Deployed inside your environment — your data never leaves

The AI analyst your enterprise can actually trust

AMOS connects to your company's data and tools, answers business questions with verified analysis, and produces graphs, reports, and presentation slides. The model proposes the work — AMOS controls access, runs the numbers, and checks every claim.

amos — analysis workspace

question Why did weekly active usage drop in the EMEA region last month?

permitted context compiled — 3 sources, permission-filtered

4 SQL queries executed by deterministic workers

12 claims verified against evidence

report, 6 charts, and slide deck compiled

decision-ready package delivered · 1 causal claim held for human review

AMOS is an internally deployed analyst system that connects to company data and tools, answers business questions, performs verified analysis, and produces graphs, reports, and presentation slides.

The problem

An AI analysis can be wrong even when the SQL runs

Models are already capable of proposing SQL, analyses, and explanations. The bottleneck for AI analysts inside enterprises isn't model capability — it's trust: controlling what the AI can access, verifying its work outside the model, and tracing every conclusion back to evidence.

Stale metric definitions

The query runs, but it computes revenue the way your company defined it two quarters ago.

Data the user shouldn't see

A model with broad credentials happily reads tables the person asking was never permitted to access.

Incomplete or shifting data

The answer was built on a partial load or a schema that changed yesterday, and nothing flagged it.

Unsupported causal claims

“Churn rose because of the price change” sounds confident, but nothing in the data actually supports it.

Conclusions that silently rot

The source data changes after publication, and last week's board slide is now quietly wrong.

No trail back to evidence

When a number is questioned, nobody can show which queries, definitions, and data versions produced it.

How it works

The model proposes. AMOS decides, executes, and verifies.

AMOS sits between the analyst agent and your company systems. The model never controls access and never becomes the source of truth.

Business question
Local analyst agent
AMOS control layer
Data connectors + workers
Verification & evidence
Reports, charts & slides
01

A question comes in

An employee asks a business question, or a recurring analysis kicks off on schedule.

02

A local agent proposes the work

An analyst agent deployed in your environment interprets the request and drafts a plan, queries, and tool calls. It never holds credentials to your systems.

03

The control layer authorizes it

AMOS compiles only the context the requester is permitted to see, checks the plan against policy, and issues narrow, short-lived capabilities.

04

Deterministic workers run the numbers

SQL, statistics, and charting run in verified workers against your connectors — outside the model, with hard limits and full records.

05

Every claim is verified

Results are checked against schemas, metric definitions, and freshness. Each material claim is typed, cited, and linked to its evidence.

06

Decision-ready artifacts ship

A deterministic compiler renders the approved narrative into charts, slides, PDF reports, dashboards, and spreadsheets — replayable when sources change.

Why AMOS

Built as an operating layer, not a chat wrapper

Runs inside your environment

AMOS ships with a locally deployed analyst agent. Your data, your definitions, and your permissions stay inside your infrastructure.

Permission-first by construction

Access filtering happens before context is ever assembled. The model only sees what the person asking is allowed to see.

Calculations outside the model

Authoritative numbers come from deterministic SQL, statistics, and charting workers — never from a language model's token stream.

Every claim traced to evidence

Each material claim is typed and linked to the queries, metric definitions, and data versions that support it, with freshness information.

Human review where it matters

High-impact judgments — like causal explanations — are routed to a review queue for approval, rejection, or correction before publication.

Replayable and change-aware

When source data or definitions change, affected conclusions are invalidated and can be replayed and revalidated — published work doesn't silently go stale.

Deliverables

Not a chat answer. A decision-ready package.

Ask a question or schedule a recurring analysis, and AMOS returns the complete deliverable an analyst would hand you — with the working shown and every claim accounted for.

  • A direct answer and executive summary
  • Verified findings with stated limitations
  • Tables and publication-quality graphs
  • A PowerPoint presentation
  • A PDF or HTML report
  • Spreadsheets and machine-readable results
  • The queries, metric definitions, and data versions used
  • Claim-level citations and freshness information
  • Assumptions, open questions, and required review decisions
  • A replayable record that can be revalidated when sources change

Our story

The same analyst workflow, over and over

While studying data analytics at Carnegie Mellon, our founder kept seeing the same pattern across every project — a bank-sponsored next-best-action system, internal tools, analytics engagements. The outputs differed, but the work was identical: translate a business question into the right data and definitions, clean and reconcile, run the right analysis, verify it, and present it in a form decision-makers could use.

Existing AI tools could help with isolated steps like writing SQL. But nothing existed that a company could trust to perform the complete analyst workflow inside its own walls — respecting permissions, computing with reliable tools, and connecting every conclusion to evidence.

AMOS is that missing operating layer. We are four technical founders who write every line of the product ourselves. In our first month we built a working Rust prototype that controls data access, executes and verifies analyses, preserves supporting evidence, and produces reviewable results — and pressure-tested the approach with nine data leaders, analysts, and technical experts alongside CMU faculty.

4

technical founders, all writing code

1 month

to a working verified-analysis prototype

9

data leaders and experts interviewed

See AMOS on your own workflows

We're working with early design partners who have a data warehouse, recurring analyst queues, and business teams waiting on reports. If that sounds like you, we'd like to talk.

Request a demo

contact@amos.com