AI Implementation
LLMs that hallucinate in production are worse than no LLMs at all
We implement generative AI only when the use case makes sense and accuracy can be verified.
Everyone is building with LLMs. Few are shipping reliably.
Demos look impressive. Production is different. LLMs hallucinate. Costs spiral. Latency kills user experience.
We implement generative AI for specific, measurable use cases: document analysis, content generation, code assistance. We validate accuracy, control costs, and build fallbacks when models fail.
When we build with LLMs
After diagnosis reveals a legitimate automation opportunity:
Document analysis & extraction
Parse contracts, invoices, reports. Extract structured data from unstructured text.
Content generation workflows
Marketing copy, documentation, summaries. With human review baked in.
Code generation assistants
Internal developer tools. Boilerplate generation. Test case creation.
Custom model fine-tuning
When off-the-shelf models aren't accurate enough for your domain.
When generative AI doesn't make sense
• When accuracy must be 100% (use deterministic systems instead)
• When latency matters more than flexibility (use traditional search)
• When you don't have evaluation metrics (you can't improve what you can't measure)
• When a simpler solution exists (LLMs are not always the answer)
Not sure if your use case fits? Start with a Systems Health Check to identify where automation actually creates value.
