01
Compute
infrastructure
Build · migrate · recoverHigh-performance computing environments, schedulers, container platforms, private infrastructure, observability, and operational handover.
Independent engineering studio
Build from the
right base.
We turn demanding compute, data, and AI work into systems teams can operate, inspect, and extend.
Built for
Technical startups · Research teams · Data-heavy organisations
01 A system begins at its base
Base layerOPERABLE
01 / Point of view
The hard part is rarely one algorithm. It is everything around it: compute, provenance, failure recovery, access, and the path from a prototype to something people can run twice.
02 / Services
Engineering for
systems under load.
BaseNode Lab takes on bounded, high-consequence work where an early architecture decision can save months of rework.
Compute
Build · migrate · recover02
Private AI
& data workflows
Traceable by defaultLocal knowledge retrieval, RAG evaluation, data pipelines, and automation designed around access boundaries and human review.
03
Prototype-to-
production
A working system, not a slide deckFocused MVP delivery for complex algorithms and research prototypes, including interfaces, packaging, deployment, tests, and an explicit route to ownership.
03 / Selected field notes
Representative systems.
Details stay private.
Client and institutional identities remain confidential. These notes describe the engineering shape of work we can discuss without exposing business data or infrastructure.
FIELD NOTE / 01THROUGHPUT
Petabyte-scale analysis pipeline
A high-throughput processing design for large scientific datasets, with scheduler-aware execution, restartable stages, provenance, and workload visibility.
- System layer
- Compute + workflow
- Typical stack
- Linux · Slurm · Nextflow / Snakemake · Python
- Design pressure
- Large data movement, partial failure, repeatability
FIELD NOTE / 02PRIVATE AI
Local knowledge and automation system
A private retrieval and workflow layer that keeps source access controlled, preserves citations, and leaves material decisions with the operator.
- System layer
- Data + applied AI
- Typical stack
- Python · FastAPI · vector search · job queues
- Design pressure
- Permissions, evaluation, audit trail
FIELD NOTE / 03OPERATIONS
Secure hybrid infrastructure
A monitored, failure-aware topology connecting mixed environments without turning public endpoints into the control plane.
- System layer
- Network + operations
- Typical stack
- Linux · containers · private networking · telemetry
- Design pressure
- Recovery paths, least access, service continuity
No client logos by design. Architecture diagrams and open-source tools will be published when they can be separated cleanly from client environments.
04 / Working method
Scoped tightly.
Handed over cleanly.
- 01
Define the operating constraint
We map the workload, data boundary, failure modes, and ownership before choosing technology.
- 02
Prove the risky path first
The first milestone tests the assumption most likely to break cost, performance, or delivery.
- 03
Keep evidence inspectable
Metrics, provenance, evaluation, and deployment state remain readable by the people responsible for the system.
- 04
Transfer ownership
Delivery includes documentation, tests, recovery notes, and a clear boundary between our work and yours.
05 / Engagement
A short route
to a real decision.
We don't start with a generic transformation programme. We start with the system you need to understand or ship.
- 01
Technical brief
Send the workload, current state, hard constraints, and what a successful handover would change.
- 02
Fit review
We identify missing facts, access needs, and whether a focused engagement is the right tool.
- 03
Written scope
You receive deliverables, acceptance checks, dependencies, and commercial terms before engineering starts.
- 04
Build and handover
Work lands in reviewable increments, then closes with verification and operating documentation.
06 / About the studio
A small engineering lab for difficult systems.
BaseNode Lab is an independent studio started by researchers and engineers with backgrounds in high-performance computing, high-throughput data architecture, and applied AI engineering.
We work under the BaseNode Lab brand while the long-term legal entity is being prepared. We publish no invented headcount, customer list, office address, or certification.
Independent studioRemote by designIdentity-sensitive work stays private
07 / Contact
Bring the
hard constraint.
For a useful first reply, include the system's current state, data sensitivity, delivery window, and the decision you need to make.
hello@basenodelab.com ↗ Project enquiries · Technical partnerships · Research engineering