AI model development
Werkon develops or adapts models only when the task, data, and operating context justify it. Every candidate is compared with a simpler baseline, evaluated on protected cases, documented by segment and limitation, and packaged for reproducible release.
Development contract
The development boundary includes the real decision, a useful non-model or existing-model baseline, the full data lineage, protected evaluation cases, model and system constraints, downstream controls, and the people accountable for release.
Model path
The evaluation set, release measures, and simpler alternatives are defined before candidate tuning. This keeps the team from optimizing toward a result it already saw or treating training effort as proof of value.
Define the exact input, output, decision, users, context, consequences, baseline, metrics, segments, uncertainty behavior, acceptance thresholds, human role, and stop conditions.
Confirm rights and provenance, profile quality and representation, resolve label meaning, detect duplication and leakage, create versioned development and protected holdout sets, and record exclusions.
Measure the current method, rules, heuristics, existing models, and lower-complexity approaches under the same evaluation contract before adapting or training a candidate.
Run controlled experiments, track code, configuration, data, artifacts, and cost, examine segment and error behavior, test attacks and misuse relevant to the context, and reject unjustified complexity.
Run the locked holdout evaluation, document uncertainty and limitations, obtain accountable approval, reproduce the chosen artifact, define its system contract, and hand off monitoring, rollback, update, and retirement controls.
Model choice
The choice is not simply build or buy. Several lower-complexity options may solve the task with stronger control, lower operating burden, and easier verification.
Use deterministic logic when requirements are stable, exact, auditable, and testable. A model adds uncertainty without adding useful coverage in these cases.
Evidence: Rule owner, source facts, edge cases, test suite, error path, change frequency, maintenance cost, and baseline outcome.
Use a supported model with prompting, structured constraints, tools, or retrieval when the base capability is adequate and organization-specific context can stay outside its weights.
Evidence: Provider and version, evaluation set, prompt and retrieval configuration, data boundary, limitations, cost, latency, update behavior, and exit path.
Adapt an existing model when representative examples can improve a stable task that prompting or retrieval does not satisfy, and the gain justifies new data and lifecycle ownership.
Evidence: Training rights, clean splits, baseline comparison, ablations, segment results, overfitting checks, artifact lineage, serving plan, and update triggers.
Train a new model when the representation, objective, constraints, or deployment boundary cannot be met responsibly by lower-complexity options and sufficient lawful data and expertise exist.
Evidence: Formal objective, data scale and coverage, architecture rationale, compute and security plan, reproducibility, independent evaluation, operating owner, and retirement plan.
Development controls
Model work can look rigorous while leaking evaluation cases, obscuring data rights, averaging away harmful failures, or losing the exact artifact that produced a result. The controls protect the evidence chain.
Engagement fit
Source basis
NIST AI Resource Center
AI RMF CoreCurrent voluntary guidance calls for contextual baselines, documented test sets and metrics, evaluation under deployment-like conditions, explanation, validation, segment-relevant review, and continuous risk tracking. AI RMF 1.0 is currently under revision.
National Institute of Standards and Technology
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and MitigationsThe March 2025 report covers attacks and mitigations across data, models, training, testing, deployment, private context, and systems whose model outputs can affect real-world actions.
A Systems Audit is the usual starting point. If the opportunity is already clear, we can move directly into a focused build.
Show Us the WorkflowStart with the free automation readiness checklistOBSERVEQUANTIFYDECIDEBUILD
Continue with the work
Stay with the operating question. The technology can wait until the work is clear.
Compare provider choices using the workload, evaluation evidence, data terms, and replacement costs.
Open path02Compare model development with consulting, custom systems, integration, agents, automation, deployment, and lifecycle support.
Open path03Connect the model to sources, deterministic rules, permissions, interfaces, human decisions, evaluation, and owned operation.
Open path04Compare candidate workflows before committing to model development, integration, or a simpler deterministic improvement.
Open path05Define the identity, data, action, failure, and reconciliation contracts needed to use a chosen model inside a product or workflow.
Open path