Awishcar.com

PORTFOLIO — BUSINESS DASHBOARD

AI Product Monitoring Dashboard

LLMOps + FinOps + Reliability in one decision-ready view. Modern AI features don’t fail loudly—they degrade: latency creeps up, error rates spike by model/region, costs balloon, and quality regressions hide behind stable uptime. Awishcar gives product, engineering, and operations teams a single view across reliability, performance, cost, and AI quality—from executive KPIs down to tenant- and endpoint-level root cause.

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A live look at the dashboard

What this product helps you do

  • Run AI in production with confidence — p50/p95/p99 latency, error rate, retries/fallbacks, queue depth, and concurrency, broken down by model, region, version, plan, and tenant
  • Control costs with FinOps visibility — daily spend, cost per 1K requests, token consumption, GPU utilization, and top tenants by spend
  • Detect quality regressions and safety risk — thumbs-up rate, task success by version, safety flag volumes, hallucination proxy rate, and embedding drift
  • Go from dashboard to action fast — drill into the most expensive endpoints, top error types, and tenant-specific issues to protect SLAs

What you’ll see in the dashboard

Executive KPIs (at-a-glance)

A clean, always-on top row designed for leadership and on-call:

  • Requests/min and active tenants
  • Error rate; p50/p95/p99 latency
  • Cost today + cost per 1K requests
  • Input/output tokens today and average context length
  • Safety flags today

Reliability & Performance (LLMOps / SRE)

Pinpoint where performance is degrading:

  • Latency by model and by region (p95 trends)
  • Error rate by type (timeouts, upstream 5xx, rate limits)
  • Retry and fallback rates (gateway-level resilience)
  • Queue depth & concurrency (capacity indicators)
  • Hour-of-day latency heatmaps

Cost & Capacity (FinOps)

Connect usage to spend and infrastructure:

  • Token consumption by model/provider
  • Cost by model/day and cost trends
  • GPU utilization by region (self-hosted or hybrid inference)
  • Top tenants by spend (cost drivers)
  • Unit economics: cost per request & cost per success

Quality & ML Monitoring

Move beyond uptime into product outcomes:

  • User feedback score / thumbs-up rate trends
  • Task success rate by version (regression & rollout safety)
  • Safety flags by type (policy, PII, jailbreak attempts)
  • Hallucination proxy rate (guardrail effectiveness)
  • Embedding drift & novelty score (RAG/embedding stability)
  • Prompt topic distribution

Tenant / Customer Drilldown

Instantly isolate a specific customer’s issues:

  • Tenant daily spend, request volume, and token volume (30-day view)
  • Tenant error rate and p95 latency
  • Top error types for that tenant
  • Most expensive endpoints and call volumes

Who it’s for

  • Product teams shipping AI features needing quality + adoption signals
  • Engineering / Platform teams running inference, gateways, and RAG pipelines
  • SRE / On-call teams managing latency, error budgets, and incident response
  • FinOps / Leadership teams tracking spend, unit economics, and profitability

Potential data sources we can integrate

Observability & telemetry

  • OpenTelemetry (OTel) traces/metrics/logs
  • Prometheus metrics (service + infra + GPU exporters)
  • Grafana visualization layer
  • Loki / ELK log aggregation
  • Jaeger / Tempo distributed tracing

AI/LLM and model serving

  • Provider APIs & logs: OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock
  • Model gateways: Kong, Envoy, NGINX, custom/LLM routing layers
  • Self-hosted inference: vLLM, TGI, Triton, Ray Serve, Kubernetes serving
  • GPU telemetry: NVIDIA DCGM exporter, node/pod metrics, autoscaling signals

Application & product analytics

  • App events: Segment, RudderStack, Snowplow, Amplitude/Mixpanel
  • Feature flags & releases: LaunchDarkly, ConfigCat, custom rollout metadata
  • User feedback: in-app ratings, thumbs up/down, human QA, annotation tools

Data platforms & business systems

  • Data warehouses/lakes: Snowflake, BigQuery, Redshift, Databricks, S3/ADLS/GCS
  • Streaming: Kafka, Kinesis, Pub/Sub
  • Billing/usage: Stripe, Chargebee, cloud cost exports (AWS CUR, Azure, GCP)
  • Support & CRM: Zendesk, Intercom, HubSpot/Salesforce

RAG / knowledge systems

  • Vector DBs: Pinecone, Weaviate, Milvus, pgvector, OpenSearch vector
  • Knowledge sources: Confluence, Google Drive, SharePoint, Notion, internal docs
  • Search: OpenSearch/Elasticsearch for hybrid retrieval analytics

Reduce incidents. Improve latency. Control spend. Operate safer AI.

A single dashboard and a data model designed for multi-tenant AI products. Talk to us to get started.

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Tell us about your data stack and what you want to measure. We’ll show you how a tailored dashboard would work for your team.