InVocIQNatural-language & voice BI
Voice-driven AI BI. Speak the question. See the answer.
InVocIQ is a voice-activated business intelligence platform that lets anyone query live enterprise data in natural language. Speak a question and a seven-stage pipeline — transcription, intent extraction, semantic-layer compilation, governed SQL execution, and visualization — returns the answer, with citations, in seconds. Because every query runs through your semantic layer and warehouse-level security, metrics stay consistent and access stays controlled.
Capabilities
What InVocIQ does.
Multi-ASR speech input
OpenAI Whisper, Deepgram, Amazon Transcribe, and Google Speech-to-Text — with Indian-English support and on-device voice-activity detection to trim silence.
Semantic-layer integration
Works with dbt MetricFlow, Cube, AtScale, and Looker LookML so every answer uses canonical metric definitions.
Multi-warehouse execution
Runs governed SQL against Snowflake, BigQuery, Databricks, Redshift, and Postgres.
Automatic visualization
LLM-driven chart selection renders the right Plotly or Apache Superset visual for the question.
Optional voice response
Spoken answers via Amazon Polly, ElevenLabs, or Google Cloud TTS.
Citations and SQL transparency
Every answer is auditable — see the generated SQL and the sources behind the number.
How it works
7 stages, one accountable loop.
- 1
Capture
Voice capture in the browser or on mobile, with on-device VAD.
- 2
Transcribe
Speech-to-text via your chosen ASR provider.
- 3
Understand
Intent and entity extraction via LLM.
- 4
Compile
The request compiles against your semantic layer.
- 5
Execute
Governed SQL runs on the warehouse with row- and column-level security.
- 6
Visualize
Intelligent visualization generation picks the right chart.
- 7
Respond
Optional spoken response reads the answer back.
Benefits
Why teams choose it
- Metric consistency — canonical definitions prevent department-level disagreements
- Join correctness — the semantic layer prevents erroneous table relationships
- Authorization — row- and column-level security enforced at the warehouse
- Vendor agnostic — a BYOM alternative to Tableau Pulse, Power BI Copilot, and ThoughtSpot
- Data-residency control for regulated workloads
Use cases
Where it fits
- Defined metrics over defined dimensions — “revenue by region last quarter”
- Time-window comparisons — month-over-month, year-over-year
- Top-N and bottom-N queries
- Drill-down within modeled metrics
Integrations
How it compares
A vendor-agnostic alternative
vs. Tableau Pulse
InVocIQ adds a voice-first interface and a bring-your-own-model backend, so analytics aren’t locked into a single vendor’s cloud.
vs. Power BI Copilot
Answers compile against your own semantic layer (dbt MetricFlow, Cube, LookML) with warehouse-enforced security — not tied to the Microsoft fabric.
vs. ThoughtSpot
Natural-language and voice queries return transparent, cited SQL — there’s no proprietary search index to license and maintain.
vs. Amazon Q in QuickSight
Warehouse-agnostic across Snowflake, BigQuery, Databricks, Redshift, and Postgres, with data-residency control for regulated workloads.
What is voice BI?
Voice BI — voice-driven business intelligence — lets anyone ask an analytical question out loud, such as “what was revenue by region last quarter?”, and get a charted, cited answer in seconds. It removes the dashboard-and-filter learning curve that keeps most employees from self-serving data. InVocIQ pairs production-grade speech recognition with a governed semantic layer, so a spoken question becomes governed SQL and the numbers always match the rest of the business. The result is conversational analytics that executives, sellers, and operators can use without writing a query or waiting on an analyst.
A vendor-agnostic alternative to Tableau Pulse, Power BI Copilot, and ThoughtSpot
Most natural-language BI tools are tied to one vendor’s cloud, semantic model, and language model. InVocIQ is bring-your-own-model (BYOM) and bring-your-own-warehouse: it plugs into the semantic layer you already maintain (dbt MetricFlow, Cube, AtScale, or Looker LookML) and runs governed SQL against Snowflake, BigQuery, Databricks, Redshift, or Postgres. Canonical metric definitions prevent department-level disagreements, the semantic layer prevents incorrect joins, and row- and column-level security is enforced at the warehouse. For regulated workloads, data-residency control keeps queries inside your boundary. That makes InVocIQ a practical alternative when Tableau Pulse, Power BI Copilot, or ThoughtSpot would force a stack you don’t want to standardize on.
Where natural-language BI fits — and where it doesn’t
InVocIQ is strongest on defined metrics over modeled dimensions: “revenue by region last quarter,” month-over-month and year-over-year comparisons, top-N and bottom-N rankings, and drill-downs within modeled metrics. It deliberately does not guess on free-form analysis over un-modeled data, statistical inference, or forecasting — it defers to notebooks and your existing models instead of inventing an answer. Every response ships with the generated SQL and its sources, so analysts can audit exactly how a number was produced.
FAQ
Common questions
Is InVocIQ an alternative to Tableau Pulse or Power BI Copilot?+
Yes. InVocIQ is a vendor-agnostic, bring-your-own-model alternative to Tableau Pulse, Power BI Copilot, and ThoughtSpot. It works with your existing semantic layer and data warehouse instead of locking you into one vendor’s cloud, and enforces security at the warehouse.
How does InVocIQ keep metrics consistent?+
Every query compiles against your semantic layer (dbt MetricFlow, Cube, AtScale, or LookML), so answers use canonical metric definitions instead of ad-hoc SQL.
Is it secure for regulated data?+
Yes. Row-level and column-level security are enforced at the warehouse, and data-residency control supports regulated workloads.
What is it not built for?+
It excels at defined metrics over modeled dimensions. For free-form analysis on un-modeled data, statistical inference, or forecasting, it defers to notebooks and existing models rather than guessing.
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