The Agentic BI Analyst · Builds Your Context Graph

Your Concierge Analyst that learns. with your business.

Reads your warehouse, models, and every SQL your team has ever run. Composes verified plans from your validated Segments and Calculations. Same accepted plan. Same SQL. Every time.

Built for analytics teams where the semantic layer matters and ad-hoc questions never stop.

In production with teams in

AdTech FinTech PaaS Debt Collection

LEARNS your business

Reads your warehouse, past SQL, and your team's vocabulary. Speaks back in measures, segments, hierarchies — not column names.

Powered by your Context Graph →

COMPOSES the plan

Builds from your validated Segments and Calculations — not from scratch. Plan visible before SQL runs.

Read the plan, then approve →

COMPOUNDS with every Q

Every verified answer compounds your Context Graph. Knowledge persists past people and conversations.

Day 1 → Week 4 → Quarter 2 →

Decide faster. Trust every answer. Stay ahead.

01

DECIDE FASTER

A verified answer in seconds.

In your own business language — not a ticket behind the analyst’s queue. The whole org self-serves, not just the top.

02

TRUST EVERY ANSWER

Every answer shows its work.

The terms, filters, measures, and source logic behind it. If something’s ambiguous, Spotonix asks instead of guessing.

03

STAY AHEAD

Ask once. Operate the week.

Each answer becomes a watched signal — a morning brief on what changed, a weekly review that remembers what you decided. Your vernacular compounds into knowledge that outlasts turnover.

Meaning, turned into answers.

Self-service BI is an illusion of competence.

Independent signal

BARC 2025: BI improves decisions when used, but only 25% of employees use it daily — 16% in large enterprises.

Dashboards
Trusted · Rigid

Pre-built views. Break on novel questions.

Breaks on novel questions
SQL Notebooks
Flexible · Siloed

Flexible but siloed. Knowledge walks out with the analyst.

Context walks out the door
Text-to-SQL
Fast · Opaque

Generates from scratch every time. Different SQL Mon vs Tue.

Context never forms
ChatGPT · Claude
Brilliant · Stateless

Brilliant. Stateless. No memory of your business.

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.

“How is this different from Claude or ChatGPT?”

CLAUDE OR CHATGPT IN YOUR WAREHOUSE

A brilliant stranger holding a flashlight.

Writes plausible SQL each time. Different syntax Monday vs Tuesday. No memory of your business's vocabulary, your validated logic, or what your analysts decided last quarter.

/* generated, not composed */
SELECT … FROM raw_orders
WHERE customer_id IN (…)
// 4 columns away from the right answer

SPOTONIX ON YOUR CONTEXT GRAPH

An analyst with a notebook of your past decisions.

Composes the answer from your validated Segments and Calculations. Same question Monday and Tuesday compiles to the same SQL, by design. Plan visible before SQL runs.

/* composed from accepted plan */
WITH habitual_buyers AS (…)
SELECT store_id, qoq_change
// same accepted plan → same SQL, every time

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.

01

YOUR DATA

Schema, joins, profiles, data quality.

Read once, enriched continuously. The system understands the shape of your warehouse before any question gets asked.

02

YOUR LOGIC

Measures, segments, hierarchies, vocabulary.

Captured as first-class business primitives — Segments, Calculations, Answers. The things your team already says, promoted to the system's native nouns.

03

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. Persistent. Sovereign. Growing.

Understand. Interpret. Compound.

Three steps under the hood. Each one is a checkpoint — none of them is a black box.

Which stores are losing habitual buying customers?
01

Understand

Discovery, classification, and reasoning

A business question isn't a search query. "Stores," "losing," and "habitual buying customers" each carry different semantic weight. Spotonix resolves these against your company's vocabulary — through semantic search, pattern classification, and structural reasoning — not a generic model.

interpret.understand
StoresSegment losingGrowth Pattern habitual buying customersSegment
Language Analysis Ground Classify Reason Resolve
02

Interpret

Compose a plan in BI Algebra — and show it before SQL runs

The LLM proposes a symbolic plan in BI Algebra, composed over your Segments and Calculations. The system accepts the plan only if the algebra closes — every concept resolves, every Calculation grounded. The Plan Card is visible before any SQL compiles. You see exactly what the system understood — approve, or refine.

interpret.algebra
SegmentHabitual Buyers✓ discovered & adapted
CalculationQoQ Customer Count Change+ composed
AnswerStore × Quarter Retention+ composed
InterpretationResolved againstStatus
"Habitual buyers"Customers, $500+ / quarter✓ algebra closes
"Losing"QoQ decline in customer count✓ algebra closes
✓ Approve & compile SQL Refine
03

Compound

Every verified answer compounds your Context Graph

Validated interpretations become reusable building blocks. New Segments, Calculations, and Answers compound the graph permanently. The next question builds on what you've already proven — the 100th question is faster than the 10th.

A tool on Day 1.
Infrastructure by Quarter 2.
The asset is yours — persistent, sovereign, growing.

knowledge.compound
4
Blocks
14
Blocks
Q1Top stores by revenue+2 Segments
Q2Customer segments by spend+1 Seg, +2 Calcs
Q3Retention by channel+1 Calc, +1 Analysis
Q4Quarter-over-quarter growth+1 Calc
Q5Stores losing habitual buyers+3 Segs, +1 Analysis

Same analyst. Five questions. 3 Segments, 4 Calculations, 2 Answers — all reusable.

See how it thinks

SCENE 1 / 3 · CLARIFY
SCENE 2 / 3 · REUSE
SCENE 3 / 3 · DISCOVERY
> Which stores are losing habitual buying customers?
> Top categories habitual buyers shop this year?
> Show me how the customer dimension is segmented w.r.t. sales

? "habitual buyers" matches 2 Segments — pick one

≥4 visits / quarter · used in 4 prior Answers
spend ≥ $500 / quarter
Segment Habitual Buyers ✓ resolved
Calc QoQ Customer Count Change ✓ grounded
Dim Store × Quarter ✓ scoped
Algebra closes · 12 stores losing habitual buyers

Plan · auto-applied from Context Graph

"habitual buyers" ≥4 visits / quarter Segment · from your Context Graph
"top 5 categories" rank(sum(sales)) limit 5 Calc · ranking
"this year" year = current_year Dimension
No clarification needed. Algebra closed · 5 categories returned

Surfaced from your Context Graph · 3 Segments + 2 attribute groups

Habitual Buyers ≥4 visits / qtr used in 2 prior Qs
Premium Customers spend > $5,000 validated
High Spenders top 5% by spend validated
Demographics gender × marital × education attribute
Geography state → city → zip hierarchy

TPC-DS benchmark · clarify once · memory forever · same accepted plan → same SQL

Three pillars. All three are required.

01

YOUR CONTEXT GRAPH

Yours to compound.
Not theirs to ship.

Built from your schema, queries, and BI assets. Compounds every time you use it. Data-quality issues compound alongside — surfaced without being asked.

02

ALGEBRAIC VERIFICATION

The system refuses
instead of inventing.

No probabilistic guessing. Ambiguous questions are surfaced for clarification, not guessed at. Every verified query has its 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. Snowflake, Databricks, BigQuery — same system.

Each pillar exists elsewhere in pieces. The hard part is all three operating together in one workflow.

Most BI tools answer one question. We answer all four.

Analytics has four invariant questions — what happened, why, what’s next, what to do. Because Spotonix learns your business and verifies every plan, it can answer all four — for every signal you watch, automatically and honestly.

01

What happened

The number, the deltas, the trend — composed, not rendered.

02

Why

The driver, gated by process-control. Or an honest "investigate."

03

What’s next

The trajectory if pace holds — with the analyst’s caveats.

04

What to do

One trustworthy recommendation. Never three charts and good luck.

Read-only access. Your VPC. Your boundary.

Read-only credentials · SOC 2 in progress · BYO LLM key · Self-host option · Your Context Graph never leaves your boundary.

Works with

Snowflake Databricks BigQuery Redshift dbt Power BI Tableau Looker

What data leaders are saying.

"Spotonix is taking a first principles approach to building business context. This is essential for the coming world of agentic data applications."

BM
Bob Muglia
Fmr. CEO, Snowflake · Advisor

"The context layer is the missing piece in modern data stacks."

MS
Mohit Saxena
Founder & CTO, InMobi

"Most analytics tools optimize for speed. Spotonix optimizes for institutional memory. That's an entirely different game."

VS
Venkat Sonnathi
Chief Architect, Yubi

We've built this infrastructure before.

A team of engineers and leaders who built the metadata systems, BI engines, and semantic infrastructure the modern data stack runs on. Spotonix is the convergence of those lessons.

8VC Tokyo Black Webb Investment Network Advisor Bob Muglia Fmr. CEO, Snowflake

A 2-week scoping pilot. On your data. No paid contract.

WHAT WE NEED FROM YOU

  • One read-only warehouse credential
  • 30 min of your data team's time, once
  • One hour with two business users
  • An NDA template you already have

WHAT YOU GET IN 2 WEEKS

  • Your Context Graph, built from your data
  • Three trusted answers your team validated
  • Quantified ROI projection (yours)
  • Go / no-go decision with zero sunk cost

founders@spotonix.com