InfoDump SLM Studio
Build role-specific small models without becoming an ML engineer
InfoDump SLM Studio wraps the role design, example generation, evaluation, training, packaging, and testing workflow in a local desktop app for non-technical teams.
Studio helps the model learn how a role thinks. Private company facts stay in InfoDump's governed retrieval layer, where source access, permissions, and auditability can be controlled.
InfoDump SLM Studio
Local role model workspace
Workflow
Role
CFO
Examples
Ready
Eval
Reviewing
Behavior evaluation
Checks role reasoning, output format, and policy boundaries before training.
Private data policy
Train role behavior. Keep company knowledge in governed retrieval.
Built For Operators
The work of a data engineer, packaged for a business user
A founder, finance lead, operator, or domain expert should be able to shape a role model without learning the model tooling stack. Studio turns that work into a guided product flow.
No Python environment setup
No terminal commands
No JSONL hand-editing
No eval schema design
No model packaging work
No Hugging Face workflow knowledge required
Studio Workflow
From role definition to local testing in one product flow
Studio is designed around the actual lifecycle of a role-specific SLM: define what the role should do, generate examples, evaluate behavior, train, package, and test.
01
Define the role
Describe what the role should do, how it should reason, what it should avoid, and what a good answer looks like.
02
Generate examples
Create and review training examples from a guided interface without writing datasets by hand.
03
Evaluate behavior
Run structured checks that show whether the model follows the role, tone, boundaries, and expected output style.
04
Train the model
Start fine-tuning from the Studio workflow while the complex model operations stay behind the interface.
05
Package and publish
Prepare a role-specific model for controlled use, local testing, and InfoDump-governed deployment paths.
Governance Model
Teach role behavior without putting private facts into the model
Studio separates the behavior a role model should learn from the private company context it may need at runtime. That keeps the commercial story aligned with InfoDump's privacy and data ownership posture.
The model learns behavior
Studio helps teach a model how a CFO, CTO, or operator should think, structure answers, and follow boundaries.
Company facts stay governed
Private company data remains in InfoDump's retrieval layer, where access, permissions, and source controls can be applied.
Testing happens before rollout
Teams can publish and test role behavior before connecting it to governed company context or broader workflows.
Role Templates
Start from the role your company actually needs
Studio can support role-specific models for finance, technical, executive, operations, people, and revenue work while keeping each model narrow enough to evaluate.
CFO
CFO SLM
Shape a model around finance reasoning, budget review, runway context, board summaries, and operating performance.
CTO
CTO SLM
Create a model that understands engineering tradeoffs, technical planning, infrastructure risk, and architecture notes.
CEO
CEO SLM
Build executive behavior for prioritization, strategic review, investor updates, leadership briefs, and decision framing.
COO
COO SLM
Support operating reviews, vendor context, process bottlenecks, execution risk, and cross-functional planning.
HR
HR SLM
Guide people operations work around policy, onboarding, internal knowledge, workforce planning, and compliance support.
Sales
Sales SLM
Tune revenue behavior around pipeline review, account summaries, deal risk, customer context, and forecast support.
Build With Studio
Give business teams a local way to create role-specific AI
InfoDump SLM Studio turns role modeling into a guided commercial workflow while InfoDump keeps private data governed through retrieval, permissions, and auditability.