AI Analytics Agent

Most AI data tools confidently give you wrong answers.
Ours doesn't.

The reason every “ask your data in plain English” tool fails is the same: it's built on broken data, with no understanding of your business. We fix the foundation, build the context, and ship you an AI agent your team can actually trust — custom-built for your product, or set up on top of Julius, Vanna, or DataGPT.

Book a CallSee How It Works
4-Week BuildCustom or Tool-BasedTested on Your Real Data
Why It Fails

You've probably tried this already.
Here's why it didn't work.

Every “AI analyst” demo looks magical for the first ten minutes. Then someone asks a real question, and the wheels come off.

01 / Hallucination

It hallucinates numbers that look right

You ask it for last month's MRR. It returns a number. The number sounds reasonable. You paste it into a board update. Three days later, finance pulls the same metric and gets something different. Turns out the AI joined the wrong tables, used a stale event, and confidently invented a number that was off by 18%. The worst part is you didn't know to question it.

Real Problem

No validation layer. The model doesn’t know what "MRR" actually means in your business — only what it looks like in random examples from its training data.

02 / Wrong Questions

It can't answer the questions that matter

Your team asks: "Why did paid conversion drop in the EU last week?" The AI either says "I don’t have enough information," or it generates a SQL query against the wrong table and returns a number that’s technically correct but answers a completely different question.

Real Problem

No business context. The agent has no idea which table holds the right version of the data, what "paid conversion" means in your funnel, or how to think about geography in your schema.

03 / Abandonment

It works for two weeks, then your team stops using it

Onboarding goes well. The first few queries land. Then the team hits the third or fourth wrong answer in a row — and just goes back to asking the analyst directly. Six months later, the tool is still in your stack, and nobody’s logged in since March.

Real Problem

No feedback loop. Nobody validated the answers in the early weeks, nobody refined the knowledge base when the model got it wrong, and trust eroded faster than the team had patience for.

The Datalyze Build

What it actually takes to ship an AI analytics agent that works

Six steps. Built around the three things every other approach skips: clean data, real business context, and a validation loop. Most engagements take 4–6 weeks end to end.

01

We start with how your business actually works

We don't begin with your schema. We begin with your team. What questions do they ask the analyst every week? What metrics matter to your CEO? What language does your product team use that's different from your finance team? The answers to these questions become the foundation of the knowledge base — and the reason the agent will eventually understand the difference between “active users” and “paying users” without being asked twice.

What this fixesThe "no business context" failure. Most tools start with your data schema. We start with your business.
02

We clean and model your data so the agent can reason over it

Raw data breaks AI. Misnamed events, drifted definitions, broken pipelines, and stale tables produce hallucinated answers no matter how good the model is. We audit your full data layer — product events, warehouse tables, pipelines, definitions — and rebuild the parts that the agent will need to query. You end up with clean, reusable tables as a side benefit, even outside the AI agent itself.

What this fixesThe "hallucinated numbers" failure. Clean data inputs are non-negotiable. Skip this and the model will lie to you with confidence.
03

We build the agent's brain

This is what every other AI analytics tool skips, and it's the single biggest reason most of them don't work. We create a deep knowledge base covering your business context, metric definitions, table relationships, common questions, and the gotchas only a senior analyst would know (“if someone asks for revenue, use the deduped Stripe table, not the raw events one”). The agent doesn't just see your schema — it understands your product.

What this fixesThe "can't answer the questions that matter" failure. This is the difference between a chatbot that runs SQL and an agent that thinks like an analyst.
04

We build the agent or set up the right tool

With clean data and a real knowledge base in place, we either build a custom AI agent tailored to your product, or we configure a best-in-class tool like Julius AI, Vanna.ai, or DataGPT on top of your foundation. The choice depends on your stack, your budget, and how custom your needs are. We'll recommend the right path on the first call — and we have no incentive to push you toward the more expensive option.

What this fixesLock-in and overspend. Most agencies will sell you their custom build because that's what they make money on. We'll tell you when an off-the-shelf tool would do the job better.
05

Two weeks of supervised testing on your real questions

Your team uses the agent on real questions for two weeks. We sit alongside, tracking where it's right, where it's off, where it's almost-right-but-subtly-wrong. We refine the knowledge base, add missing context, and tighten the responses until the team trusts what comes back. This is the step that decides whether the agent gets used or quietly abandoned in three months.

What this fixesThe "team stops using it" failure. No AI tool earns trust without supervised early use. We build that trust deliberately.
06

We keep it sharp over time

Your data changes. New tables, new events, new metrics, new questions from new hires. Without ongoing maintenance, the knowledge base goes stale and the agent's accuracy degrades within a quarter. We offer ongoing maintenance plans — keeping the data model current, expanding the knowledge base, and retraining when something material changes in how your business measures itself.

What this fixesSlow decay. The reason AI tools die in production isn't the launch — it's the lack of upkeep.
Two Paths

Custom AI agent, or set up the right tool. We'll tell you which.

Most engagements fall into one of two paths. Both start with the same foundation work — clean data, knowledge base, validation. The difference is what gets built on top.

Path A

Custom AI Agent

We build the agent ourselves, tailored to your stack and your business logic. You own the code, the knowledge base, and the deployment. No per-seat pricing. No vendor lock-in.

Best forComplex data, unique product logic, sensitive data that can’t go through third-party tools, or teams that want full control over how the agent behaves.
Timeline4–6 weeks build + ongoing maintenance
Path B

Tool Setup

We configure a best-in-class AI analytics tool — Julius AI, Vanna.ai, or DataGPT — on top of your cleaned data and knowledge base. You get the polish of a productized tool with the foundation work that makes it actually accurate.

Best forTeams whose data and business logic fit cleanly into an existing tool’s model, buyers who want a polished UI out of the box, or those who’d rather pay a SaaS subscription than maintain custom code.
Timeline3–4 weeks setup + ongoing maintenance

Not sure which? That's what the first call is for. We'll look at your stack, your data, and your team's needs, and tell you which path fits — even if the answer is “neither, you don't need this yet.”

Proof

What this looks like in practice

Series B Fintech·Consumer Finance·100K+ customers
The problem

Their growth team was asking the data team 30+ ad-hoc questions per week. Half of them had been answered before. The other half took 2–3 days to come back with conflicting numbers across dashboards. Their analyst was spending 60% of their week fielding repeat questions instead of doing real analysis.

What we built

A custom AI analytics agent built on top of their cleaned warehouse, with a knowledge base covering 80+ business metric definitions, table relationships, and the specific gotchas in their Stripe and payment data model. Two weeks of supervised testing before handoff.

The outcome
  • Reduced ad-hoc data requests by 73% within 6 weeks
  • Growth team self-served 4 out of 5 questions without waiting on the data team
  • Data team reclaimed ~15 hours/week to focus on strategic analysis

“For the first time, I trust an AI tool to answer a metrics question without checking the work. That's what we'd been trying to get to for two years.”

Head of GrowthSeries B Fintech
Questions

Frequently asked

How is this different from buying Julius AI or DataGPT directly?

You can absolutely buy those tools directly. Most companies that do find out within a month that the tools don't work on their data — because their data is messy, their definitions are inconsistent, and there's no business context loaded into the tool. We do the foundation work that makes those tools (or a custom agent) actually accurate. About a third of our engagements are tool setups, not custom builds.

Why do most AI analytics agents give bad answers?

Three reasons, in order of impact: (1) the data they're querying is broken — misnamed events, drifted definitions, stale tables; (2) the model has no idea what your business metrics actually mean — it's reading your schema like a tourist reading street signs in a foreign language; (3) nobody validated the answers in the first few weeks, so wrong answers slipped through and trust eroded. We solve all three.

How long does this take to build?

Most engagements run 4–6 weeks end to end. The foundation work (cleaning data, building the knowledge base) takes 2–3 weeks. The agent build or tool setup takes 1–2 weeks. The supervised testing phase takes 2 weeks. We can start within a day of signing.

Do I need SQL or technical skills to use the agent?

No. That’s the whole point. Your team asks questions in plain English ("what’s our paid conversion rate in Germany this month?") and the agent runs the right query against the right table and returns the answer. The technical work happens once, during the build — your team never touches SQL.

What if I already have an analyst?

Even better. The agent doesn't replace your analyst — it removes the repetitive questions from their queue so they can focus on the analysis that actually requires a human. Most of our clients see their analyst's productivity jump because they're no longer answering “what's our MRR” for the fifth time this week.

What if the agent gives a wrong answer after we launch?

It will, occasionally — every AI tool does. The difference is that during the supervised testing phase, we catch the failure modes early and refine the knowledge base before the team relies on the answers. After launch, ongoing maintenance keeps the accuracy up. We monitor the question patterns, and when the agent starts getting something wrong, we fix it within the week.

Can you work with our existing data warehouse?

Yes. We work fluently with BigQuery, Snowflake, Databricks, Postgres, and most modern warehouses, plus the pipelines and modeling layers around them (dbt, Fivetran, Segment, Rudderstack). If your data lives somewhere unusual, mention it on the first call and we'll tell you straight whether we can handle it.

Book a Call

Stop guessing whether
your data is right

Book a 30-minute call. Bring your stack, your top 5 questions your team asks every week, and any AI tools you've already tried. We'll tell you exactly what would need to happen to make an AI analytics agent work for you — and whether it's worth doing yet.

Book a Call

We take on 2–3 new AI analytics builds per month.