Bounded service
Private AI Deployment Sprint
Prove—or disprove—that one useful workflow can run locally, then leave with the tested system and the evidence behind the decision.
Your starting point
A problem worth resolving.
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.
- 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
From your question to a decision.
- 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 to begin
- 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
Scope and handover
A decision you can act on.
Customer work is controlled by a signed statement of work. Public pages provide information and do not create a service commitment.
Acceptance conditions
- 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.
Scope and limits
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.
Next step
Bring one decision worth resolving.
Tell us what is getting in the way, the outcome you want and your timeframe. A short description is enough to begin; leave sensitive material out of the first message.