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LLM -The Engine, Not the Driver

LLM -The Engine, Not the Driver
LLM gives us power to automate what we couldn't before. But its only a part of the solution.

Covenance | Jan 4th, 2026 | AI & Automation

This post explains the vision of Covenance.ai for how to use existing AI to create products that bring our customers savings and speed not possible in the era before AI. We can do it today.

Business impact of AI: whats missing?

The promise of AI in business is tantalizing: it could solve complex tasks quickly and at low cost. However, the reality as of early 2026 is often one of early hopes turning into frustration. :

Simply pointing a powerful Large Language Model (LLM) at a business problem—even when augmented with a vast knowledge base via RAG—often results in systems that are brittle, unreliable, and lack the explainability that professional environments demand.

The issue isn't the power of LLMs, but how people are trying to use them. We believe the fastest path to value isn't in waiting for a hypothetical AGI to autonomously run our processes. It’s in building Modern Expert Systems: structured, repeatable business procedures driven by an LLM engine. The procedures here come from existing best practices and experts. We bring the speed, predictability and cost reduction of a computer into these procedures.

The 80/20 Reality

This approach acknowledges a crucial reality: most high-value business processes are 80% procedure and 20% exception.

  • The 80%: We automate with precision and reliability.
  • The 20%: We intelligently escalate the complex, novel, or high-risk cases to the human experts who have always handled them.

The Ghost of Expert Systems Past

The idea of automating tasks by mimicking human experts isn't new. In the 1980s, the first wave of "expert systems" tried to revolutionize industries by encoding the knowledge of top performers into rigid if-then-else logic. The dream was to turn a senior expert’s expertise into a software.

The effort failed. These systems were too brittle. They could not handle the slightest deviation from their programmed path because they lacked the ability to understand nuance, context, or ambiguity. The world was too messy for hard-coded rules.

The LLM: The Missing Piece

Today, LLMs are the missing piece that makes the expert system paradigm viable. An LLM can do what a 1980s algorithm never could: operate on concepts, not just strings and numbers. This allows the right level of abstraction to describe a business procedure.

Case Study: The KYC/AML Workflow


Let's make this concrete with a KYC/AML (Know Your Customer/Anti-Money Laundering) process. A seasoned compliance officer describes the daily reality:

"I’ve spent the last decade running compliance desks for fintechs. The real work isn't just checking boxes—it's managing the exceptions. Most applicants are exactly who they say they are, but then there are 'hairy' cases. I have to be fast handling big volume of data, and then be ready switch the gear completely when something is off."

The Expert’s Manual Workflow

  • Document Intake: Comparing passports and selfies. 80% are crystal-clear matches.
  • Sanctions & PEP Screen: Running names against watch-lists. Most hits are "fuzzy" false positives (e.g., the wrong "John Smith").
  • Address & Device Reputation: Checking Geo-IP and billing addresses.
  • Risk-Score & Approval: Manually inputting data into Excel or a risk tool.
  • Manual Escalation: Handling the "hairy 20%"—poorly cropped IDs, Venezuelan watch-list matches, or complex corporate trusts.

The low hanging fruit here is to take the screening and routine cases off the human expert. The expert usually can give us a procedure / rules to do just that. Instead of trying to replace the expert entirely, we use an LLM to reliably execute the high-volume, procedural steps.

Where the LLM-Powered System Comes In

By automating the trivial cases and escalating only the complicated ones, we save close to 50% of the total time immediately. And for the other half that is not automated yet - this approach means we can collect data on those complex escalations, allowing us to handle them in time as our dataset grows.

Why This Approach Wins

  1. Immediate Trust & Transparency: The system leverages your existing, time-tested procedures. You can pinpoint exactly which step made which decision.
  2. Leverages Existing Expertise: We start by capturing the knowledge of your senior employees, achieving a high baseline of quality.
  3. Controlled Risk: The cost of an error is capped. The AI escalates rather than "hallucinating" a high-stakes decision.
  4. The Data Flywheel: Those escalated 20% of cases become a curated dataset of your most valuable problems to solve next.

Key Research & References

Automation, honestly. That's Covenance.

This pragmatic, step-by-step approach is the foundation of Covenance. We are not selling a far-off dream of AGI; we are delivering real automation for critical business processes today.

In the legal and compliance domains, where precision is paramount, this is the only way forward. We build systems that are reliable, transparent, and leverage the deep expertise of our clients.

We don't have to wait for AGI to create immense business value. We can do it now.

https://dpia.covenance.ai

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