Service
AI Analytics Dashboards & Business Intelligence
Waiting on someone to pull a report before you can decide?
VelvoAI consolidates the numbers scattered across your systems into one live view, then adds a layer that explains what moved and answers plain-language questions. The generated query is shown with every answer, so a number can be checked rather than believed.
Built by VelvoAI, an independent AI systems studio. One engineer scopes and writes every system.
What the system does
- Data freshness
- Live
- Source of truth
- One
- In plain language
- Ask
- ComponentNext.js
- ComponentPostgres
- ComponentPython
- ComponentOpenAI
- ComponentRecharts
The problem
Why this keeps costing you
The data exists. It is just in six places, in incompatible shapes, and assembling it into a decision takes a half-day of spreadsheet work. By the time the report lands it describes a situation that has already changed, so decisions get made on instinct instead.
What we build
Inside an AI analytics dashboard we ship
- Connectors pulling from CRM, billing, ads, support, product, and operational databases
- A single semantic layer so a metric means the same thing everywhere it appears
- Live executive dashboards with drill-down to the underlying records
- Natural-language querying for ad-hoc questions without SQL
- Anomaly detection that flags unusual movement rather than waiting to be asked
- Scheduled digests into email or Slack with plain-language commentary on what changed
How we approach it
The part most implementations skip
We define the metrics before we build anything, because most dashboard projects fail on disagreement about what a number means rather than on engineering. Once definitions are agreed, we pull data continuously, model it once, and expose both a fixed executive view and an ad-hoc question interface.
Deliverables
What you actually receive
Every VelvoAI engagement ends with working software you own, not a slide deck. Here is what ships with an AI analytics dashboard.
How a build runs, step by step- D.01Agreed metric definitions documented and version-controlled
- D.02Data pipeline with freshness monitoring and failure alerts
- D.03Dashboard covering the decisions you actually make weekly
- D.04Natural-language query interface over the modelled data
- D.05Scheduled digest configuration per team
By industry
AI Analytics Dashboards in your sector
AI Analytics Dashboards, applied to the workflows and rules of the sectors we work with most.
- 01
AI Analytics Dashboards for E-commerce
Your true margin lives across six systems and one exhausted spreadsheet.
- 02
AI Analytics Dashboards for SaaS
Everyone quotes a different number for the same metric.
- 03
AI Analytics Dashboards for Logistics
Operational performance is only visible after the month has closed.
- 04
AI Analytics Dashboards for Financial Services
Reporting packs assembled by hand are a deadline risk every period.
Common Questions
01How is this different from a standard BI tool?
The dashboard layer is similar. The difference is the work underneath — unifying systems that do not agree with each other — plus a question interface for people who will never write a query, and commentary that explains movement rather than just plotting it.
02How fresh is the data?
Configurable per source. Operational metrics usually run near real-time; anything pulled from a rate-limited third-party API syncs on a schedule. Every panel shows its own last-updated timestamp so nobody acts on stale numbers unknowingly.
03Can it work with our existing warehouse?
Yes, and that is usually the better starting point. If you already have BigQuery, Snowflake, or Postgres, we build on top of it rather than duplicating the pipeline.
Related services
Often built alongside this
- Written by
- Usman Ali, Founder
- Last reviewed
Next step
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