AI data analysis agents that query your real data.
Custom AI agents that connect to your data warehouse, analytics platforms, and operational databases — and answer real questions in natural language. Built on the same architecture as Arlo, our own product.
Your data is scattered across systems and nobody can answer questions fast.
Marketing data in GA4 and Meta Ads. Sales data in Salesforce. Revenue in Stripe. Operations in your ERP. Engineering signals in PostHog. Each in a different dashboard, each with its own quirks.
When the CEO asks 'what drove the spike in last week's signups?' — somebody spends three days pulling CSVs from four systems, ad-libbing in Excel, and producing a slide deck that's already outdated.
Generic BI tools (Tableau, Looker, Mode) aren't the answer. They require a defined dashboard up-front, hide the long tail of questions, and still don't give you natural-language access.
An agent that knows your data and answers questions live.
We build AI data analysis agents that connect — via MCP — to your real data sources. Ask 'what's MRR by plan this month vs last month, broken out by acquisition channel' and get an answer with the actual numbers, the SQL it ran, and a chart.
This is exactly the architecture Arlo uses for agency analytics. Pass-through data access (no warehousing), per-user OAuth (no shared credentials), full audit logs of every query. We can replicate the pattern for your stack.
The shape of capabilities we build into agents for this use case. Yours may need a subset; we scope in discovery.
Natural-language SQL
Ask in English; the agent generates and executes safe SQL against your data warehouse. Read-only by default, with parametric guardrails.
Multi-source queries
Pull from your warehouse, your analytics platforms, your operational DB, your CRM — and synthesize a single answer.
Chart generation
Auto-generate charts matched to the data shape. Bar, line, stacked, heatmap. Embedded inline in the conversation or shared as a link.
Saved questions + scheduled reports
Save frequent questions. Schedule them to run daily/weekly and deliver to Slack/email.
Semantic layer
Define your metrics (MRR, churn, LTV, CAC) once in a semantic layer. The agent uses your definitions consistently.
Anomaly detection
Daily / weekly anomaly reports on key metrics. The agent flags unusual movements with suggested causes from the data.
Investigation mode
Multi-step analysis: 'Why did churn spike?' → checks cohort, segment, plan, geography, support tickets, recent product changes. Returns a hypothesis.
Permission-aware access
Per-user data scopes. The marketing lead sees marketing data; finance sees finance data. RBAC respects your existing structure.
Audit log
Every query logged with who, when, what data, what answer. Compliance-ready for SOC 2 audits.
How the agent ships.
Same structure for every agent build. Predictable timelines, fixed scope.
- 01
Discover
1 week. Real workflow, real data, what 'good' looks like. We pick what the agent owns and what humans keep.
- 02
Architect
1 week. Tool inventory, model choice, memory strategy, escalation rules, eval criteria — written and reviewed.
- 03
Build MVP
2–3 weeks. Working agent handles the happy path against real data. Observability and human-review UI from day one.
- 04
Harden
2–4 weeks. Edge cases, evals, monitoring, training, gradual rollout. Production-ready.
- 05
Run
Optional retainer. Model upgrades, prompt tuning, new tools, scope expansions.
The architecture you'd be getting.
BI tools require pre-built dashboards. Our agent handles the long tail — the questions you didn't think to put on a dashboard. Best deployed alongside your BI tools, not instead of them.
The agent shows the actual SQL it ran and the row count. Numbers are read directly from your DB, not generated. If a question can't be safely answered from data, the agent says so instead of guessing.
Warehouses: BigQuery, Snowflake, Redshift, Postgres. Analytics: GA4, Search Console, Meta Ads, Google Ads, Mixpanel, Amplitude, PostHog. SaaS: Salesforce, HubSpot, Stripe, Shopify. ERP: NetSuite, your custom ERP. Plus anything with an API or MCP server.
Yes, with the right architecture. Pass-through (no warehousing), per-user OAuth, encrypted credentials (AES-256-GCM), audit logs, configurable PII redaction. Same pattern Arlo uses; we can replicate it.
Yes. We use Cube or dbt's semantic models when present — the agent queries through them, respecting your metric definitions. Avoids the 'every analyst gets a different MRR number' problem.
Want a data analysis agent for your team?
Tell us the workflow, the volume, and the systems involved. We'll come back with what we'd build, how it would handle the edge cases, and a fixed quote.