Gobbler

Summary

Gobbler is a AI data analysis companion designed to help public officers make sense of messy, semi-structured data, such as complaints, cases, surveys, and free-text submissions in a transparent and responsible way.

Instead of producing “answers” like a chatbot, the system guides users through a schema-first, analysis-driven workflow:

• Documents are parsed and extracted into structured data objects

• Users define (or confirm) the fields that matter

• An AI agent plans and executes real analytical methods (e.g. clustering) in a sandboxed Python environment

• The agent’s planning steps and code are visible, so users can understand the methodology, assumptions, and limitations

The product is intentionally positioned as an augmentation tool, not a replacement for analytical thinking. It is optimised for sense-making and early-stage analysis, helping officers build intuition, surface patterns, and ask better questions — rather than generate audit-ready reports.

Problem Statement

Public officers want to use data to inform decisions, but time-to-first-insight is too long, leaving little space for actual analysis and judgment.

In practice:

• A large share of time is spent preparing data before any insight can be drawn

• Studies consistently show ~60–80% of analysis time goes into data preparation, not analysis

• Many officers lack confidence in analytical workflows, making exploration slow and error-prone

Generative AI lowers the barrier to getting answers, but introduces new risks:

• Users may trust outputs without understanding the data or method

• Important assumptions and limitations are hidden

• Data literacy does not improve — dependency increases

The result is a double bind:

• Too much time spent getting to first insight

• Too little understanding of how insights are produced

The opportunity is to shorten time-to-first-insight while strengthening data literacy, so officers can spend more time analysing, questioning, and applying judgment — not just preparing data or trusting black-box answers.

Our Solution

A three-layer, AI-augmented approach that reduces time-to-first-insight while strengthening data literacy.

1. Schema-First Foundation

Users define or confirm via auto-detection the fields that matter before analysis begins. This grounds the work in shared definitions, reduces ambiguity, and ensures users retain ownership of what the data represents.

2. AI-Augmented Analysis

An AI agent plans and executes real analytical methods (e.g. clustering) in a sandboxed Python environment. The agent’s plans and code are visible, helping users understand the methodology rather than blindly trusting outputs.

3. Sense-Making, Not Final Answers

The tool is explicitly positioned for early-stage exploration:

• Pattern discovery

• Hypothesis generation

• Gut-sense checks

It supports human judgment at the front of the analysis process, not report-grade conclusions, reinforcing that AI augments thinking rather than replaces it.

Initial User Feedback

We demoed an early working prototype to public officers to showcase the end-to-end flow: from schema setup, data extraction, to AI-augmented analysis. These sessions primarily informs how users reasoned with the tool, rather than to validate individual features.

Key feedback and observations

• Users do not know the difference between a RAG solution and our proposed solution

• Schema setup can be tedious

• Hallucination risk increases when users don’t understand the data

• Concerns from team leads that users don’t internalise the analysis process and they will over time, trust outputs blindly

• Intent is easily lost if the AI “feels too capable”

Design responses

• Add schema auto-detection to ease setup

• Show agent planning and code to surface thinking, not hide it

• Encourage engagement with data wrangling as part of sense-making

• Reinforce that AI does not know which data points matter without guidance

• Reframe the tool as an assistant, not an analyst

Clear use-case boundary

• Strong fit: market sensing, complaints, surveys, case analysis

• Weak fit: heavy quantitative analysis, compliance, definitive reporting

Auto-detection of schema eases setup action by users

Gobbler writes code and execute them in a sandbox to analyse your extracted data objects.

What’s Next?

Immediate (Next 2 weeks):

• Continue to gather additional user feedback from demo day and user testing

• Implement early user feedback on improvements on price filtering and other trending events

Short-term (1-2 months):

• Launch alpha version hosted on GCC and expand user testing

• Comprehensive evals for AI analysis companion

• Improve parsing and data extraction speed using concurrent workers

• Data visualisation agent tools

Long-term (3-6 months):

• Gobbler-as-a-middleware use case exploration

• Explore integration with government digital services ecosystem