THE AGENTIC BI ANALYST · BUILDS YOUR CONTEXT GRAPH
Your Concierge Analyst that learns · composes · compounds with your business.
Reads your warehouse, dbt models, and every SQL your team has ever run. Composes verified plans from your validated Segments and Calculations — same plan, same SQL, every time.
Your schema, your queries, your team's vocabulary.
Measures, segments, hierarchies — not column names.
Every answer arrives with a verified plan. No black box.
The Problem · Self-service BI is an illusion of competence.
Every existing fix hits the same wall. Four approaches, all incomplete — because none of them carries memory of your business.
| Approach | What it does | Why it fails |
|---|---|---|
| Dashboards | Pre-built views | Break on novel questions. Don't scale to ad-hoc demand. |
| SQL Notebooks | Flexible per-analyst work | Knowledge stays in notebooks nobody reads. Walks out with the analyst. |
| Text-to-SQL | Natural language → SQL | Generates from scratch. No reuse. No memory. Different SQL Mon vs Tue. |
| ChatGPT · Claude | Schema paste into a chat | Brilliant. Stateless. No memory of your business. |
LLMs have solved natural language. The missing layer is a Context Graph — the structured record of how your business actually reasons about its data.
What We Deliver · A Context Graph, constructed from your business.
Not a chat tool, not a chart tool. A structured representation of how your business reasons about data — in three layers.
Your Data
Schema, joins, profiles, data quality. Read once, enriched continuously. The system understands the shape of your warehouse before any question gets asked.
Your Logic
Measures, segments, hierarchies, vocabulary — captured as first-class business primitives. The things your team already says, promoted to the system's native nouns: Segments, Calculations, Analysis Patterns.
Your History
Past queries, validated answers, team decisions — persisted as reusable assets. Every accepted plan adds to the graph; the institutional knowledge stops walking out the door.
The Context Graph is yours alone. Portable. Sovereign. Growing.
How It Works · Understand. Interpret. Compound.
Three steps under the hood. Each one is a checkpoint — none of them is a black box.
Understand — Reads your warehouse.
Enriches schema, ingests prior SQL, encodes your team's vocabulary. The Context Graph is seeded before any question is asked. Existing investments — LookML, dbt metrics, Power BI DAX — are absorbed, not replaced.
Interpret — Compiles to BI Algebra.
When a user asks "Which stores are losing habitual buying customers?", the LLM proposes a symbolic plan over your Segments, Calculations, and Analysis Patterns. The system accepts the plan only if the algebra closes — every concept resolves, every Calculation grounded. Ambiguous? Refuse. Plan Card visible before any SQL runs.
Same plan → same SQL. Math, not probability.
Compound — Persists every verified answer.
Every accepted plan persists back into your Context Graph — adding Segments, Calculations, and Analysis Patterns your team has validated. The graph gets denser with use. Future questions answer faster because the search has more to compose from.
The 100th question is faster than the 10th.
Why We Win · Three pillars. All three are required.
| Pillar | What it does |
|---|---|
| 01 · Your Context Graph | Built from your schema, your queries, your BI assets. Compounds every time you use it. The asset is the moat — and the moat is yours. |
| 02 · Algebraic Verification | No probabilistic guessing. Ambiguous questions are refused. Data-quality issues are surfaced without being asked. Every executed query has a verified plan attached: audit trail by construction. |
| 03 · Backend-Agnostic | LLM is interchangeable. Plan is the substrate. Tested today on OpenAI and Anthropic; run on your enterprise contract. Swap GPT, Claude, or an open-weight model — the plans still close, the SQL still compiles. |
Each pillar exists elsewhere in pieces. The hard part is all three operating together in one workflow.
“This is not the LLM's answer. This is the verified plan the LLM proposed, that the system accepted only because the algebra closed.”
What Compounds · A tool on Day 1. Infrastructure by Quarter 2.
| Stage | What's different |
|---|---|
| Day 1 | First trusted answer. Warehouse connected. Context Graph seeded from query history. Self-serve answers within an hour. |
| Week 4 | Knowledge persists, not people. 100+ Segments and Calculations validated. New analysts onboard against living context. |
| Quarter 2 | A compounding institutional asset. 1,000+ validated answers. Audit-ready. Survives reorgs and departures. |
The asset is yours — portable, sovereign, growing.
The Business Question · Two things to take back to your team.
Who benefits
| Group | What they get |
|---|---|
| Business owners | A Monday-morning review without waiting on analysts. Trusted answers in seconds. |
| Analysts | Get back 50%+ of their week from ad-hoc tickets. Validate composition, do strategic work. |
| Data leaders | An audit-ready, defensible BI surface. Governance survives ad-hoc demand. Definitions converge. |
What it replaces, augments, or adds
REPLACES
Dashboard sprawl (cuts 30–60% maintenance load) · ad-hoc analyst requests (cuts cycle 3–10×) · text-to-SQL pilots
AUGMENTS
Your existing BI stack (Power BI, Tableau, Looker, dbt) · your warehouse compute · your data engineering team
NET NEW
The Context Graph itself · a compounding institutional asset · an audit-ready provenance layer
The question to ask your team isn't "do we need this?" It's "what would we stop doing if this worked?"
ROI Signal
Based on 70+ conversations with analytics leaders:
| Metric | Improvement |
|---|---|
| Cycle time on ad-hoc questions | 50–70% ↓ |
| Analyst throughput | 30–50% ↑ |
| Rework cost from misaligned definitions | 40–60% ↓ |
| New analyst onboarding time | 50%+ ↓ |
| Cross-team definition disputes | 60–80% ↓ |
Estimated annual value: $400–500K for a 10-analyst team. Payback in under 90 days.
Full ROI model: spotonix.com/roi
Who's Behind It
Venkatesh Seetharam · CEO — Co-creator of Apache Atlas (metadata system at Hortonworks). Built data governance infrastructure used by hundreds of enterprises.
Harish Butani · CTO — Founder of SparklineData (acquired by Oracle). BI engine architect at Oracle, SAP HANA, Apache Hive. 20+ years building the analytical infrastructure others rely on.
Backed by 8VC, Tokyo Black, Webb Investment Network. Angel investors include Bob Muglia (former CEO, Snowflake), Keenan Rice (Looker founding team), and Chalfen Ventures.
"Spotonix fundamentally changes how teams interact with data." — Bob Muglia, former CEO, Snowflake
The Ask · A two-week scoping pilot. On your data. No paid contract.
| What we need from you | What you get in 2 weeks |
|---|---|
| One read-only warehouse credential | Your Context Graph, built from your data |
| 30 min of your data team's time, once | Three trusted answers your team validated |
| One hour with two business users | Quantified ROI projection |
| An NDA you already have a template for | Go / no-go decision with zero sunk cost |