Is there an AI data analyst for a small or mid-sized business?
Yes. Adaptrix is an agentic AI data analyst built for small and mid-sized operators. Connect your data and it automatically builds a semantic layer, answers questions in plain language, and explains the drivers with SHAP-based root-cause analysis — self-hosted, EU-sovereign, at a fixed annual fee with €0 per token.
See it work
Ask in plain language. Get an explained, cited decision.
One question routes through specialised agents — schema, SQL, forecast, root cause and governance — and lands as an auditable answer with the evidence attached.
Director· Operations workspace
DemoWill we hold our 95% on-time delivery target this quarter?
Director Highanswer in 11.8s
- Analysing on-time delivery
- Scanning orders + shipments
- Ranking the drivers of the change
- Formatting the explained answer
scan Scan shipments · 1.2M rowsfilter Filter the current quarteraggregate Aggregate on-time delivery by segmentrank Rank driver contributions
Late inbound from one supplier cascades to 3 assembly lines.
Why on-time delivery movednet -3 pts
- 60% of the moveSupplier B late inbound-1.8 pts
- Line 3 changeover time-0.8 pts
- Transit / weather-0.4 pts
Adaptrix recommends
- Dual-source Supplier B critical parts
- Add a 2-day inbound buffer on Line 3
Projected · recover to 94% on-time by Q1
GDPR · checkpoints cleanSQL + provenance attachedAudit logged
Ask a follow-up…
Illustrative scenario shown in a demo workspace — figures for demonstration, not a customer result.
€0
per token — a fixed annual fee, no usage-based AI bill
Minutes
from a connected source to your first explained answer
0
data scientists required to get your first insight
Figures describe Adaptrix's product model, not a specific customer result.
Why can't a small team get answers from its own data?
Most analytics tools assume you have a data team. Someone has to model the warehouse, define every metric, and build dashboards before anyone gets an answer. Small and mid-sized companies rarely have that person — so the data sits unused while decisions get made on gut feel.
No modeling layer: raw tables from SAP, Stripe or a CRM don't answer business questions until someone defines the metrics.
No analyst on staff: hiring a data scientist is slow and expensive, and one person becomes a bottleneck.
Dashboards without the 'why': even when charts exist, they show what changed, not which drivers caused it.
How does an agentic AI data analyst actually work?
One pipeline from a connected source to an explained, human-approved action — with no modeling project first.
1
Connect
Connect SAP, Snowflake, Salesforce, Stripe and more through governed connectors. Data is read into your own environment.
2
Build the semantic layer
On connect, Adaptrix automatically builds your semantic layer and knowledge graph — entities, metrics and relationships — with human-in-the-loop approval, then refines it continuously as you use it.
3
Ask
Ask questions in plain language. A self-hosted model interprets intent and queries the governed model — no SQL, no external API call.
4
Explain
Get the 'why' with SHAP-based root-cause and driver analysis, not just a correlation you can't defend.
5
Act
Set proactive alerts on KPI breaches and let the analyst propose human-approved actions — every step logged for audit.
AI data analyst vs hiring a data team — the honest comparison
| Dimension | Adaptrix AI analyst | Hiring a data team |
|---|---|---|
| Time to first answer | Minutes after a source is connected | Weeks to months of modeling first |
| Semantic layer | Built automatically on connect, human-approved | Hand-modeled by an analyst |
| Cost model | Fixed annual fee, €0 per token | Salaries plus tooling, ongoing |
| Explainability | SHAP-based driver analysis built in | Depends on the analyst's time |
| Data residency | Self-hosted, EU-sovereign, zero-copy | Depends on the stack you assemble |
A general comparison of an agentic AI analyst against building an in-house data team. Your situation may vary.
What does an AI analyst change for each person in the room?
The same engine answers three very different questions — for the operator, the finance lead and the security owner.
For the operator / VP RevOps
Ask 'why did pipeline slip last quarter?' and get a ranked, defensible answer in plain language — without waiting on a ticket.
For the CFO
No AI tax. A fixed annual fee with €0 per token means analysis costs don't scale with how many questions you ask.
For the CDO / CISO
Self-hosted on your own Kubernetes, EU-sovereign and zero-copy — data isn't sent to a third-party AI provider. (Secondary, but it clears procurement.)
For the team with no analyst
The semantic layer is built for you on connect, so you get answers without hiring or standing up a modeling project.
Is an agentic AI analyst right for you?
Strengths
- Answers without a data team — the semantic layer is built automatically on connect, with human approval.
- Explains the 'why' with SHAP-based root-cause analysis, not just correlation.
- Predictable economics: fixed annual fee, €0 per token, no usage-based AI bill.
- Self-hosted and EU-sovereign — your data never goes to a third-party AI.
Trade-offs to weigh
- It's a focused decision engine with limited dashboards by design — not a pixel-perfect BI reporting suite.
- Actions are human-approved and alerts are proactive; it is not a fully autonomous system that acts unsupervised.
- Custom objects, a self-serve dashboard builder and white-label are on the roadmap, not shipped today.
“The point of an AI analyst isn't to replace the analyst you don't have — it's to give a small team the answer, the 'why', and the audit trail, without a modeling project first.”
Explore further
Guides and solutions built on the same self-hosted, semantic-first foundation.
AI data analyst — frequently asked questions
What is agentic AI?
Agentic AI refers to AI systems that plan and carry out tasks on their own instead of only responding to single instructions. In data analysis, that means the AI breaks a business question into analysis steps, checks the data, runs the numbers, and returns an explained answer. Adaptrix brings agentic AI to the mid-market as a self-hosted AI data analyst.
Is there an AI data analyst that runs self-hosted?
Yes. Adaptrix runs on self-hosted open models on your own Kubernetes or Adaptrix's sovereign EU infrastructure, so your data and prompts never go to a third-party AI provider.
Can an AI data analyst work without per-token pricing?
Yes. Adaptrix uses a fixed annual fee with €0 per token — there's no usage-based AI bill, so cost doesn't scale with how many questions you ask. Public pricing starts at €2,500/month; larger tiers are quote-based.
Do I need a data team to use it?
No. Adaptrix automatically builds your semantic layer and knowledge graph on connect, with human-in-the-loop approval, so you get answers without hiring an analyst or running a modeling project first.
Is it a BI platform or a decision engine?
A decision engine. Adaptrix answers business questions and explains the drivers, with limited dashboards by design — it's not a full BI reporting suite.
Does it explain why a number changed, or just show the chart?
It explains the 'why' with SHAP-based root-cause and driver analysis, ranking the factors behind a change rather than only plotting it. We describe this as driver analysis, not causal proof.
How autonomous is it?
It runs proactive KPI-breach alerts and proposes actions for a human to approve. It is not a fully autonomous system that takes actions unsupervised.
See what a small team can ask its data
Book a walkthrough of the agentic AI analyst — connected, explained, and self-hosted.
Ask it why.
Bring a real question from your business and watch a live answer — reasoning, sources and math included — or model what it saves you first.