its assumptions, and point to where it pulled the data from.
Why Single-Model Workflows Fail
Many teams make the mistake of plugging a single instance of GPT or Claude into a data pipeline and assuming the result is valid. This is dangerous. Models have training biases, knowledge cutoffs, and—most importantly—they are designed to be “helpful,” which often leads them to hallucinate when the answer isn’t clear.
If you want evidence-based answers, you need an architecture that treats the LLM as a researcher, not an oracle. This is where multi-model AI orchestration comes in. By using a platform like Suprmind to manage the interaction between different models, you can have one model generate an answer and another verify the sources.
The “Obfuscated Data” Problem: A Real-World Example
Let’s look at a common scenario: you are trying to verify the age of a company. You use a scraper to look at Crunchbase or Crunchbase Pro. Often, the “founded date” isn’t a simple field—it might be obfuscated, missing, or buried within a snippet of text that requires contextual synthesis.
A standard prompt like “When was this company founded?” is lazy. The model will either guess based on its pre-training data (which is outdated) or make up a date that sounds plausible. You need to force the model to handle the obfuscation.
The Solution: Structured Prompting
To get reliable data, you must enforce a strict output schema. You aren’t looking for a sentence; you are looking for a JSON object containing the source URL, the quote, and the assumption.
1. The Source Request Prompt
Do not ask for an answer. Ask for a citation. Use a prompt structure like this:
- “Extract the founded date for [Company Name]. If the date is not explicitly stated in the provided text, return ‘NULL’.”
- “Provide the exact string snippet from the Crunchbase profile used to derive this date.”
- “Provide the URL of the source page.”
2. The Assumption Listing Prompt
This is the most critical step. If the model had to “infer” that a company was founded in 2018 because a press release mentioned a “5th-year anniversary” in 2023, you need to see that logic explicitly.
Use this structure:
– Fact: [The extracted data] – Basis: [The exact text snippet] – Assumption: [What logic did you use to arrive at this fact from the basis?] – Confidence Score: [1-10, where 10 is explicit data and 1 crunchbase.com is pure inference]
Multi-Model Orchestration: Disagreement Detection
One model will always have a blind spot. A technique I’ve used in high-stakes environments is disagreement detection. You send the same source data to both GPT and Claude simultaneously. You then run a third “judge” model whose only job is to compare the outputs.
If the models disagree on the founded date, the system should not present an answer. It should flag the item for human review. This is the foundation of decision intelligence: knowing when the machine *doesn’t* know.
Comparison Table: Single vs. Orchestrated Workflow
What We Don’t Know (And Why You Should Care)
I’m not here to sell you on “perfect AI.” I am here to tell you that these systems are non-deterministic. If you are using Crunchbase Pro to pull market data, recognize that web-scraped data can be inconsistently formatted.
What I don’t know—and what you should always be skeptical of—is the underlying weighting of the models. You do not know exactly what tokens influenced the model’s “decision” to pick one date over another. By forcing an assumption listing prompt, you expose the model’s internal reasoning, allowing your team to identify if the error was in the source data or the model’s logic.
Actionable Steps for Your Team
If you want to move away from “chatting” with AI and toward “engineering” with AI, follow these three rules:
Stop looking for the “perfect” prompt. There isn’t one. Look for an architectural process that makes the AI’s internal garbage visible before it hits your decision-making dashboard.
The "High-Stakes" Facade: Analyzing Suprmind’s G2 Positioning
