Design partner opportunity
Training Workbench
Make one local AI training workflow reproducible from approved data to a versioned checkpoint, evaluation, and evidence pack—then prove a second operator can run it.
Your starting point
A problem worth resolving.
This is a fit when
- A partner that needs one reproducible local training/evaluation workflow
- A team willing to test portability and operator independence rather than buy a generic platform
When another approach is a better fit
- A managed general-purpose training platform
- Indefinite tuning, unsupported data rights, or a requirement for a production model outcome
The useful output
What you take away.
- 01Reproducible environment and source/data/model identities
- 02Tokenizer, cache, training, exact-resume, and held-out evaluation records
- 03Generation/reload checks and normalized evidence pack
- 04Operator runbook, intervention log, and witnessed second-operator result
Where this stands
Built so far. What comes next.
A second operator must complete the full bounded 14-stage workflow on a fresh workstation using approved open or synthetic data, with interruption/resume and portable artifacts.
Existing basis
- A registered 14-stage data, tokenizer, training, evaluation, evidence, and release workflow
- Source identities, recovery, evidence, and clean-room control surfaces
- Substantial internal training/workbench implementation
Evidence still needed
- Full 14-stage fresh-workstation execution
- Second-operator completion with founder interventions measured
- End-to-end artifact portability and customer-operability evidence
Working together / 8–16 weeks after gate
From your question to a decision.
- 01Discovery and environment qualification.
- 02Freeze the workflow, identities, rights, operator, and acceptance path.
- 03Run the workflow and recovery checks under controlled observation.
- 04Repeat with a second operator on a fresh target.
- 05Accept, remediate, redesign, or stop.
What we need to begin
- One lawful dataset/right set and bounded model/profile
- A supported target workstation and named operator cohort
- A frozen acceptance protocol, holdout, and compute ceiling
- Permission to preserve failures and reproducibility evidence
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
- A fresh target workstation completes the scoped data-to-evidence path from source identities.
- A second operator completes the workflow using the runbook and records founder interventions.
- Interruption/resume, evaluation, artifact hashes, portability, and limitations pass the signed matrix.
- Any optional benchmark, gateway, or deployment step is accepted only if separately scoped.
Scope and limits
One registered data-to-evidence workflow, environment, operator cohort, model/profile, rights set, and acceptance protocol. Not a general training platform.
A minimal fresh-clone documentation/schema/contract check is not evidence of a complete customer data-to-training workflow.
Offer source and review record
Next step
Explore a partnership around your workflow.
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.