Your knowledge. More ways to use it.
Build around your engineering and service teams.
Start with one defined need and a collection of relevant documents. Evaluate the experience against real questions before adding more users or content.
One valuable workflow. Clear quality standards.
Technical knowledge
Manuals, service documentation and engineering records can be difficult to navigate together. Build a defined knowledge-search workflow with Haystack Enterprise Platform, then test it on the questions your engineering or service team actually asks.
Checkable answers
When your team asks a technical question, the supporting document matters. Use Haystack Enterprise Platform to retrieve relevant documentation and show references alongside responses, so engineers can check the source before acting.
Model choice
A document lookup and a complex technical question may need different model choices. With Haystack Enterprise Platform, compare supported models on your own examples and choose the configuration that meets the task’s quality and cost requirements.
Build new experiences. Keep your choices.
Your knowledge. Checkable AI answers.
Connect retrieval to a defined task. Show supporting references so your team can inspect what a response is based on. References help review; they do not guarantee correctness.
Define the collection
Choose the documents, users and task boundaries for this application.
Inspect supporting sources
Inspect responses and evidence against the standards your team sets.
Evaluate real questions
Test representative examples and failure cases before wider use.
Your workflow. Your AI choices.
Configure models and deployment for your application. Review the full data flow and connected services against your requirements. Deployment choice alone does not establish compliance.
Review connected systems
Assess authentication, document access and service integrations.
Compare supported models
Evaluate supported models against the outputs your team needs.
Assess deployment options
Review hosting, model calls and data flows against your infrastructure requirements.
Scale your AI. Keep quality and cost in view.
Compare configurations on the same task using shared acceptance criteria. Assess quality, usage, retries and total operating cost before expanding.
Compare like for like
Use the same questions and acceptance criteria for every configuration.
Measure the full task
Include retrieval, model usage, retries, review and infrastructure costs.
Review before expanding
Choose a configuration when it meets the quality standards for the task.
