Custom AI Agents
for Data Analysis & Reporting
We build analysis agents that connect to your warehouse, answer the questions your team keeps asking, and send the report before anyone requests it — with the SQL visible and the numbers checkable.
30 minutes. Bring your last month of ad-hoc data requests and we'll tell you which ones an agent could answer reliably — and which ones it shouldn't.
Your analysts spend most of their week on requests they've answered before
Every department wants numbers. Product wants the funnel broken down by cohort. Finance wants revenue by segment. Marketing wants attribution. Each request lands in Slack or a ticket, and each one costs an analyst the same sequence: understand what's actually being asked, find the right tables, write the query, sanity-check the result against a source system, and paste it somewhere the requester will see it.
A large share of those requests are the same metric sliced a different way. The queue stretches. Executives stop asking because the answer arrives after the decision. And the analysts you hired for modelling and investigation spend their week on retrieval.
The instinct is to build more dashboards. It rarely works, because the next question is always slightly outside what the dashboard covers — and answering that still requires an analyst.
What removes the work is an agent that can read the question, understand your schema and your metric definitions, write and run the query, check the result, and deliver it in the format the requester actually uses. Plus the reports nobody should have to request at all: the Monday summary, the anomaly alert, the month-end pack.
Why most “ask your data a question” tools disappoint
Natural-language-to-SQL demos are impressive and the published research is more sober than the marketing.
On BIRD, the standard research benchmark for text-to-SQL against realistic databases, the leading system reaches roughly 82% execution accuracy, against human performance around 93%. That's a strong result — on a benchmark of self-contained, single-turn questions.
Enterprise conditions are harder, and the drop is dramatic. Spider 2.0, which evaluates against real enterprise environments — schemas with thousands of columns, multiple SQL dialects, multi-step workflows — reports execution accuracy falling to roughly 21%, against over 90% for the same class of system on academic single-turn tests. BEAVER, a benchmark built from real private data warehouses and actual query logs, exists precisely because researchers found benchmark scores systematically overstate production performance.
There's a second gap the benchmarks barely measure: real analysis is a conversation. “Revenue by region.” “Now just enterprise accounts.” “Compare to last quarter.” Strong single-turn accuracy doesn't imply strong multi-turn accuracy, and multi-turn remains understudied relative to how much it matters in practice.
Why this matters for what you're buying
An agent that gets 80% of queries right sounds good until you consider that the 20% arrive looking exactly as confident as the rest. A wrong revenue number in a board pack is worse than no number, because someone acts on it.
So the engineering that matters isn't the model. It's constraining the problem: a defined semantic layer, a curated set of questions, verification against known-correct results, visible SQL, and a system that says “I'm not confident” instead of guessing. That's what we build, and it's why we start narrow.
What we build
Ad-hoc question answering
The recurring questions your analysts field, answered against your warehouse in the channel people already ask in — Slack, Teams, or email. Every answer ships with the SQL that produced it, so anyone on your data team can verify it the way they'd review a junior analyst's work.
Scheduled and triggered reports
The Monday revenue summary, the month-end pack, the weekly channel breakdown — generated, formatted and delivered without anyone assembling them. Triggered reports fire on conditions rather than a clock: when a metric crosses a threshold, when a pipeline stage stalls, when a cohort behaves unusually.
Anomaly detection and alerting
Continuous monitoring of the metrics you nominate, with alerts when a value falls outside its expected seasonal range. The threshold is configurable per metric — set it too tight and the alerts get muted within a fortnight, which is the most common way this feature fails. [FILL: your default threshold and how you tune it. Be specific; the competitor on this SERP is.]
Narrative summaries
Not just the chart. A written explanation of what moved, by how much, and which segment drove it — the paragraph an analyst would write above the table. This is where an agent genuinely outperforms a dashboard: dashboards show change, they don't explain it.
Semantic layer and metric definitions
If "active user" means three different things in three teams, an agent will confidently return all three. Part of every build is pinning down the definitions and encoding them so the agent computes your metrics your way. [VERIFY: confirm you do this work. If you don't, cut this — but it's the single highest-value thing on the list and worth adding to the service if it isn't already.]
Data quality checks
Freshness, null rates, row-count drift, referential breaks. An analysis agent running on stale or broken data produces wrong answers faster than a human would — so we check the inputs before trusting the outputs.
Works with the stack you already have
Your warehouse vendor probably ships its own assistant. They're generally good at conversational querying inside their own boundary, and limited when the answer needs data from somewhere else, business logic that lives in your application, or a report delivered outside their platform. That gap is where we build.
Snowflake
Agents built against your Snowflake environment with read-only roles, warehouse-level cost controls, and access to the semantic definitions your team already maintains. [FILL: one specific thing you've built on Snowflake.]
BigQuery
Query-cost control matters more here than anywhere else, because a badly formed agent query can scan a great deal of data before anyone notices. Byte-scanned caps and query timeouts are part of the build, not an afterthought. [FILL: your actual caps.]
Databricks
[VERIFY: only claim Databricks if you have genuine experience. Cut this section otherwise.]
Power BI, Looker & Tableau
Agents that read from your existing semantic models rather than reimplementing your metrics — so the number the agent returns matches the number on the dashboard. Discrepancies between the two are the fastest way to lose trust in an analytics agent. [FILL: which of these three you've genuinely shipped against.]
Postgres, MySQL & app databases
For teams without a warehouse. We connect via read-only replicas so analysis load never touches production. [VERIFY: confirm this is your standard practice.]
GA, HubSpot, Salesforce & Stripe
Business systems as data sources alongside the warehouse, so a question like "which campaign drove the accounts that renewed" doesn't require three exports and a spreadsheet.
Spreadsheets & no warehouse
Plenty of companies run on Google Sheets and a production database. That's a valid starting point — and often the right first build, because it's cheap to prove the value before anyone funds a warehouse migration.
How we keep the numbers right
This is the page's differentiator — and what a data leader is actually evaluating.
Read-only by default
The agent's database role cannot insert, update or drop. A bad query can waste warehouse credits; it cannot corrupt data.
Cost guardrails
Query timeouts and scanned-data caps, so a poorly formed question can't produce a surprising bill. If a query would breach the limit it's rejected and the agent proposes a cheaper formulation. [FILL: your actual timeout and cap defaults.]
Visible SQL
Every answer carries the query that produced it. Your data team reviews the agent's SQL the way they'd review any other analyst's.
Evaluation set before launch
We take questions your team has already answered, with known-correct results, and build a scored test set. It's how we know the agent is right, how we know a change improved things, and what you see before it answers anything for real.
Scope discipline
We launch on a defined question set and expand as accuracy holds. An agent that answers 40 questions reliably beats one that attempts anything and is right most of the time.
Confidence thresholds
Below the threshold, the agent says it isn't sure and routes to a human rather than producing a plausible number. Given the benchmark reality, this is the most important design decision on the whole build.
Human approval on consequential outputs
Anything going to a board, a regulator or a customer gets a review step by default.
How we build it
Audit to production in 2–6 weeks.
Free audit (week 1)
We look at your recent ad-hoc requests and recurring reports, and classify them: automatable now, automatable after definition work, and not suitable. You get the breakdown and an honest read on whether the volume justifies a build. Some audits end here.
Definitions and scope
Agree the metric definitions, the question set for launch, the never-automate list, and the confidence thresholds.
Evaluation set
Real questions, known-correct answers, scored.
Connect and constrain
Read-only access, cost caps, semantic layer, data quality checks.
Shadow mode
The agent answers alongside your analysts and you compare. This is where thresholds get tuned against your real questions rather than our assumptions.
Go live on the scoped set
Highest-volume, best-understood questions first.
Monitor and expand
Accuracy against the eval set, question coverage, escalation rate, query cost, and how often answers get corrected — tracked from day one.
What it costs
Almost nobody in this category publishes a price. We do.
What moves the price: number of data sources, whether your metric definitions exist or need building, how many questions and reports are in scope, delivery channels, and compliance requirements.
What's not included: model usage and warehouse compute, billed to your own accounts so you see the real cost per query, and third-party licences.
What you can expect
We'd rather set the expectation properly than quote a number we'd have to defend later.
Recurring questions with stable definitions, scheduled reporting, anomaly alerting on well-understood metrics, and first-pass segmentation. These are high-volume and well-scoped, which is exactly the condition under which accuracy holds.
Open-ended investigation, anything where the right question isn't yet clear, causal claims, and any number going somewhere consequential without review. Agents are good at retrieval and summary; judgement about what a number means is still yours.
[FILL: one real deployment with client-verifiable numbers. This section is honest but unproven without it — and the direct competitor on this SERP has concrete operational detail. A single named deployment does more here than every paragraph above it.]
Built for your team
Data teams
Clear the ad-hoc queue so analysts do analysis. The agent's SQL is reviewable, so it works like a junior analyst rather than a black box.
Operations & finance
Recurring packs and reconciliations that assemble themselves.
Founders & ops leads without a data team
The most common case we see. You have a production database, a Stripe account and a spreadsheet, and no one whose job is answering questions about them.
Security and data handling
Access
Read-only roles, least privilege, scoped credentials per source, full audit log of every query the agent runs.
Retention
[FILL: state your position precisely — whose retention, at which layer, with which providers.]
Deployment
Cloud, your VPC, or on-premise. [FILL: confirm what you genuinely offer.]
Training
Your data is not used to train models. [VERIFY contractually across every provider in your stack.]
Row-level security
The agent respects your existing permissions — someone who can't see a table through the warehouse can't see it through the agent. [VERIFY you implement this. It's a gating question for any enterprise buyer.]
Prompt injection
Data returned from queries and any external content is treated as data, never as instructions.
Certifications
[FILL: certifications you hold. If none, describe the practice and skip the badge line.]
Frequently asked questions
Find out which of your reports could run themselves
Book a free AI audit. Bring your recent ad-hoc requests and recurring reports. We'll classify what's automatable, what needs definition work first, what shouldn't be automated at all, and give you a price from the table above.
Written by Siddhant [FILL: surname], [FILL: role] at Agents Chef
Google Cloud Professional Machine Learning Engineer
[FILL: 2 sentences — production data systems built, warehouses worked with, domains. Verifiable only.] [VERIFY: confirm the exact certification name as issued before publishing]