Most support conversations are routing and retrieval. Test right-sized models for those steps against your evals, and keep the harder answers where they pass.
Prefer email? hello@azitalabs.com, subject “Workload evaluation: Chatbots & support AI”
When a customer asks a basic question, your chatbot pipeline runs several steps: intent classification, entity extraction, context retrieval and response generation. Today every step runs on a frontier model, even simple helpdesk routing. Resolution quality has to hold as ticket volume grows, and you cannot tune the endpoints you rent.
An example workflow
Most tickets are account and billing questions answered from the help centre. An evaluation could run a sample of resolved tickets through right-sized models and compare resolution quality, latency and total cost.
An example workflow
Order status and returns questions spike during sales. An evaluation could replay a week of conversations, check the answers against the order system, and compare accuracy and total cost with the current provider.
An example workflow
Answers must follow approved wording and stay in the country. An evaluation could run the approved FAQ set where the firm's rules allow and compare how closely answers follow the wording, and what they would cost.
Every step is judged against your conversation evals. A step that does not pass stays where it is.
Intent, extraction and retrieval steps are matched to models sized to them, and harder answers stay on the model that passes.
Each step's total cost compared with what you pay today, before you switch.
Run your workload on capacity from Azita’s installations. Scope the workload and data requirements.
Discuss your workload on the fleetScope the room and workload together. Azita designs, installs and commissions the deployment.
Discuss an installation in your buildingWhere the work runs, which models are used, who has access and how long anything is kept are agreed in your proposal before any work starts. The Trust page sets out custody and residency by side.
Agree a representative job and the acceptance criteria. Compare the output, turnaround, data requirements and total cost before deciding.
Available to evaluate on your own workload.
On the Azita fleet, or on an installation in your building.
We'll review what you need to run and contact you to discuss the right setup.
There is no price list. Your proposal states what is being quoted, the commitment and what is included. Human review, software licences and integration are listed wherever they apply.
Add what you pay for today. It travels with your enquiry as a note, so the comparison starts from the same work.
Prefer to read first? How pricing works
Your evals decide. We run real conversations with known answers through right-sized models, and your support lead reviews the results. A step that does not pass stays where it is.
Send your chatbot pipeline with its eval set and last month's usage. We agree the pass criteria, run the suite on both stacks, and set out pass rates, latency and total cost for each step.
Your evals run in CI, and you cut over step by step behind your own flags. Any step that does not pass on the new stack stays with your current provider.
On the Azita fleet, or on an installation in your own building. Where your chat logs are processed, who has access and how long anything is kept are agreed in your proposal before any work starts.
Proposal based on your workload and deployment. It states what is being quoted, the commitment and what is included, and lists human review, software licences and integration wherever they apply. There is no price list.
Term, what is included, exit and data handling at the end are set out in your agreement before you sign.
Related
Engineering and product
Agents, support, extraction and product features, tested on your own evals.
The page for every kind of work in this group, and how an evaluation works.
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Also for
BPO & call centres
Transcripts, summaries and quality scores from your own call recordings. Compare accuracy, turnaround and total cost before you switch.
Read
Also for
SaaS product
Routing and extraction steps behind your AI features on right-sized models, tested on your evals. Compare pass rates, latency and total cost first.
Read
A production workflow and its eval set. We agree the pass criteria, then compare output, latency and total cost on the same input.
Arrange a workload evaluation