AI Engineering

Local LLM and secure AI systems for regulated organizations.

137Forge helps organizations review AI risk, design private LLM and RAG-backed workflows, and engineer systems that operate inside defined data, identity, security, and control boundaries. The work is built for environments where AI has to be useful, auditable, and maintainable.

Local AI 01

Local Inference Architecture

Plan deployment patterns for local or private models, GPU or server placement, network boundaries, identity controls, and operational ownership.

Local AI 02

Controlled Retrieval

Design RAG workflows around approved sources, source attribution, provenance, retention, and access rules that match the organization.

Local AI 03

Validation and Evidence

Connect prompts, retrieval behavior, logs, human review, and output handling to validation notes and control evidence.

Reference Architecture

Secure AI architecture design

This is a practical model for regulated environments: approved use cases, evidence sources, model choices, and data sources are assessed, mapped to controls, and exposed through governed AI workflows for reviewable assistance, documentation, and analysis.

Zone 01

Business Context & Data Sources

Policies, procedures, runbooks, and engineering notes

Infrastructure, identity, and security control evidence

Vendor materials, findings, questionnaires, and audit artifacts

Approved internal knowledge bases and local model constraints

Zone 02

Security and Governance Core

Evidence normalization and provenance tracking

Use-case, data, and risk assessment

Control mapping and traceability workflow

Secure retrieval, access, model, and AI workflow boundaries

Governance Rail

Identity, approvals, provenance, monitoring, and review expectations remain attached to the workflow instead of being added after the fact.

Zone 03

Validated AI Outputs

Controlled AI or local LLM workflow

RAG-backed knowledge workflow

Evidence and control mapping view

Validation evidence and operating guidance

System Output 01

Data and use-case boundary model

System Output 02

AI risk and control alignment

System Output 03

RAG-backed knowledge workflow

System Output 04

Validation and operating guidance

Architecture Flow

From evidence to governed AI workflow.

This platform pattern is the architecture flow 137Forge uses to review, design, validate, and hand off AI workflows that need traceability across local, cloud, or hybrid systems.

Flow 01

Source Evidence & Context

Identify policies, runbooks, evidence, architecture notes, vendor materials, and approved data sources that can support an AI workflow.

Flow 02

Provenance & Boundary Review

Clarify ownership, sensitivity, identity boundaries, source provenance, and where retrieval or model access should be constrained.

Flow 03

Control Mapping

Map use cases, documents, risks, and control expectations so AI outputs remain traceable to the operating environment.

Flow 04

Governed Retrieval

Shape RAG-backed knowledge workflows, prompt boundaries, and output review paths around approved data and accountable users.

Flow 05

Validation & Sustainment

Produce reviewable guidance, validation evidence, and operating notes that internal teams can inspect, maintain, and improve.

AI Security Flow Diagrams

Secure AI from use case to validated operations.

These flows separate architecture design from validation so teams can see where data boundaries, access controls, human review, and operating evidence enter the system.

AI Security Flow

Use Case to Output

Use Case Intake

Clarify the intended AI workflow, data sensitivity, users, business owner, and decision points before architecture choices are made.

Data and Identity Boundary

Define which data sources, accounts, roles, networks, vendors, and retrieval paths the workflow can reach.

Retrieval and Prompt Controls

Shape approved retrieval, prompt boundaries, logging, source attribution, and output handling around the organization's rules.

Reviewable Output

Keep human review, ownership, and validation attached to generated outputs before they become operational guidance.

AI Validation Flow

Risk to Operations

Risk Scenario

Identify misuse, data exposure, access bypass, inaccurate output, vendor dependency, and operational failure scenarios.

Control Mapping

Map each scenario to expected safeguards, review points, documentation, and accountable owners.

Evidence and Testing

Review logs, access paths, source provenance, prompt/retrieval behavior, and validation evidence against the intended boundary.

Operational Improvement

Deliver operating notes, residual risk, remediation steps, and ownership guidance internal teams can maintain.

System Lane

Useful AI without bypassing security or control boundaries.

137Forge keeps AI grounded in approved use cases, defined data boundaries, reviewable outputs, and accountable ownership. The system should fit how the organization operates instead of becoming an unmanaged shortcut around existing controls.

Security Check 01

Use-case, data, and business-risk review

Security Check 02

Identity, access, and data boundary design

Security Check 03

Prompt, retrieval, and output control review

Security Check 04

Vendor, model, and workflow risk review

Security Check 05

Human review and validation evidence

Security Check 06

Evidence alignment for AI-adjacent workflows

Security Check 07

Operating guidance for on-premises, cloud, and hybrid AI workflows

Architecture Principles

Useful AI systems need boundaries as much as automation.

Principle 01

AI workflows operate inside defined data, identity, and access boundaries rather than broad unmanaged access.

Principle 02

Use cases, source documents, control expectations, and generated outputs stay linked for traceability.

Principle 03

AI support should preserve accountable ownership, review, and judgment instead of replacing them.

System Fit

Designed for environments where data access, architecture, governance, and review have to stay in sync.

This direction is especially useful when teams want AI capability inside the organization but need data protection, clear ownership, controlled retrieval, and validation before broad rollout.

When implementation support is scoped, 137Forge can engineer AI workflows across local, on-premises, cloud, or hybrid environments while preserving ownership, data boundaries, and review responsibilities.

Local LLM, on-premises, cloud, and hybrid environments

Compliance and security groups that need reviewable AI workflows

Teams evaluating private, local, or controlled AI architecture

Talk through your environment with 137Forge.

Reach out to discuss security advisory, AI engineering for local LLM systems, secure architecture design, control readiness, targeted engineering, risk assessment services, or cybersecurity training.