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Aqeeq Technologies

Natural language SQL analytics for retail leadership

Services

  • Data Engineering
  • NLP
  • AI

Technology

  • AI
  • NLP
  • SQL
Sales Analytics AI

Client

TwinFusion

TwinFusion leadership queries sales performance in plain language against governed SQL metrics — an AI and NLP dashboard for retail decision-making without waiting on analyst queues.

Sales Analytics AI

Project

Retail leadership needed sales answers in plain language — without exporting CSVs or waiting on analysts.

The engagement delivered an AI and NLP SQL dashboard over curated retail metrics.

Tasks

  • Define metric catalog and row-level security rules
  • Build NL-to-SQL with guardrails and query explainability
  • Ship executive dashboard with saved questions
  • Document data dictionary for self-serve expansion

KPIs

  1. 1. Same-day insights — leadership queries without analyst queue
  2. 2. Governed access — every answer respects role-based filters
  3. 3. Adoption — weekly active leadership users within 30 days

Project journey

  1. 1. Metric design

    Workshops with finance and merchandising on canonical definitions.

  2. 2. Assistant build

    Guardrailed NL interface over the metric warehouse.

  3. 3. Enablement

    Training and playbook for expanding question types safely.

Challenges

Retail leaders needed answers in plain language but were blocked by BI queues and raw SQL. Exporting CSVs produced stale snapshots and inconsistent metric definitions across teams.

  • Analyst bottlenecks delayed leadership decisions waiting on ad-hoc report requests.
  • Ungoverned SQL risked incorrect answers without row-level and metric controls.
  • CSV culture created conflicting numbers across departments.
  • Low self-serve adoption when tools required technical query skills leaders did not have.
Sales Analytics AI

Results

  • Same-day leadership insights without analyst queues
  • Governed answers that respect role-based filters
  • Consistent metric definitions across teams
  • Higher adoption of self-serve analytics by leadership
  • Safer expansion of question types over time

Production

Grade delivery

Phased

Rollout model

Operated

Post-launch support

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