AI Systems
LLM Application Development for Production Business Apps
LLM application development is building software features around a large language model: answering questions from your own documents, drafting replies or summaries, pulling structured fields out of messy text, or helping staff search what the company already knows. The model is one component; the application around it decides what the model sees and what happens to its output.
Teams usually come to us after a promising prototype. It answered well in the demo, then gave confident wrong answers on real questions, showed data a user shouldn't see, or became slow and unpredictable as usage grew. Fixing that is application engineering: retrieval from the right sources, permission checks, structured outputs, evaluation and monitoring.
We build LLM features into products, portals and internal tools, and keep a person in the loop where an output carries risk. When the model must take actions in other systems rather than produce text, see AI agent development.
Best fit
The business wants an LLM feature inside a product, portal, or operational tool.
The use case requires retrieval, structured context, or guardrails around output.
Leadership needs an LLM system that can be maintained, measured, and improved over time.
Related work
Tension Radio (an AI content platform): content generation and scripting with versioned prompts and templates, explicit boundaries for content approval, and generation cost variability controlled with caching, batching and queue priorities. The case study reports 24/7 programming coverage without manual scheduling during steady-state operation.
Guardrail guides: AI prompt and policy governance and how to stop prompt injection attacks.
Example use: an AI knowledge base for internal operations.
What we typically deliver.
The exact scope depends on the workflow and system landscape, but these are the core engineering elements usually involved.
LLM-enabled application flows for internal or external users.
Retrieval, grounding, prompt management, and review logic where appropriate.
Backend services and orchestration around model access, latency, and fallback behavior.
Operational instrumentation for usage, quality, and cost visibility.
How the build usually runs
- 1
Collect real questions or documents, write down what a correct answer looks like, and turn them into an evaluation set before choosing a model.
- 2
Build retrieval from approved sources, per-user permission checks, prompt versioning and output validation. If the feature lives in an app made with an AI builder, run the App Security Check first to catch model keys exposed in the browser and database tables open to the public.
- 3
Release to a small group, track answer quality, response time and usage per feature, and adjust prompts, sources or models from those results.
Ready to evaluate fit?
Talk through the workflow, constraints, and likely delivery path.
The best next step is usually a practical conversation about the system, users, integrations, and failure modes rather than a generic intake form.
Outcomes teams should expect.
A more useful LLM product than a thin chat wrapper.
Better reliability and trust around model-backed interactions.
Clearer control of model cost, latency, and system behavior.
A foundation that supports iterative product improvement.
Common questions
What is LLM application development?
It is building software that uses a large language model as one part of a product or internal tool, together with the data access, retrieval, interface, permissions, testing and monitoring needed to run it reliably.
What is the difference between an LLM application and an AI agent?
An LLM application mainly produces text or structured output for a person or system to use, such as an answer, a summary or extracted fields. An AI agent goes further and takes steps in other systems toward a goal.
How do you keep an LLM from making things up?
Answers are grounded in passages retrieved from approved sources, the app is allowed to say it doesn't know, outputs are checked against expected formats, and a test set of real questions is rerun whenever prompts or models change.
Can an LLM application use our private data safely?
Yes, with controls. Retrieval respects each user's existing permissions, sensitive fields can be masked before they reach the model, and prompts and responses are logged for review under your retention rules.
Which language model should we use?
It depends on the task, how sensitive the data is and the response time you need. We design the app so the model can be swapped, and compare candidates on your own evaluation set rather than general benchmarks.
Related pages.
Use these pages to explore adjacent engineering capabilities and connected delivery work.