Agent FleetGallery Let's Talk
FinSight Agent
01FinanceLive

FinSight Agent

FinSight turns raw ledgers and market feeds into board-ready narratives. It forecasts cash positions, flags anomalies before they cascade, and drafts variance explanations analysts can trust.

Watch demo All projects
Close prep
−65%
Anomalies caught
3.2×
Narrative draft
< 4 min

Demo

See it in motion

A short walkthrough of the agent loop — from signal in to decision out.

FinSight Agent demo

Demo reel

Month-end variance walkthrough

2:14

Overview

Case at a glance

Target users: FP&A teams, CFOs, and finance ops who need faster closes without losing control.

01 — Problem

The friction

Finance teams drown in spreadsheets while risk signals arrive too late.

02 — Solution

The approach

An agentic finance analyst that connects to ledgers and market feeds, runs recurring analysis loops, and delivers variance narratives with forecast confidence and risk flags.

03 — Value

Why it matters

Hours of manual reporting compressed into minutes — with audit-ready explanations.

How it works

The agent loop

From raw signal to a decision FinSight can defend.

  1. 01

    Ingest

    Pulls ledger entries, budgets, and market feeds on a schedule, normalizing everything into one analysis frame.

  2. 02

    Analyze

    Runs variance, trend, and concentration checks in SQL, then ranks what actually moved the numbers.

  3. 03

    Explain

    Drafts the narrative with figures, drivers, and confidence — every sentence traceable to a query.

  4. 04

    Alert

    Escalates anomalies and forecast breaches to the owner who can act, with recommended next steps.

Capabilities

What FinSight actually does

Built for FP&A teams, CFOs, and finance ops who need faster closes without losing control.

  1. 01

    Variance storytelling

    Explains what moved, why it moved, and what deserves attention — in language executives actually read.

  2. 02

    Cash & forecast loops

    Projects short-horizon cash and revenue scenarios, then refreshes as new actuals land.

  3. 03

    Risk anomaly radar

    Surfaces unusual spend, margin compression, and concentration risk before month-end surprises.

  4. 04

    Board-ready packs

    Assembles charts, commentary, and recommended actions into a shareable briefing.

Architecture

How the system thinks

Data flows left to right: sources feed the orchestrator, specialists reason in parallel, tools execute, and one artifact comes out.

01

Sources

  • DataLedger & ERP
  • DataMarket feeds
  • DataBudget model
02

Orchestration

  • OrchestratorFinance orchestrator
  • LLMReasoning LLM
03

Specialists

  • AgentVariance agent
  • AgentForecast agent
  • AgentRisk agent
04

Tools & output

  • ToolSQL runner
  • ToolChart builder
  • OutputBriefing pack
  • Data
  • Orchestrator
  • LLM
  • Agent
  • Tool
  • Output

In action

A real exchange

An abbreviated conversation, close to what the agent actually returns.

FinSight Agent
FinSight Agent Session active
  1. You

    Why did EMEA gross margin drop 3 points in Q2?

  2. FinSight Agent

    Three drivers: freight up 1.8 pts on the new carrier contract, discounting up 0.9 pts concentrated in two accounts, and mix shift toward hardware for 0.4 pts. Freight is the only one still trending worse in July.

  3. You

    Draft that for the board deck.

  4. FinSight Agent

    Done — one slide with the margin waterfall, both accounts named, and a suggested renegotiation window for the freight contract. Every figure links back to its query.

Design decisions

Trade-offs worth naming

The constraints that shaped the system — and what was deliberately left out.

  1. 01

    Numbers before prose

    Every narrative sentence is generated from a query result, never from model memory, so any figure can be traced back to source.

  2. 02

    Deterministic math

    Aggregations run in SQL, not in the model. The LLM explains results it cannot silently recompute.

  3. 03

    Bounded autonomy

    The agent proposes; posting to the ledger stays a human action. Write access was deliberately left out of scope.

Honest limits

Where it stops, where it goes

What the agent does not handle today, and what comes next.

Current limitations

  • Forecast horizon is capped at one quarter — longer ranges lose reliability.
  • Requires a mapped chart of accounts; messy ledgers need onboarding work.
  • Narrative depth drops when source systems carry no monthly commentary.

On the roadmap

  • Scenario modelling with driver-level sensitivity
  • Multi-entity consolidation and FX walk
  • Slack-native briefings with follow-up questions

Stack

Technology

Nothing added without a concrete requirement behind it.

PythonLangGraphOpenAIPostgreSQLdbtNuxt