LemonadaCore · What we deliver
Improve an AI application against measurable business goals, using evaluations before changing prompts or models.
We build an evaluation set from representative tasks and failure cases. Quality, response time and cost are measured together so an apparent saving does not hide a worse user experience.
We compare prompt design, retrieval quality, caching, model routing and smaller models. Fine-tuning is considered when the data and task justify it, rather than treated as a default requirement.
Candidate changes are checked against a baseline, including regressions and unusual inputs. We document trade-offs and monitor the chosen approach after release. Performance improvements depend on the starting system and workload.
We review your use case, inputs and constraints, then agree on deliverables, acceptance criteria and a proposal.
Yes. We first assess the current tools, access and code or data, then propose the changes needed.
Describe the problem in your own words. If AI is not the right answer, we will say so.
Tell us what is not working. A message on WhatsApp or a call reaches us directly — no form, no queue.
Prefer email? info@lemonadacore.com