AI for Messy Data: Making PDFs, Emails, and Logs First-Class Citizens
- JC
- Aug 13
- 3 min read
The Data That AI Can’t See
Imagine this:
A supplier misses a shipment deadline, so you check your AI assistant for the penalty clause and it confidently answers, but it’s wrong.
The correct clause? Buried on page 47 of a PDF sitting in someone’s email, and the AI agent never saw it.
This is the hidden reality in almost every enterprise: the most important details don’t live in your clean, well-labeled database tables. They live in messy data -PDFs, scanned contracts, Slack threads, maintenance logs, meeting transcripts, Jira tickets. And right now, your AI agent can’t use them properly.
The Problem with 'Messy' Data
Unstructured content makes up 80–90% of enterprise knowledge. But traditional data pipelines, BI tools, and even most GenAI retrieval setups are built for neat, structured records.
If you want to use messy data today, you’d likely need to:
Spend weeks manually tagging and cleaning it.
Restructure it into a schema that may be obsolete before it’s finished.
Accept that you’ll lose context -like which customer, product, or order the document relates to.
And when you skip those steps? Your AI agents either hallucinate, or worse, give partial answers that sound right but are missing critical facts.
Why AI Struggles Without Context
Most enterprise AI is blind to the relationships between unstructured and structured data.
For example:
The penalty clause in a PDF contract isn’t linked to the supplier ID in your ERP.
A failure event in a log file isn’t connected to the product batch in your manufacturing system.
A policy rule in a SharePoint document isn’t linked to the system it applies to in your asset register.
Without those links, AI can’t reason across your full knowledge. It’s like asking it to navigate a city with half the streets missing from the map.
Conode's Approach: No More Second-Class Data
At Conode, we make messy data a first-class citizen in your enterprise knowledge.
Here’s how:
Load As-Is: Bring in PDFs, emails, logs, and tickets instantly -no need for manual schema design or tagging.
Automatic Linking: Conode’s knowledge graph extracts entities, events, and relationships, and connects them to your structured data.
Explainable Context: Every fact is traceable to its source, right down to the page, timestamp, or message where it appeared.
Ready for Action: AI agents can now query, reason, and take actions grounded in all your data, not just the neat parts.
What This Looks Like in Action
Before Conode:
Supply chain team gets a disruption alert
They check ERP for orders, then search email for contract terms, then Slack for updates
Half a day later, they have an answer
With Conode:
AI agent instantly answers:
“Supplier A’s delay affects Orders 45, 62, and 78, total value £3.2M. Penalty clause waived due to force majeure on page 47 of Contract-2023.pdf. Recommend reroute via Port 09.”
Source links show the original ERP order record, the PDF clause, and the Slack message confirming the disruption.
Real-World Use Cases
Automotive Manufacturing: Link warranty PDFs, maintenance logs, and part master data to answer:
“Which vehicles have part X and have seen repeated warranty claims in the last 6 months?”
Compliance: Search across policy documents and audit logs:
“Which controls apply to customer data stored in AWS?”
Supply Chain:
“If Port 09 closes, which customers and SKUs are affected, and what’s the total order value?”
The Payoff
By making messy data explorable in minutes:
You remove manual bottlenecks in finding and verifying information
You give AI agents the full context they need to act correctly
You ensure every answer is explainable and defensible
The result? Your enterprise AI stops guessing, and starts acting with certainty.
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