Generative AI & Large Language Models
Language-model workflows designed around your data, systems, and review process.
Put Language Models to Work in a Business Workflow
Large language models can draft, summarize, classify, extract, and answer questions over language-based information.
Their usefulness depends on the surrounding workflow: what context they receive, which tools they can use, how outputs are checked, and where people make the final decision.
Tactical Edge connects the model to approved data and systems, then tests the complete workflow against the measures your team defines.
What an LLM Workflow Needs Beyond the Model
A model response is only one part of a useful workflow. The surrounding data access, evaluation, permissions, integrations, and human review determine how the output can be used.
Missing relevant business context
Permissions that do not match the source systems
Unsupported or inconsistent outputs
Missing tests, records, and review steps
No connection to downstream actions
Design the Complete Workflow
Tactical Edge plans for production from the start, including data access, quality testing, security, monitoring, and day-to-day operations.
Retrieval-augmented generation (RAG)
Retrieve relevant material from approved sources and show the evidence used for the response.
Secure data access and isolation
Apply and test access controls for the data sources and query paths in scope.
Prompt and model governance
Version and test the prompts and model configurations used by the workflow.
Observability and logging
Record the model and tool activity your operators need for review and troubleshooting.
Workflow and system integration
Connect LLMs to the approved systems, APIs, and workflow steps in scope.
LLMs Power Agents - They Are Not the Agent
A language model can interpret and generate information, but it still needs workflow state, tools, permissions, tests, and clear handoffs.
Within agentic systems, LLMs are used to:
Interpret unstructured information
Reason over context and constraints
Generate plans, explanations, and actions
Communicate with humans and systems
We define which actions the system may take, which require approval, and what evidence the operator sees before deciding.
Controls Your Team Can Test
Start each engagement by defining the security, privacy, governance, and compliance requirements the system must meet.
Data privacy and isolation
Define data boundaries and test allowed and denied access paths for the workflow.
Role-based access control
Map permissions to the roles and responsibilities your team defines, then test allowed and denied paths.
Model and prompt versioning
Keep a useful history of important prompt, model, policy, and configuration changes so your team can test, review, and roll back releases.
Continuous monitoring and evaluation
Monitor the quality, latency, cost, errors, and review signals selected for the operating workflow.
Human-in-the-loop mechanisms
Route important decisions and sensitive operations to the people your team authorizes.
What the Workflow Can Support
Search and answer workflows over approved institutional knowledge
Drafting, summarization, extraction, and classification tasks
Reviewable decision support with cited source material
Tested behavior, exception handling, and human review
Integration with approved agentic workflows
Frequently Asked Questions
Generative AI consulting helps teams choose a business task, compare suitable models, connect approved data, define access and review steps, and test quality, latency, cost, and operating fit before launch.
An LLM can work with approved company context through prompt inputs, retrieval-augmented generation, or a suitable customization method. The right approach depends on the task, data permissions, quality tests, latency, cost, and how the output will be reviewed.
The design depends on the workflow. Common parts include approved context retrieval, versioned prompts, model access, role-based permissions, activity records, monitoring, evaluation, and human review for important outputs.
We agree on a quality rubric, build a representative test set, compare candidate models and retrieval options, check important outputs with deterministic rules or people, and monitor the measures the operating team needs.
See how generative AI integrates into agentic systems