Guided Question Parsing
AI helps interpret phrasing but always maps to a known metric and template.
Enterprise Analytics Copilot MVP
How an enterprise innovation team tested an AI analytics copilot that sits on top of existing BI tools.
Enterprise AI
Enterprise innovation labs, Analytics leaders, Data platform teams
$30k–$55k
Enterprise Analytics & BI
Industry
AI MVP
App Type
4 weeks
Timeline
Web
Platforms
Project Overview
1
What We Built
- A copilot MVP that exposes a curated metric dictionary, translates natural-language questions into safe queries against the warehouse, and returns charts plus narratives.
- Business stakeholders wanted answers quickly without relying on analysts for every question; analysts wanted to scale their impact.
- Ideal for: Large orgs with mature data stacks but low self-serve analytics adoption, Innovation labs exploring AI copilots over BI, Data teams tired of ad-hoc dashboard and query requests
2
The Challenge
- Enterprises invest heavily in data platforms and BI tools, but most business users still rely on screenshots and spreadsheets.
- Confusing metric names and dashboards
- Slow turnaround on ad-hoc questions
- Fear of ‘breaking’ things in BI tools
3
Our Solution
- Limit the MVP to a handful of curated metrics and subject areas, with a semantic layer and strict templates for queries.
- No direct SQL from the model; only from vetted templates
- Curate a metric dictionary with owners and definitions
- Log all questions, queries, and answers for review and improvement
4
Results & Impact
- Business users in the pilot group were able to answer many of their own questions without waiting on analysts, while data teams retained control over logic and governance.
- Proves that an analytics copilot can add value without undermining data governance
- Creates a pattern for rolling AI over existing BI investments
- Builds excitement and momentum for broader AI adoption in the enterprise
Key Features & Scalability
Essential features and scalability measures that make this MVP powerful, user-friendly, and ready to grow.
Narrative Explanations
Plain-language descriptions of what charts show and what might be driving changes.
Usage & Trust Metrics
Track adoption, satisfaction, and trust over time.
Configured for one domain and small user group, but structured to expand to many more.
Initial infrastructure setup designed for growth and expansion.
Scaling Strategy
Add more domains and metric packs under governance, Offer copilot in additional channels like chat tools
Built for bursts of interactive query sessions from business users.
Traffic handling capability for high-volume concurrent users.
How We Built It
Our step-by-step development process from concept to deployment, ensuring quality and efficiency at every stage.
01
Domain & Metric Selection
Chose a narrow domain (e.g., product usage or revenue) and a small metric set.
02
Semantic Layer & Templates
Defined metrics and query templates that the copilot could safely use.
03
Pilot & Governance
Launched within a small group and set up governance for iterating safely.
04
Design System
Professional visuals aligned with corporate branding.
05
Wireframes
Enterprise-ready interface that feels familiar to BI users.
06
Handoff Process
Close partnership with data and security teams.
Core Product Modules
1
User App
- Ask Analytics Interface
Chat-like interface where users can ask questions about key metrics and receive charts plus explanations.
- Metric Browser
A browsable catalog of available metrics with definitions and example questions.
2
Admin Panel
- Metric & Template Management
Define metrics, their owners, and query templates the copilot is allowed to use.
- Question & Feedback Review
See what users ask, how the copilot responded, and collect feedback to improve.
Technology Stack
We use modern tools to build AI apps that grow with you. We pick the best tools for each project, like React, Next.js, Python, and Go.
Performance & Security
Built with enterprise-grade optimization and security measures to ensure fast, reliable, and secure operation.
Frontend Performance
Optimized chart rendering, Graceful handling of long-running queries
Backend Performance
Query caching, Rate limiting for expensive queries
Database Performance
Minimal state in the app; rely on warehouse, Indexes on any local logs
Authentication
Enterprise SSO and role-based authorization tied into existing identity systems.
Data Protection
Rely on existing warehouse security controls, Encrypt any local configuration and logs
Security Best Practices
No broad access outside existing data entitlements, Complete logging of queries and results for compliance
Performance & Security
Built with enterprise-grade optimization and security measures to ensure fast, reliable, and secure operation.
Frontend Performance
Optimized chart rendering, Graceful handling of long-running queries
Backend Performance
Query caching, Rate limiting for expensive queries
Database Performance
Minimal state in the app; rely on warehouse, Indexes on any local logs
Authentication
Enterprise SSO and role-based authorization tied into existing identity systems.
Data Protection
Rely on existing warehouse security controls, Encrypt any local configuration and logs
Security Best Practices
No broad access outside existing data entitlements, Complete logging of queries and results for compliance
Project Timeline
1
Week 1 – Domain & Governance
1 week
- metric dictionary
- governance model
- MVP spec
2
Week 2–3 – Build & Integrate
2 weeks
- ask interface
- metric browser
- warehouse integration
3
Week 4 – Pilot & Evaluation
1 week
- limited pilot
- user feedback
- next-steps roadmap
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