Everyone wants to know how to embedd  generative AI directly into operational workflows to unlock new levels of automation, efficiency, and innovation. But without the ability for organisations to manage their propmpt libraries, and deploy autoomated workflwos through a robust orchestration layer, AI integrations risk becoming fragmented add-ons—difficult to trust, govern, or scale.

TextStem is an Enterprise Prompt Orchestration System (EPOS) designed from the ground up to make AI a first-class capability across the system. Every interaction withtextsem provides the ability to use inbuilt or customised prompts to that are contextually invoked, permission-aware, audit-tracked, and trigger automated data operations seamlessly embedded in the system. 

What is an Orchestration Layer?

Generative models are powerful—but they are not workflow-aware by default. Left unstructured, an LLM:

  • May generate hallucinated or inconsistent data.

  • Lacks awareness of the current context, the user's use-case, the system's record, schema, or business logic.

  • Cannot enforce permissions, data validation, or audit requirements.

TextStem solves this by wrapping every AI invocation in an orchestration layer that:

  1. Injects precise, real-time context—including current data, object states, and user roles, suggested prompts.

  2. Constrains outputs to safe, schema-validated formats (e.g., JSON records, object lists).

  3. Applies role-based permissions to both prompt execution and data access.

  4. Provides human-in-the-loop previews, ensuring traceable, accountable AI interactions.

Unlike general-purpose chat tools, TextStem enables AI to perform meaningful system actions—like bulk-creating structured database records, mapping freeform input into complex models, or generating interrelated data objects based on enterprise logic.

Core Architectural Pillars of TextStem

  1. Prompt Library A curated collection of reusable, permission-scoped prompt templates mapped to specific business objects and workflows.

  2. Contextual Invocation Every prompt is executed with enriched context: current model states, record IDs, field values, UI state, and system constraints.

  3. Structured Response Validation Prompts specify expected output schemas (e.g., array of task objects, Markdown snippet, JSON patch). TextStem validates and parses LLM output before applying changes.

  4. Multi-LLM Abstraction TextStem supports multiple model providers and deployments, routing calls via a flexible provider layer that handles cost tracking, fallback logic, and access policies.

  5. RBAC-Scoped Experiences Access to prompts, context fields, and output actions is tightly governed by role-based access control—defined at the user, group, or organization level.

Core Components

1. Frontend (React-Based UI)

  • AI Action Button Appears in all relevant views (e.g., forms, tables, wizards). Opens a dropdown of context-aware prompt templates.

  • Preview Mode Displays model output in a simulated view. Data is not committed until user approval.

  • Admin Consoles

    • Prompt Explorer: Search, filter, and organize templates by usage, team, or application.

    • Prompt Designer: Define inputs, bind context fields, set output types, and configure RBAC rules.

2. Backend Services

  • Prompt API CRUD for templates with RBAC enforcement and cloning/versioning support.

  • LLM Router Connects to different AI providers, applies rate limits, and logs usage for cost and compliance monitoring.

  • Audit & Preview Service Stores uncommitted previews and finalized interactions for full traceability.

  • Usage Monitor Tracks token consumption by prompt, user, and organization.

3. Data Layer

  • Prompt Table: Metadata, inputs, output schema, and visibility rules.

  • Usage Logs: All activity is audit logged with timestamp, user ID, and context.

  • Credential Store: Per-organization credentials and quota settings.

  • Invocation Context: Optional full context payload logs for high-compliance environments.

4. LLM Provider Abstraction

Unifies model selection, token pricing, failover, and API compatibility across providers.

5. Roles & Permissions

Granular access control for:

  • Who can use a prompt.

  • What context can be shared.

  • Who can publish or approve output.

Key Interfaces & Workflows

AI Button & Prompt Selector

  • Appears within forms, dashboards, and other operational views.

  • Only shows prompts scoped to the current record, model, or user.

  • Provides transparent token usage forecasts.

Execution Flow

  1. Select Prompt

  2. Merge Context

  3. Estimate Tokens

  4. Invoke Model

  5. Preview Result

  6. Approve/Discard

  7. Audit Log Entry

Prompt Design & Discovery

  • Prompt Designer: Build structured, validated, and reusable prompt logic.

  • Prompt Explorer: Browse by role, usage pattern, or integration point.

Access & Oversight Tools

  • Organization-wide dashboards for quota usage.

  • Admin controls for prompt visibility and credential rotation.

  • Exportable logs for compliance and ethics review.

Automating Real Work

TextStem transforms business AI from passive suggestion to active execution. It enables:

  • Structured Data Generation: Turn descriptions into validated database records.

  • Bulk Record Creation: Automate entry of dozens of linked objects from a single prompt.

  • Relational Mapping: Translate unstructured input into interrelated entities with references.

  • System Actions: Generate patches, route updates, and invoke downstream automations.

Unlike tools focused on drafting prose, TextStem is built to reduce time spent in CRUD interfaces—handling data population, transformation, and integration with precision and control.