A small, measurable workflow is a good place to begin.
Start with one useful task, approved information, and a way to measure the result. Test realistic questions and missing information before expanding the assistant. Keep review and escalation available where an incorrect answer would matter.
- Target high-volume, structured text and document processing before attempting generative conversational AI.
- Use approved organizational knowledge, evaluation, and escalation to reduce the risk of inaccurate AI answers.
- Enforce deterministic human-in-the-loop escalation paths for sensitive or high-impact actions.
Look for repeatable work
Start with the questions your team answers repeatedly, documents they read, or information they search for. Choose a contained process with a clear owner and a way to judge whether the output is useful.
- Audit internal ticketing and customer inquiry queues for the top 5 recurring inquiry patterns.
- Identify document-heavy processes such as invoice parsing, policy verification, and proposal drafting.
- Define quantitative success metrics (e.g. 50% faster inquiry triage) before writing code.
Make the information dependable
Identify the sources the system can use, who can access them, and how they stay current. Decide what should happen when an answer is uncertain or a document is incomplete.
- Implement Retrieval-Augmented Generation (RAG) using vetted PDFs, internal wikis, and verified knowledge bases.
- Restrict vector search boundaries to prevent cross-tenant or unauthorized internal data access.
- Program the model to return verifiable source citations with every synthesized answer.
Keep a person in the loop
Define the decisions that need review before taking action. Test with realistic examples, including mistakes and exceptions. Compare the time saved with the work needed to check the output before expanding the system.
- Require explicit operator sign-off before executing financial transactions, sending external emails, or mutating live database state.
- Log confidence scoring alongside every prediction to highlight low-confidence items for human review.
- Track model token consumption and latency metrics in production dashboards.
Frequently asked questions
How do you stop AI assistants from making up incorrect facts (hallucinations)?
We constrain the model strictly to your verified private documents using semantic embeddings and deterministic prompt fences. If the retrieved documents do not contain the answer, the system is programmed to state that clearly and escalate to a staff member.
Is our private company data protected from being trained on by external AI providers?
Data handling must be assessed for the actual provider, endpoint, settings, and agreement. We review permissions, retention, training terms, and hosting requirements before selecting the design. No retention arrangement should be assumed from the label enterprise or custom.
Ahmad Nadeem · CEO & Co-founder
Ahmad is an AI graduate from the University of Management and Technology. His work spans machine learning, computer vision, natural language processing, and software products. At Cortlinx, he leads the technical direction and turns business needs into practical product decisions.
Connect with Ahmad Nadeem on LinkedIn ↗