Energy knowledge, curated by experts.
Served by AI.

Neurohive helps energy companies build, update, curate, and serve knowledge — combining your own data with datasets curated by industry experts, so every answer is grounded, current, and traceable to its source.

Curated fromHydrocarbon ProcessingGlobal Energy InfrastructureU.S. EIAWorld Oil
Hover to see a quick demo

Every answer walks back to a document.

Answers are sampled from real knowledge bases and expert-curated datasets — so every claim keeps the passage it came from.

Which residue upgrading routes hold up on high-sulphur feed?

Delayed coking is the most tolerant of high-sulphur residue. Catalyst consumption climbs sharply above 4 wt%. Deasphalting costs a heavier pitch stream.

Coke sulphur content rises approximately linearly with feed sulphur across the tested range, and the relationship held across all three units surveyed. Above the mid range the coke falls outside anode specification and is directed to fuel-grade outlets, which is the practical constraint on feed selection rather than unit throughput.
Catalyst replacement rate approximately doubles between 3 and 5 wt% feed sulphur. Operators attributed the step change to metals deposition rather than to coking, and reported that it was not recovered by adjusting severity alone.
Pitch yield increases with feed heaviness and requires a dedicated disposal outlet. Where no outlet exists within economic haul distance, transport rather than the deasphalting step itself sets the margin on the unit.
Sources

Deep, expert-curated knowledge — ready on day one.

Anyone can index documents. Neurohive’s datasets are curated by subject-matter experts who know not just which data is relevant to a question, but also its limitations — and how to combine technical, analytical, and cost perspectives the way a specialist working a real business problem would.

That expert judgement is built into the platform itself, so complex questions and workflows get answers you can defend — not just fluent text.

Decades of published technical knowledge across refining, petrochemicals, and midstream processing — searchable and conversational.

A structured dataset of energy projects across the globe — capacities, status, ownership, and involvement — built for analytical queries and automated reports.

Trusted, verifiable answers on upstream operations and technology, drawn from one of the industry's most established archives.

Independent statistics and analysis from the U.S. Energy Information Administration — production, consumption, prices, and forecasts across every fuel.

Everything you need to turn data into decisions

Research

Explore technical, analytical and cost questions across every dataset you can access - with answers grounded in verified sources, not only the open web.

The Neurohive chat box, asking a question with the World Oil dataset attached.

Sources

Answers are sampled from real knowledge bases and expert-curated datasets — so every claim keeps the passage it came from.

Search Results (2)

1NIST | Sustainable Metals Processing and Alloy Development

The National Institute of Standards and Technology (NIST) is committed to advancing sustainability in the metals processing industry by developing cutting-edge technologies, standards, and data infrastructure…

2Hydrocarbon Processing | High-Performance Duplex Stainless Steel Alloys for Deepwater Drilling Applications

Technical specifications and corrosion resistance properties of 2205 and 2507 duplex steels used in subsea equipment and drilling risers.

Datasets

Turn your documents and data into curated, governed datasets - kept current as things change, and usable alone or alongside the curated library.

Documents being selected from a folder and turned into a dataset.

Projects

Keep research, analysis and reports organized around the business questions that matter, so knowledge compounds instead of scattering.

The Neurohive chat box asking why a well was flagged as an anomaly, with a production summary spreadsheet attached.

See it in action

Hover to play

Your data and ours. Working Together.

Build your own datasets with the Dataset Builder and use them independently or in combination with Neurohive’s curated datasets. One governed knowledge layer — and all your team’s work runs on top of it.

Two sources — your own dataset, built with the Dataset Builder and governed by you, and Neurohive’s curated datasets covering Hydrocarbon Processing, GEI, World Oil, EIA and more — feed one expert-curated knowledge layer, which provides relevant sources, known limitations, always-updated data and traceable answers to three surfaces: Reports, Projects and Research.

  1. Never used for training

    We never use your data to train models of any kind.
    Your content powers your answers — nothing else.

  2. Isolated per tenant

    Every customer gets a separate index, with access controls governing exactly who can reach which data inside your organization.

  3. Enterprise-grade Infrastructure

    Built on AWS services with robust protections in place, following cloud security best practices end to end.

  4. Nothing retained after deletion

    When you delete a document or a dataset, we remove it — the source, its index entries, and everything derived from it.

Your data. Our AI engine.
New revenue.

Neurohive builds the AI layer that turns your content and data — whether editorial archives or structured project and market datasets — into instant, trustworthy answers and reports for your employees and customers.

  1. Publish

    Publish and share your data as a curated dataset on the Neurohive platform, so your existing clients can run more complex use cases on it — and your reach grows.

  2. Monetise

    AI search and automated report generation become premium features that support your subscription and data-access tiers — not a free commodity.

  3. Integrate

    We integrate AI search and analytics directly into your existing site and database — working with the vendor already running your platform. No rebuild required.

From first call to production, without the leap of faith

  1. 1. Discovery call

    We work out whether your problem is a knowledge problem, and what data would have to be in reach to solve it.

  2. 2. Proof of concept

    A small-scale working solution scoped to your use case — typically a fully working POC within 1–2 weeks of data access.

  3. 3. Full scale roll-out

    Once it earns its place it widens — more sources, more teams, and datasets that keep themselves current.

If you work in energy, we’d like to talk.

Bring one question and the data behind it. That is the whole evaluation.