It’s easy to get caught up in the hype around large language models (LLMs). But at DeepHorizon, we treat them not as magic — but as powerful tools that require structure, control, and context to actually be useful.
And when you frame it that way, LLMs become something even more valuable: collaborators.
LLMs in Our Workflow
We integrate LLMs at multiple levels of the build process — from internal ops to client-facing systems. Some examples:
- Code and config generation during setup of data pipelines
- Natural language interfaces to surface insights from structured analytics
- Automated documentation generation for ML pipelines and API wrappers
- Content drafting and validation for dashboards, alerts, or even reporting layers
In all cases, we treat the LLM not as the answer — but as the accelerator.
Guardrails and Ground Truth
We never plug an LLM directly into core decision logic. Instead, we:
- Wrap it with validation logic and confidence scoring
- Use prompt engineering templates designed to enforce structure
- Blend LLM outputs with retrieval-based grounding from our own datasets
- Always keep a human-in-the-loop for client-facing systems
This is what lets us go from clever response to reliable functionality.
Modular Models, Modular Teams
Our philosophy has always been modular — and LLMs fit right in. Instead of embedding them in tightly coupled pipelines, we containerize, version, and orchestrate them just like any microservice. This allows us to:
- Test multiple providers in parallel (OpenAI, Mistral, Claude, etc.)
- Swap models based on task type or deployment constraints
- Maintain explainability and reproducibility, even in dynamic systems
Why It Matters
LLMs don’t replace critical thinking. But they do extend the surface area of what’s possible. When paired with good engineering, strong data, and tight architecture, they don’t just answer questions — they open new ones.
We don’t just use LLMs.
We work with them.
