A data analyst for your whole team.
Ask a question in Slack, Teams, or email. Get an answer from your own numbers in minutes, not days, with the chart and the sources attached.
It reads your warehouse and your documents, respects who is allowed to see what, and runs in your AWS account on Amazon Bedrock AgentCore, Amazon's managed service for running AI agents in production.
If your departments' numbers don't agree yet, that's the first project, and we'll say so.
VP Finance9:02
@analyst How did gross margin move from Q2 to Q3, by region? Anything I should flag for Monday?
AnalystAgent9:03
Gross margin fell 1.5 points, from 34.1% to 32.6%. Most of the drop is in the West.
Margin change, Q2 to Q3
points- West: freight cost per order up 22% since the July carrier change.
- Discounts above 15% doubled on two product lines.
- Every other region is within a point of Q2.
Sources: gl_margin_by_region · freight_invoices · Q3 pricing memo (PDF)
The Problem
Simple questions about your business take days to answer.
The fix isn't another dashboard. It's a trusted analyst, available to everyone, in the tools they already use.
Most companies have the data. Getting an answer out of it is the hard part:
- Only one or two people know where the data lives, so every request waits on them
- Dashboards answer last quarter's questions, not this morning's
- Contracts, tickets, and documents sit outside the warehouse entirely
- People stop asking, and decisions get made on instinct
The Hard Lesson
Twenty years of data projects taught us one thing above all: people trust numbers they can check. So every answer shows its sources, its query, and who was allowed to see it.
What It Does
Everything you'd ask of a good analyst
Answers in plain English
Anyone on the team asks a question the way they would ask a colleague, including ones nobody has asked before. The agent works out what's being asked and how to answer it. No SQL, no report request, no waiting in the data team's queue.
Produces real analysis
Comparisons, trends, and what drove a change, with the method explained in a sentence so the reader knows how the number was reached.
Builds charts and tables
Answers come back with the chart or table that makes the point, ready to drop into a deck or forward to the board.
Reads structured and unstructured data
Your warehouse and business systems, plus the documents that never made it into one: contracts, PDFs, tickets, and shared drives.
Works where your team already works
Slack, Microsoft Teams, email, scheduled briefings, or the AI assistant you already use, like Claude, ChatGPT, or Copilot. Nobody has to learn a new tool or remember another login.
Respects permissions
Each question runs with the asker's own access, through the sign-in system you already use. Nobody sees data they couldn't see before.
Shows its work
Every answer cites the tables, queries, and documents it used, so a skeptical reader can check it in seconds.
Multiplayer Analytics
Bring the data to where people already work.
Ask in a shared channel and the whole team sees the same answer, from the same numbers, with the same sources. Follow-up questions build on the thread.
That is what one version of the truth looks like day to day: not a dashboard someone has to remember to open, but an answer in the conversation where the decision is being made.
Slack
Mention the analyst in any channel or send it a direct message.
Microsoft Teams
Same agent, same answers, inside the chats your team already uses.
Claude, ChatGPT, or Copilot
Connect it to the AI assistant your team already pays for, through MCP (Model Context Protocol), the open standard these assistants use to reach your systems.
Forward a question or a spreadsheet and get the analysis back in the thread.
Scheduled briefings
A Monday morning summary of the numbers that matter, delivered before the first meeting.
Web chat and API
A light chat page, or an endpoint your own applications can call.
How It Works
From question to cited answer in five steps
Someone asks
A question lands in Slack, Teams, or email, or a scheduled briefing comes due.
No new front door to learn. The agent meets people in the tools they open every morning.
The agent plans
It works out what the question means in your business terms and which sources can answer it.
Business definitions (what counts as revenue, which customers are active) are agreed with your team up front, so every answer uses the same ones.
It queries and reads
It writes and runs queries against your warehouse and reads the relevant documents.
Read-only by default. The agent looks at your data; it doesn't change it.
It checks permissions and its own work
Access is enforced for the person asking, and the agent sanity-checks results before replying.
If the data can't support a confident answer, it says so instead of guessing.
The answer lands
The reply arrives in the same thread, with the chart, the sources, and a full trace.
Follow-up questions build on the thread, and everyone in the channel sees the same answer.
Platform Foundation
Built on Amazon Bedrock AgentCore, so the plumbing is managed and auditable.
We don't build agent infrastructure from scratch. AgentCore handles the parts that are hard to get right, and we spend the engagement on your data, your questions, and your rules.
Runtime
Managed, isolated compute for each conversation. Nothing to patch or scale.
Gateway
Turns your existing databases and APIs into tools the agent can use, under your rules.
Identity
Acts on behalf of the person asking, through Okta, Entra ID, or the identity provider you run today.
Policy
Checks every action against explicit rules before it runs.
Memory
Keeps the context of a conversation, plus the knowledge you decide it should retain.
Observability
Step-by-step traces of every question, query, and answer in Amazon CloudWatch.
Evaluations
Scores answer quality against real questions, so drift gets caught early.
Code interpreter
A sandbox where the agent runs the analysis and draws the charts.
Inside Your Boundary
Your cloud, your controls.
Your account, your region, your keys
The agent, its memory, and its traces stay inside your AWS environment.
No training on your data
Model calls through Amazon Bedrock are isolated. Your data doesn't train anyone's model.
Every answer is auditable
Who asked, what ran, what was read, and what came back.
Read-only by default
Any action that writes anywhere is gated, logged, and reversible.
No lock-in
Model and framework are swappable: Strands, LangGraph, the Claude Agent SDK, or your own.
Is It a Fit?
Where this works best, and where to start instead.
A good fit when
- Your team has more questions than your data team has time to answer
- Your data lives in a warehouse, or in systems with APIs your team can describe
- Someone can own the definitions, like what counts as an active customer
- You know who should see what, even if it isn't written down yet
Start somewhere else when
- Every department's numbers disagree and nobody owns the fix
- Key data is trapped in spreadsheets or a system nobody can export from
- Your warehouse is a legacy platform that already struggles to keep up
An agent is only as trustworthy as the data under it. If yours isn't ready, we'll say so and start with the data foundation.
The Engagement
Typically six to eight weeks, design week included.
Your timeline is set during scoping. More data sources, channels, or permission rules can add time, and we'll say so before you sign.
Week 1
Design
Questions, data sources, channels, permissions, and success criteria agreed.
Weeks 2–3
Build
Agent and data connections stood up. A test set of real questions built with your team.
Weeks 4–5
Harden
Permissions, policy rules, traces, and evaluation runs. Working demo in your channel.
Weeks 6–8
Tune and hand off
Two rounds of tuning on your feedback, then infrastructure code, runbook, and sign-off.
From your team
A product owner, a data contact, and a few reviewers who know the questions.
Review cadence
Two review rounds per deliverable, five business days each.
Acceptance
A pass rate on real questions agreed up front, not a subjective read.
Yours on day one
Source code, prompts, infrastructure code, test results, and a runbook.
Start with a 30-minute call.
Bring five questions your team asks every week. We'll tell you whether your data is ready to answer them, what a first version would look like, and how long it would take.