The reporting bottleneck
Enterprise business intelligence still takes days — and still can't tell you what happens next.
Manual BI turns simple operational questions into IT tickets. Early GenAI attempts swapped the wait for a new problem: confident, fabricated numbers. AINalyzer fixes both.
Manual BI & "AI that draws charts"
- ✕Slow time-to-insight. Every question means hand-writing SQL/ES queries and frontend code — days-long IT tickets.
- ✕Hallucination risk. Generic GenAI invents placeholder metrics and broken charts on large datasets.
- ✕Historical only. Forecasting lives in a separate, technical data-science workflow — disconnected from reporting.
- ✕Runaway cost. Ad-hoc LLM querying with no caching or loop limits burns API spend and strains databases.
Trusted, predictive intelligence — on demand
- ✓5–15 seconds. A natural-language question becomes fully interactive, self-contained business intelligence.
- ✓Zero hallucination. A dedicated Validation Agent makes every metric explainable — traced to actual database output before it renders.
- ✓Past and future in one view. Real ML forecasting with confidence bounds, built into the same conversation.
- ✓Predictable spend. A 48-hour cache and a strict 25-iteration limit keep compute and API costs in check.
Proven in production
From hours of manual work to
seconds of validated insight
s
Time to full, interactive business intelligence — down from hours
Hallucinated metrics — caught and blocked by the Validation Agent
x
Autonomous self-healing retries before any failure surfaces
h
Cache window — identical requests served instantly, no re-inference
How it works
One unbroken, secure loop —
from question to validated
business intelligence
A dual-agent architecture does the work of an autonomous data engineer: it
discovers your schema, writes native queries, runs real forecasts, and refuses
to ship anything that doesn’t check out. Tap any stage.
Ask a question in any language
"Show sales trends and predict next quarter transaction peaks." No SQL, no dashboard builder, no IT ticket — anyone on the team can ask.
Plans the work with memory of context
The Primary Agent retains awareness of prior queries for consistency, then autonomously plans how to answer — including a retry loop of up to 25 iterations.
Native queries run inside your databases
Tools like list_indices, get_mappings, search, esql and forecast_time_series execute against Elasticsearch, Snowflake, PostgreSQL, Neo4j and more — aggregating at the source.
Dedicated models do the real math
Interpreted intent and time-series data are passed to real forecasting engines (TimesFM 2.5, Prophet, NeuralProphet) that compute trends and confidence bounds.
Every metric is checked before it ships
An isolated agent runs HTML validation, hallucination detection and completeness checks. IF PASS → render. IF FAIL → rewrite the query and retry.
Self-contained, interactive, offline-capable
Validated data is compiled into a professional, interactive BI dashboard with KPIs, charts and an executive summary — clear, actionable and explainable, with every metric traceable to your real data.
See it in action
Simulate your business
intelligence — live
Watch a real question turn into explainable, forecast-ready business
intelligence: schema discovery, native queries, an ML forecast, validation, and
a finished interactive view.
Why GraphRAG
"Isn't this just an LLM drawing
charts?" No — and here's
exactly why.
AINalyzer is a deterministic data-engineering architecture. The LLM is used<br>purely to orchestrate and render. The heavy lifting is offloaded to real<br> databases and dedicated ML models.
Real ML, not "LLM math"
Live petabyte data, not a static sandbox
Multi-connector scale, not context limits
MCP orchestration, not a stateless chatbot
Closed-loop self-correction, not single-shot
Real machine learning, not "LLM math"
Ask an LLM to "forecast the next 90 days" and it draws a line that looks plausible from textual pattern-matching — with no real seasonality, no statistical rigor, and no true confidence intervals.
We bypass the LLM's mathematical limits entirely. The backend intercepts your data and runs it through dedicated, local ML engines. Claude never does math — it only renders the result.
Live, petabyte-scale data — not a static sandbox
Chat-based artifacts live in an isolated frontend sandbox. To visualize anything, someone has to export a CSV and paste it in — creating PII exposure, context limits, and outputs that are stale on arrival.
Through the Model Context Protocol, AINalyzer connects securely to your live production clusters and queries them at the exact second of the request. Aggregation runs in the database; only mathematical summaries reach the LLM.
Infinite scale via multi-connector architecture
LLMs have hard context-window limits. Paste a large relational database into a chat and the model truncates or crashes — making real enterprise-scale analysis impossible.
The agent writes native SQL, ES|QL and Cypher to run massive JOINs and aggregations at the database level. Only the tiny finalized payload returns to the LLM — enabling petabyte-scale analysis with zero memory crashes.
The MCP orchestration server — not a stateless chatbot
A native chatbot is a stateless text generator. It has no agency, no network access, no security guardrails, and no safe way to touch live infrastructure.
Our custom MCP server is an active, deterministic orchestration layer. It sits as a secure firewall — the "bouncer" — between the LLM and your data. The model can't reach into the database; it must request a tool, and unauthorized payloads are rejected.
Closed-loop autonomous self-correction
Standard AI runs single-shot. Give it the wrong column name and it crashes or hallucinates a fake chart — forcing a human to debug and re-prompt.
A ReAct loop runs up to 25 autonomous iterations. The agent reads your schema before querying, and when a query fails it reads the database error, rewrites its own logic, and retries — no human in the loop.
What it does
Three capabilities, one
conversational interface
Autonomous Data Exploration
The agent discovers your data so your team doesn't have to configure anything.
- Automatically identifies datasets, schemas, mappings and relationships across enterprise sources.
- Generates optimized queries and retrieves the right data without manual setup.
Predictive Analytics & Forecasting
See what happened and what's coming next — in the same view.
- AI-driven forecasts with future trend lines, confidence intervals and predictive insight.
- Peak prediction, anomaly detection, stabilization analysis and capacity planning.
Self-Healing Business Intelligence
Quality control is built into the pipeline, not bolted on after.
- Converts natural-language requests into interactive business intelligence, KPIs, charts and executive summaries.
- Completeness checks, hallucination detection and auto-correction before anything renders.
Built for enterprise trust
Real models do the math. A
secure server guards your
data.
Forecasts you can defend in a board meeting
AINalyzer routes time-series work to purpose-built forecasting models — the same class of model used in production data science, not a line the LLM imagined.
Your raw data never leaves your perimeter
The MCP server is a strict firewall between the model and your infrastructure. It enforces what the LLM can and cannot do.
Math is pushed down to the database; only summaries are returned.
Row-level PII is never sent to the model — only aggregated results.
If the LLM attempts an action it shouldn't, the server strictly rejects the payload.
See your data answer
its own questions.
Thirty minutes. Your stack, your question, working business intelligence at the end of it. No slideware.