vyastiInformation without ownership
If no one knows which source is current, an answer can look convincing and still be wrong.
AI PRODUCT ENGINEERING / VYASTI
Build a conversational assistant that knows which sources to trust, which actions are permitted and when a human should take over. Useful answers start with a system you can verify.
User asks a question
Check approved knowledge
Answer or hand over
THE PROBLEM / NOT THE PITCH
Fluent conversations are not the same as reliable support. An assistant can invent an answer, retrieve the wrong information or keep a customer in a loop with no way out.
Before selecting a model or a platform, we map the content, people, permissions and situations that can go wrong.
If no one knows which source is current, an answer can look convincing and still be wrong.
A bot that cannot recognize uncertainty or hand off appropriately creates more work for users and teams.
Connected tools require explicit access rules, confirmation steps and a reviewable record of what happened.
INTERACTIVE WORKSTREAM EXPLORER
Choose a workstream. See the decision, the handover and the test that should make it accountable.
Choose eligible content, decide how fresh it must be and identify who maintains every source.
Which sources may the assistant cite?
FROM CONSTRAINT TO RELEASE
Set approved documents, content owners, access limits and review dates.
Design clear replies, clarifying questions, refusal behaviour and handoffs.
Decide which tools can be used, by whom and with what confirmation.
Review factuality, privacy, recovery, accessibility and failure handling.
THE CASES THAT MATTER MOST
No honest team should promise zero hallucinations or completely autonomous support. The design must handle uncertainty as carefully as the ideal conversation.
SCOPE / HANDOVER / ACCOUNTABILITY
The final package depends on discovery, platform constraints and the signed scope. Each agreed output should have an owner, a review method and a clear acceptance check.
No implementation, integration, support period or launch result is presumed before the scope is agreed.
Common requests, uncertainty, clarifying questions and handoff responsibilities.
Approved sources, freshness expectations and content ownership.
Read/write limits, role checks and required confirmations.
Test cases, reviewed results, exclusions and operating responsibilities.
FOUR EXAMPLES OF RELEASE EVIDENCE
INTERACTIVE ACCEPTANCE EXERCISE
Select a use case and a risk to see an illustrative evaluation plan. This is a local planning exercise, not a production AI test.
Conceptual example only. This does not query a model or connect to accounts, datasets, CRMs or support systems.
EXAMPLE / RELEASE GATE
The answer must reference approved evidence. Otherwise it should state uncertainty and offer an escalation route.
Test question, expected fallback, observed response and reviewer decision.
BEFORE YOU BUILD
When there is a recurring task or customer need that a conversational interface can improve, and a team can maintain reliable knowledge and a human handoff. Discovery should also check whether a simpler search, form or support workflow would work better.
BEFORE YOU BUILD
When there is a recurring task or customer need that a conversational interface can improve, and a team can maintain reliable knowledge and a human handoff. Discovery should also check whether a simpler search, form or support workflow would work better.
Potentially. The first step is to identify approved sources, ownership, access levels, update frequency and which information must never be returned. Retrieval and answer behaviour are then tested against those boundaries.
Integrations may be possible after reviewing the platform, available APIs, permissions and operational risk. Any action that changes a record or contacts a person should have scoped authorization and appropriate confirmation.
By designing role-based access, limiting what can be retrieved or changed, defining retention and logging rules, and testing for cross-user disclosure. Specific security and compliance requirements must be agreed for the project.
A representative test set should cover approved facts, missing evidence, incorrect requests, escalation, privacy, tool actions, latency and accessibility. Reviewers compare observed responses against agreed acceptance criteria.
The agreed scope can include conversation flows, source policy, integration permissions, test cases, operating guidance and escalation ownership. Exact deliverables and post-launch support are defined before work begins.
No. AI systems can make mistakes. The responsible goal is to reduce risk through grounded information, bounded actions, evaluation, monitoring and a clear route to a human when needed.
BETTER ANSWERS START WITH BOUNDARIES
Bring the questions customers ask, the content your team trusts and the places an answer must stop. We'll help shape the scope, permissions and tests before you commit to a build.
Approved knowledge. Defined permissions. A human route when needed.
