Kenop Intelligence — built in India, running globally

Your problem.
Global compute.
Grounded in reason.

Describe your problem. Upload your data. In fifteen minutes: root causes, likelihood scores, corrective actions — stress-tested by six AI models across eleven reasoning stages. Then the thread opens for your whole team.

Start your investigation →
11
Reasoning stages
6
AI providers
15m
To a structured report
24h
Raw data auto-delete
How Kenop works — at a glance

Your inputs, the pipeline, the report
— and the thread that follows.

Step 1 · InputsFile 1process dataFile 2supportingFile 3contextProblemdefinitionExpectedoutcomesStep 2 · The 12-stage boxKenop pipeline01Trend scan02Decomposition03First principles04Causal chain05Protocol check06Deep research07Industry pulse08Synthesis09Counter-argument10Report draft11QA review12StructureReportroot causes · actions · benchmarksStep 3 · ThreadNeutralisation loss — threadReport pinned — 4.2% loss · 3 causes · 7 actionsKKenopARArjun R.KPSPriya S.

Once the report is out, Kenop enters your team thread as a participant. Local knowledge meets global compute — in conversation.

What Kenop does

Four things working
together for the first time.

Each one matters. Together they are a different category of tool.

01

Multi-model reasoning from first principles

Claude reasons from chemistry, physics, and first principles. Perplexity finds live global data. Grok reads industry signals. GPT-4o synthesises. Before the report is written, a dedicated counter-argument stage challenges every conclusion. What you receive has already been stress-tested.

First principles + counter-argument
02

Reasoning output available as team context

Every report Kenop produces is indexed and embedded as structured knowledge — not stored as raw files. Your team opens the thread and reasons on top of what Kenop already worked out. The starting point is never zero again. Knowledge compounds with every reason you run.

Persistent reasoning context
03

Kenop as AI representative in your team thread

After the report, a thread opens. Your whole team joins — and Kenop is already in it, with your full reasoned context loaded. Ask Kenop on Claude. Get a second opinion on Grok. Pull live data on Perplexity. Kenop answers every question as a team member who read everything.

Kenop in your team, on any model
04

Continuity of threads across problems and time

A problem does not end when the report does. Threads stay open. Return days later. Add new data. Change the model. The thread remembers. New team members join and read the full reasoning history. Problems become institutional knowledge, not personal knowledge that disappears.

Thread continuity
The reasoning pipeline

Eleven stages.
Each one, the right model.

A council of models, not a single voice.

Each stage is assigned to the model best suited for that type of thinking. First principles goes to Claude. Live research goes to Perplexity. Industry signals go to Grok. The counter-argument goes to a fresh model that has not seen the prior reasoning — so it genuinely challenges it.

The output is not a summary of what one model thought. It is a synthesis of what a workforce of models reasoned together.

ClaudeGPT-4oPerplexity SonarGrokGemini FlashMistral Large
01Trend scan
02Decomposition
03First principles
04Causal chain analysis
05Protocol check
06Deep research
07Industry pulse
08Synthesis
09Counter-argument
10Report draft
11QA review
Also built in

Six more things
that matter.

S

Structured output, not prose

Root causes with likelihood scores. Corrective actions with timeframes. Parameters benchmarked against standards. A verdict. Every reason produces something you can act on immediately — not an essay to interpret.

C

Counter-argument by design

Stage 9 of 11 is a dedicated counter-analysis. A fresh model challenges every conclusion the prior eight stages reached. You receive the answer and the strongest case against it — already stress-tested before you read it.

D

Domain grounding

Kenop knows AOCS, ISO 660, CODEX, API standards, IFRS, IBC. The reasoning cites applicable standards and flags deviations. Not generic intelligence. Domain intelligence built for the industries that need it.

T

Team intelligence

The thread is shared. Your team reads the full reasoning, continues the conversation, and drops new files — all against the same reasoned context. Problems become institutional knowledge, not personal knowledge.

A

Audit trail of reasoning

Every stage is logged — which model, which conclusion, which confidence. You can trace any finding back to its origin. In regulated industries, you cannot just say the AI said so. Kenop shows exactly how it got there.

P

Privacy by architecture

Raw files deleted within 24 hours. What persists is structured knowledge — extracted, not stored. View, edit, or delete everything Kenop holds about you at any time. Full control, not just a privacy policy.

Intelligence threads

Your team reasons
with the report.

After a reason completes, a thread opens. Kenop applies global compute to your specific case — with the full reasoning context already loaded.

kenop
Intelligence threads
Process
Finance
Tender
Contract
HR
Strategy
Participants
K
Kenop
Intelligence
AR
Arjun R.
Plant head
PS
Priya S.
Process engineer
Case
Soya oil neutralisation — refining loss exceeding 1.8x theoretical
CompleteThread
Kenop Intelligence
Investigation complete for XYZ Industries. Neutralisation loss running at 4.2% against 1.8% theoretical. Dominant cause: separator feed temperature at 68°C, 8°C above the recommended ceiling.
kenop-system
Arjun R. — XYZ Industries
That matches what we see on the floor. What is the fastest fix before the next shift?
Kenop Intelligence
Reduce separator feed temperature to 58°C immediately. This recovers 40–50% of excess loss within one shift. Setpoint correction only — no equipment change required.
claude-sonnet-4-6
Priya S. — XYZ Industries
Our Lovibond colour ranges between 37 and 182. The buyer is rejecting inconsistent batches.
Kenop Intelligence
Variance traces to inconsistent bleaching earth contact time. Stabilise at 22 minutes and the range should compress to 40–65, within AOCS limits for your grade.
gpt-4o
R
Investigation report
4.2% loss · 3 root causes · 7 corrective actions
Ask Kenop about the refining process

Most AI tools give you an answer. Kenop gives you a position — one that has been challenged, tested from first principles, and grounded in your data. Then it puts that position in a room with your team.

Kenop Intelligence — built for decisions that matter

Start your first
investigation.

Upload a document. Describe the problem. In fifteen minutes, a structured report — root causes, likelihood scores, corrective actions. Then the thread opens for you and your team.

A 100 TPD soya refinery ran Kenop on a neutralisation loss problem. Separator temperature corrected. Loss cut 40% in one shift — no equipment change.

Start reasoning →