Agentic operational intelligence
Structured plus unstructured. One answer you can trace.
Your systems hold the numbers. Your documents hold the reasons.
Most of what explains your operation never reaches your ERP. It sits in emails, scanned reports, claim files and technician notes, so people read it by hand and the root cause stays buried for weeks. EXTRCT reads it all, joins it with your operational data and gives your teams decisions backed by the evidence.
PDFs and scans
Emails and notes
Photos and handwriting
Every answer cited
Live in about 6 weeks
The short answer
EXTRCT is agentic operational intelligence. Teams of AI agents read your unstructured documents, reason over them alongside your ERP and operational data, and deliver root causes and decisions linked to the source.
Your experts stop reading documents line by line and start acting on what they say. Every finding shows where it came from, so quality, claims, compliance and finance teams can check it before they rely on it.
See it in action
Watch EXTRCT read real documents, connect them to operational data and turn them into decisions with the evidence attached.
The blind spot
Traditional analytics only works on the structured part of your data. The rest, the part that explains why things go wrong, needs people to read it.
Gap 1
The why lives in documents
Failure reasons, claim details and supplier changes sit in reports, emails and notes no system reads.
EXTRCT fix. Agents read every document type
Gap 2
Root causes take weeks
Experts pull files, compare cases and build spreadsheets, so the answer arrives after the damage is done.
EXTRCT fix. Agents reason across thousands of cases
Gap 3
AI answers nobody can check
Generic AI summarises confidently, but nobody can see where the answer came from, so nobody acts on it.
EXTRCT fix. Every answer linked to its source
How EXTRCT works
Three steps, run by teams of agents that check each other’s work.
Step 1
Read
Document intelligence
Pulls entities, figures, tables and context from any document, without building templates.
PDFs, scans, emails and photos
Handwritten and technician notes
Mixed languages, including Arabic
Repair estimates and claim files
Step 2
Reason
Multi agent collaboration
Agents link document signals with ERP, MES and KPI data and trace every conclusion to its source.
Pattern detection agents
Root cause agents
Validator agents that cross check
A person only where needed
Step 3
Resolve
Evidence linked decisions
Turns findings into insights, early warnings and decision workflows your teams can audit.
Root cause reports with citations
Early warnings on new patterns
Approval flows with confidence scores
Questions answered in plain language
The EXTRCT ecosystem
Unstructured and structured sources flow into the same engine. Agent teams read, reason and validate, then hand decisions to your people and your systems. Cortex sits underneath.
Unstructured sources
PDFs and scans
Emails
Technician notes
WhatsApp threads
Photos
Repair estimates
Handwritten notes
Structured sources
ERP
MES
KPIs and BI
Claims systems
CRM
Data platforms
▼
EXTRCT engine
One source of truth · A knowledge graph of entities, cases and evidence · Every fact traced to its document
▼
Read
Document intelligence agents. Entity, table and figure extraction
Detect
Pattern detection agents. Recurring failures, spec changes, anomalies
Explain
Root cause agents tracing effects back to their causes
Validate
Validator agents checking VINs, dates, part numbers and rules
Answer
Synthesis and conversational agents for plain language questions
Evidence linked governance
Citations on every output · Confidence scores · Low confidence items sent to experts · Full audit trail
▲ ▼ Decisions written back to your systems ▲ ▼
Where the decisions land
Root cause reports
Early warning alerts
Claims approved or routed
ERP and quality records updated
Audit evidence packs
Leadership questions answered
Your experts
Review the exceptions and approve the decisions that matter, with the evidence in front of them
Runs on Cortex
EU cloud · Hybrid · On premise · Air gapped · 200+ models or your own
Reading and answering
Detection
Root cause
Validation
Worked example
Step 1
Intake
A claim arrives by email with 5 to 15 attachments, photos and a repair estimate.
Step 2
Read
Agents extract the VIN, plate, parts, labour and dates from every document.
Step 3
Check
Validator agents cross check against the policy and benchmark parts and labour costs.
Step 4
Decide
Clean claims move ahead within policy. Anything unusual goes to an adjuster with the evidence.
Step 5
Learn
Patterns across claims surface, from repeat repairers to rising part costs.
Built for document heavy work
Claims recovery and adjudication
Email intake, documents read across every claim, entity cross checks, parts and labour benchmarking and escalation to adjusters.
Insurance · Claims recovery
Automotive insurance and warranty
Lead enrichment, VIN and plate recognition, inspection scheduling, premium calculation and personalised warranty offers.
Insurers · Dealers · OEMs
Manufacturing and industrial
Failure patterns found across maintenance reports, MES exports and technician notes, with root causes in minutes.
Quality · Maintenance · Operations
Banking and financial compliance
KYC document parsing, AML narrative reasoning, regulatory change tracking and audit evidence linking.
Compliance · KYC · Audit
Healthcare and pharma
Patient note synthesis, trial documentation, adverse event extraction and quality audit trails, with on premise options.
Clinical · Regulatory · Quality
Energy, utilities and heavy industry
Outage post mortems, asset health from inspection notes, regulatory filings and safety incident root causes.
Operations · HSE · Regulatory
Already at work
80%
Faster claims processing for an automotive insurer in the GCC.
Automotive insurance. GCC
4 to 6x
More extended warranty conversions for the same insurer, from enriched leads and personalised offers.
Automotive warranty. GCC
The difference
OCR and IDP tools
Generic AI chat
Build in house
EXTRCT
Reads messy documents
With templates
Yes
If you build it
Yes, no templates
Joins with ERP and KPIs
No
No
If you build it
Yes, in one knowledge graph
Finds root causes
No
Guesses
If you build it
Agents that cross check
Shows its evidence
Partly
Rarely
If you build it
Every output cited
Time to value
Fast for extraction
Fast, unreliable
Many months
About six weeks
Security and data
Claims files, patient notes and compliance records need more than good extraction. They need control over where data lives and who sees it.
Data residency
EU cloud by default
Isolated per client
Never used to train shared models
Deployment
Hybrid with data on premise
Full on premise
Air gapped with self hosted models
Audit
Citations on every output
Every decision logged
Purge with proof of deletion
Human control
Confidence based approvals
Experts review the exceptions
Feedback improves calibration
SOC 2
ISO 27001
GDPR
HIPAA ready
Air gapped deployment
How we’d start
Step 1
Discovery session
We map the documents, volumes and decisions in the process that slows you down most.
Step 2
Proof of value
A four to six week sprint on your own documents, with the impact target agreed up front.
Step 3
Scale across the business
More document types, more connectors and more business units on the same platform.
Validate
Proof of value
One workflow or document type
Up to 3 source connectors
Read and detect agents set up
Agentics delivery team included
Scale
Production deployment
Read, Reason and Resolve in full
Up to 10 connectors, one business unit
Agent and workflow builders
Six month impact commitment
Enterprise
Full platform licence
Unlimited connectors and agents
Multiple business units
Air gapped, on premise or hybrid
Twelve month impact commitment
Every pilot runs under our Validation First Framework. It’s tested on your own documents before you commit a larger budget, and if the numbers don’t hold, we tell you. Pricing follows workflow scope, never document volume.
EXTRCT, answered
What is EXTRCT?
EXTRCT is agentic operational intelligence from The Agentics Co. Its agents read unstructured documents such as PDFs, scans, emails and technician notes, join them with ERP and operational data, and give answers and decisions that link back to the source evidence.
What kinds of documents can it read?
PDFs, scanned reports, emails, technician notes, chat threads, photos, repair estimates, handwritten notes and mixed language documents, including European languages, English and Arabic. No templates are needed for each document type.
How does EXTRCT avoid making things up?
Every output links to the document or record it came from. Validator agents cross check results against rules like VINs, dates and part numbers. Low confidence items go to a person, and every decision carries a confidence score and citations.
Can we start small?
Yes. Most clients start with one workflow or document type. A first production agent typically goes live about six weeks after discovery, on the same platform that later scales across business units.
Where does our data live?
In EU cloud infrastructure, isolated per client, or entirely inside your perimeter with hybrid, on premise or air gapped deployment. Your data is never used to train shared models, and documents can be purged with proof of deletion.
Can we use our own language models?
Yes. EXTRCT runs on Cortex, whose AI gateway supports 200+ models including self hosted open models. Different agents can use different models.
How is EXTRCT priced?
By workflow scope, not by document volume. Every engagement starts with a discovery session and a fixed scope proposal with an agreed impact commitment.
Why not build it ourselves?
You can, but reaching production quality extraction, multi agent reasoning, validation and audit trails usually takes many months of specialist work. EXTRCT starts from all of that, so your team focuses on the domain problem.
