The bot sometimes invents facts and I don't know how often.
We build a test set from your real queries and measure the share of wrong answers. You get a report with examples of failures.
An AI that is right, not just confident
We measure how often your AI gets things wrong and fix it: test sets, answer evaluation, guardrails and, when justified, fine-tuning. You get numbers on your own data, not general promises. This is the step that makes the site, customers and automation chain safe to run.
Quote and schedule after your brief
Who it's for
- Teams with a chatbot or AI agent they cannot yet trust
- Companies about to launch AI to customers and worried about errors
- Businesses where mistakes are costly: health, finance, legal, warranties
- Owners switching models or prompts who need to know if it got better
Problems we solve
A prompt or model change might have broken something.
We set up regression tests (the same queries run before and after every change). A new version ships only if it is not worse than the old one.
The bot promises discounts, leaks information or goes off topic.
We add guardrails (rules that block unwanted answers): allowed topics, price checks, personal data filters, handoff to a human.
I can't tell whether I need fine-tuning or just a better prompt.
We try cheaper options first: prompt, retrieval from your knowledge base, examples. We suggest fine-tuning only when the numbers show it is needed.
What's included
- Review of your use cases and a list of unacceptable answers
- Test set built from real queries (chats, email, tickets) with personal data removed
- Scoring criteria: accuracy, tone, completeness, correct refusals
- LLM-as-judge scoring (a second model grades answers against criteria) with manual spot checks
- Prompt injection and jailbreak testing
- Guardrails: topics, format, prices, personal data, human handoff
- Automated regression tests for every change
- Fine-tuning of a smaller model on your examples, if justified
- Report with numbers and a fix plan
- Tests and scripts delivered to your repository
Choose the scope
Use a package to describe what you need. We agree on the quote and schedule after reviewing your brief and requirements.
01
Answer audit
You have a live bot and need an objective assessment
- Test set of up to 100 queries
- Criteria and LLM judge
- Manual check of a sample
- Report with failures and a fix plan
02
Evals and guardrails
The bot talks to customers and risk must come down
- Test set of up to 300 queries
- Jailbreak and injection testing
- Guardrails and human handoff
- Automated regression tests
- Re-measurement after fixes
03
Model fine-tuning
Prompts and retrieval are not enough; you need consistent style or format
- Preparing and cleaning training examples
- Fine-tuning and comparison with the base model
- Evaluation on a held-out set the model has not seen
- Update instructions
How we work
Brief and requirements
You describe the task and current workflow. We clarify requirements, choose a first stage and prepare a quote.
Test set
We collect queries from your data and strip personal information. You approve the set.
Baseline measurement
We run the bot through the tests and record the starting KPI: share of correct answers and error types.
Fixes
We adjust the prompt and knowledge base and add guardrails. If justified, we prepare fine-tuning.
Re-measure and handover
We compare against the baseline and hand over tests and access. Payment is in stages.
Scope and limits
- We don't promise zero errors: AI answers are probabilistic, so we reduce risk and show it in numbers
- We don't fine-tune without need: fixing the prompt or knowledge base is often cheaper and better
- We can't work without your data: we need real queries and correct answers from your experts
FAQ
What do I need to provide?
Access to the bot or a description of it, examples of real conversations, and at least a few dozen reference answers from your specialists. We remove personal data before work starts.
How do you measure quality?
Against criteria we agree with you: accuracy, completeness, tone, correct refusals. An LLM judge scores the answers and a human checks a sample so the judge itself stays honest.
When is fine-tuning worth it?
When you need a consistent format or style and prompts plus retrieval do not get there. For new facts it usually does not fit; those are better served from a knowledge base.
Do you work with OpenAI, Anthropic, Google models?
Yes, and with open models. The choice depends on the task, data requirements and budget. Not every model supports fine-tuning, and each has limits.
Is our data safe?
We agree on data handling up front. All accounts, test sets and files belong to you under the agreed terms. Sensitive data can be replaced with synthetic examples.
What if the KPI is not reached?
The pilot has a measurable KPI fixed before we start. If the numbers don't improve, you see why and decide whether to continue. Payment is in stages, so your exposure is limited.
Start with your task
Describe your business, the outcome you need and your current workflow. We will clarify the requirements and prepare a quote after your brief.