Work with us
Private AI Deployment Sprint
Test a private AI workflow on your infrastructure. Take away a tested setup, instructions to run and restore it, and a clear account of its limits.
Your starting point
Would this help?
This is a fit when
- Sensitive but non-classified document and knowledge workflows
- Internal drafting, retrieval, extraction, or coding assistance with human review
- A team that can define one decision and freeze a representative evaluation set
- A clear reason to keep model execution or data inside an approved boundary
When another approach is a better fit
- Clinical, weapons, transport, infrastructure, or other safety-authoritative decisions
- Classified, patient-identifiable, export-controlled, or unapproved critical material
- Public-network production service, broad autonomous action, or 24/7 support
- An undefined transformation programme without one decision owner and stop rule
The useful output
What you take away.
- 01A tested setup for the job you agreed.
- 02Results showing where it works and where it falls short.
- 03Instructions your team can use to run it and restore it.
Full project checklist
- 01Decision brief and information-boundary diagram
- 02Model/runtime selection record and frozen evaluation pack
- 03One bounded local implementation with witnessed acceptance results
- 04Operating and restore runbook, limitations register, and release evidence pack
- 05Executive accept, remediate, extend, or stop recommendation
Working together / 2–4 weeks
How we’ll work together.
Choose one job your team needs to run privately. Agree the computer, examples and checks, then test the setup and practise running and restoring it.
The project steps in detail
- 01Qualify the decision, rights, owner, deadline, and no-bid conditions.
- 02Freeze the workflow, boundary, baseline, evaluation, and acceptance matrix.
- 03Select and implement one supported model/runtime path.
- 04Run witnessed task, boundary, failure, performance, and restore checks.
- 05Hand over the system, runbook, evidence pack, limitations, and decision.
What we need before work starts
Before starting
- One repeated, valuable, non-safety-authoritative workflow
- A lawful sandbox and representative inputs that may be handled in the agreed environment
- Customer-controlled target hardware or an approved procurement path
- Named workflow, technical, security/data, and economic owners
- Permission to preserve enough evidence to reconstruct the decision
A signed statement of work, approved data/model rights, representative frozen inputs, target environment, and acceptance owner are required before work starts.
Scope and handover
What we agree before starting.
We agree the work in a signed statement of work before starting. This page explains the offer; the signed agreement sets the commitment.
How you accept the handover
- The frozen task set meets the agreed usefulness and failure thresholds.
- The implementation stays inside the approved data, network, user, and model boundary.
- A named operator completes start, stop, monitoring, and restore checks.
- The customer records accept, remediate, extend, or stop against the signed matrix.
What this work covers
Bounded founder-led service; inquiry and qualification open.
No production high availability, public exposure, autonomous action, continuous learning, broad fine-tuning, certification, 24/7 support, or unbounded integration.
Available means NeuroForge can scope founder-led bounded delivery. It does not mean a generally available software product, production SLA, or proven customer scale.
Offer source and review record
Questions
Before you enquire.
Does the sprint require ERAIS?
No. The selected model and runtime are chosen for the approved workflow, hardware, rights, and boundary. A qualified third-party open-weight component may be the correct choice.
Is this an enterprise AI rollout?
No. The standard boundary is one workflow, one environment, and a named operator cohort. Production service obligations require a separate gate and scope.
Can the answer be stop?
Yes. A properly evidenced decision not to deploy can be the most valuable result.
Talk to us
What’s getting in the way?
Tell us what keeps going wrong and what you need to do once it’s sorted. We’ll come back with what we think could help.