VividCloud rapidly develops custom AI solutions for any industry use case leveraging the accelerator.
Using our proprietary accelerator as a starting point, VividCloud is able to tackle just about any use case in any industry to develop custom AI solutions quickly. The client specifies their AI wishes and VividCloud develops the precise solution satisfying the requirements. The client owns all the IP. No product costs and no royalties.
At the center of the solution is a Planner agent that orchestrates a team of specialized agents in real time — querying operational data, calling external systems, searching your document library, and building visualizations — before composing a single, cited answer. Every step streams to the screen as it happens, so the reasoning is visible, not a black box.
Ask it in chat. Call it from your systems. Same agents, same answers.
$ curl -X POST /query/stream \ -H "Authorization: Bearer $TOKEN" \ -d '{"input":{"input":"anomaly, zone 4?"}}' event: agent_progress data: {"agent":"elasticsearch"} event: answer data: {"text":"14 stoppages… inspect dock C."}
HOW IT WORKS
A single Planner reads every request and decides, step by step, which specialist agent to call next. Each agent’s result feeds back into a shared context, and the loop keeps going until the answer is complete or a genuinely ambiguous question needs a clarifying reply instead.
THE AGENTS
Every request is handled by whichever combination of these actually answers it, never guesswork about which one applies.
Writes, executes, and self-repairs queries against your events, telemetry, or logs, then explains the results in plain language.
Auto-discovers a REST API’s OpenAPI spec, plans a call, executes it, and repairs failed requests with corrected parameters.
Hybrid vector + keyword search across your document library, reranked, cited, and automatically re-searched when the first pass falls short.
Queries the data, drafts a chart, renders it, looks at the result, and redraws it until the chart actually communicates the answer.
Extracts durable facts from every conversation and queues them for expert review before they inform the next answer.
Reformulates the question, selects the next agent, and decides when the answer is complete — the loop diagrammed above.
TWO WAYS IN
Both paths run the same Planner and the same agents. Nothing about the platform assumes a human typed the question.
Analysts, adjusters, and floor supervisors ask follow-up questions, watch the plan unfold step by step, edit or branch a message, and pin any chart straight to a dashboard.
Built for back-and-forth: the interface streams reasoning as it happens, not just a final answer.
Any authenticated service posts a question to POST /query/stream and gets the same agent reasoning back as a stream of events — no browser, no session, no person in the loop.
POST /query/stream
This already runs in production-shaped code today: the chart-alerts worker evaluates dashboard thresholds on a schedule with zero UI involved.
BUILT TO SPECIALIZE
A domain configuration — schema context, example queries, instructions — is swapped in per vertical without touching agent code. Two examples of what that looks like in practice.
The robot_fleet domain — live, configured, running.
A floor supervisor asks “What’s the robot downtime this week?” in chat. Fact lookup supplies known patterns, the data agent queries the robot-events index, and the answer comes back with stoppage counts and a recommendation.
A fleet-monitoring service posts “any anomalous stoppage pattern in zone 4?” to the same endpoint every 15 minutes. No person involved — the answer triggers a reroute or opens a maintenance ticket automatically.
Point the domain system at a claims index and a policy library, and this is the shape you get.
An adjuster asks “Do this claimant’s last three claims match their loss history?” The knowledge agent cites underwriting guidelines, the data agent pulls claims history, and the answer cites both.
The claims-intake system calls the same endpoint the moment a First Notice of Loss is filed, before an adjuster opens the file, and routes standard processing versus fraud review off the response.
FOUNDATION
Amazon Bedrock for reasoning, Cognito for authentication, Aurora with pgvector for the knowledge base, Elasticsearch/OpenSearch for operational data, DynamoDB for chat history, and ECS/Fargate plus Lambda for compute — all provisioned through CDK.
Solution Brief
Frequently Asked Questions
AWS Architecture
Contact us to discover how we can help safeguard your digital assets while driving business efficiency.
With VividCloud, you get ingenuity on demand to solve your most pressing cloud software engineering challenges. Drop us a line to begin the conversation — we can’t wait to hear from you.
"*" indicates required fields
Δ