Private · Provenance-aware · Verifiable

Trusted AI infrastructure for humanity's knowledge.

Alacene is building secure information infrastructure for organizations that need AI systems grounded in source truth — from private search and document intelligence to provenance-oriented RAG and local deployment.

Pre-release · Building with select partners
An indigo cord loops and knots around pastel geometric fragments, binding them into an interlocking structure within a protective arc, representing connected knowledge and traceable sources.
The problem

Teams have the knowledge. Their AI can't show where it came from.

Notes, tasks, tables and files are scattered across tools, and AI answers from them without pointing back to anything a person can check.

Answers without sources

An answer that can't be traced to the text it came from can't be trusted by the people who have to act on it.

Knowledge in pieces

Documents, tasks and files live in separate tools, so no single place — and no assistant — sees the whole picture.

Documents handled by hand

People still copy, paste and reconcile documents manually to get answers out of them.

Private text, outside vendors

Sending sensitive work to a third-party model is a risk many teams can't take.

Our approach

Alacene is building the trust layer for knowledge work.

One workspace where notes, tasks, tables, files and AI work are all blocks. Every block is searchable as you write, AI reads exactly the blocks you give it, and every answer cites them.

Not another chatbot.

AI works inside your documents, on the exact blocks you choose — not in a separate chat window.

Not another document tool.

What you write becomes searchable, citable context — for your team and for your AI.

Answers you can trace.

Every answer links to the exact blocks it used, so anyone can check it.

How it works

From your documents to a cited answer — and back.

Everything is a block. Blocks are indexed as you write, AI assembles its context from the blocks you choose, and its answers return to the document as blocks you can edit, search and cite.

  1. 01

    Write

    • Text, tasks and tables as blocks
    • Real-time collaboration
    • A stable ID for every block
  2. 02

    Index

    • Searchable as you write
    • Keyword and semantic search
    • Results respect permissions
  3. 03

    Ask

    • AI reads the blocks you select
    • Only what you're allowed to see
    • Works with local models
  4. 04

    Cite

    • Answers cite exact blocks
    • Unknown citations rejected
    • Each run's context recorded
  5. 05

    Keep

    • Answers land as blocks
    • Edit, undo and search them
    • Indexed again next time
  6. Answers become context
Capabilities

Capabilities under active development.

Alacene is currently building with select collaborators and strategic partners. Each capability is shipped as part of a single coherent infrastructure layer.

IN DEV

Private Search Engine

A search layer built from the ground up for secure, provenance-aware retrieval across humanity's knowledge.

IN DEV

Provenance-Oriented RAG

Generate answers grounded in source documents, with citations, traceability and claim-level verification.

IN DEV

Document Intelligence

Parse, classify and structure complex documents — including PDFs, scans, tables, forms and enterprise files.

IN DEV

Local AI Infrastructure

Deploy AI workflows in private, on-prem or air-gapped environments with a software and hardware stack.

IN DEV

Verifiable Claims

Track where answers come from, how claims are supported and which sources were used at each step.

IN DEV

Specialist Assistants

Build domain-specific assistants for analysts, engineers, researchers, legal teams and support workflows.

Why now

Trust is becoming the bottleneck for enterprise AI.

As organizations adopt generative AI, the limiting factor is no longer model access. It is whether the system can be trusted with sensitive information, audited, verified and deployed within operational constraints.

Provenance/ 01

Provenance before automation

Workflows that act on AI output need a clear chain of evidence — not a confidence score attached to a paragraph.

Verification/ 02

Verification before scale

Throughput is meaningless if outputs cannot be checked. Verification is a first-class primitive, not an afterthought.

Local control/ 03

Local control before dependency

Sensitive information must remain under organizational control. Local and air-gapped deployment is the default, not the upgrade.

Deployment

Designed for private and controlled environments.

Alacene is designed to operate inside your perimeter. Documents, models, search index and user workflows remain within an enterprise-controlled boundary.

  • Local hosting
  • Air-gapped deployment
  • Private data boundaries
  • Hardware/software bundle
  • Apple Silicon optimization
  • Enterprise-controlled flow
Secure boundaryLOCAL · ON-PREM · AIR-GAPPED
Internal endpoints
In-perimeter inference
Authorized Mac Studio endpoints served by Alacene inside the perimeter
No outbound model calls required
Outbound: blocked
Telemetry: opt-in
Keys: customer-held
Inference: in-perimeter
About

A small team building something deliberate.

Alacene is built by a founding team with backgrounds in applied AI, distributed retrieval and information products for high-trust environments.

Read more about us
Abdalaziz Al-Maeeni
Abdalaziz Al-Maeeni
Co-founder — Research, Modelling, Infrastructure & Inference

Scientist and engineer with publications in Nature and collaborations with a Physics Nobel laureate. Former CERN engineer and LAMBDA researcher (HSE), with experience spanning materials science and principal data science, delivering Bayesian systems for live entertainment and pharmaceuticals. Specializes in interpretable ML for physical and high-stakes systems.

Daria Galimzianova
Daria Galimzianova
Co-founder — Retrieval & Trust

PhD researcher at MBZUAI and former Senior NLP researcher in industry. Published at A* conferences on retrieval-augmented generation, uncertainty in LLMs and trustworthy QA. Shipped a factual question-answering skill inside a commercial virtual assistant used at scale.

Maxim Kochurov
Maxim Kochurov
Co-founder — Modelling & Infrastructure

Core developer of PyMC and active NixOS contributor. Built large-scale training infrastructure for computer vision and led Bayesian engineering across pharmaceuticals, consumer goods and live streaming. Invited lecturer in Practical Bayes at Moscow State University.

The name

آلة

Arabic · machine

-cene

Greek · new

A new machine era, where your knowledge stays yours.

Program

Built with select strategic partners.

Alacene is looking for early collaborators with complex information environments, high trust requirements and a need for secure, AI-powered knowledge workflows.

Ideal partners
  • Regulated enterprises
  • Industrial companies
  • Defense-adjacent organizations
  • Research labs
  • Legal & compliance teams
  • Healthcare & life sciences
  • Technical support organizations
  • Knowledge-heavy SMBs
What partners get
  • Early access to emerging capabilities
  • Influence over product direction
  • Technical collaboration with the founding team
  • Tailored deployment discussions
  • Priority onboarding when the platform matures
Working with us

Help shape the future of trustworthy AI infrastructure.

If your organization needs AI systems that can search, reason, cite, verify and operate within secure environments, Alacene wants to work with you.