Everyone gets the A-TeamA-Team
Every person at Dubit works with a team of AI agents behind them.
We built over 100 specialist agents, each trained on our institutional knowledge — the methods, the proprietary data, the standards our best people have developed across thousands of projects. They handle execution: data analysis, research synthesis, quality checks, report generation.
Our people handle the work AI can't do: understanding your problem, shaping strategy, creative thinking.
That's the A-Team. Every project gets it.
Try one of our agents
→ Searching 10 years of research data...→ Found 5 matching survey waves (n=47,000)→ Building age-controlled query...→ Query validated ✓→ Analyzing results...US: 34% daily TikTok usage (ages 8-12)UK: 28% daily TikTok usage (ages 8-12)Source: Dubit Trends, Q3 2025 (n=12,400)Our Research Analyst is one of over 100 agents in the A-Team. We've made it available for you to try.
Ask it a question about young people's media habits. It searches a decade of youth research. Builds the database queries. Validates the statistics. Returns the finding with the evidence behind it.
A question that takes an analyst hours comes back in minutes.
This demo uses a sample: recent data on children aged 1–15 in the US and UK. Ask about platforms kids are shifting to, content types that are growing, or how habits differ by age, gender, and country.
How an agent works
An LLM is intelligent. What it lacks is experience doing the job and tools to do it with.
We give it experience — written best practices our team has developed across thousands of projects. How to structure a presentation. How to interpret survey data. How to check statistical claims. Twenty years of institutional knowledge, made explicit.
We give it tools — to search our databases, build queries, generate reports, create slides, check its own outputs against the source data.
That's what makes it an agent. It reads the brief, decides how to approach it, uses the tools to do the work, and adapts based on what it finds. Not a script. Not a template. An agent working through the problem.
Experience
Tools
Team Member
‘Analyze gaming trends by age group’
Orchestrator
Routes to the right specialist
Data Analyst Agent
Has autonomy to solve the problem
Deterministic
Query Builder
Formatter
Calculator
LLM-Powered
Semantic Search
Intent Classifier
Sub-Agent
Validation Agent
Stats Checker
Sample Validator
Agents working together
One agent handles one job well. Real work usually needs several — so our agents direct other agents, combining specialists for the task.
The Insight Finder shows what this looks like. It discovers patterns in our research data. It doesn't query the database itself — it directs the Data Analyst to do that. Decides what to look for, instructs the Data Analyst to run queries, evaluates the responses, determines what to explore next. When it thinks it's found something, it runs the finding through a 5-gate verification before accepting it.
In a single run, the Insight Finder executes 50+ database queries and produces work that would take an analyst days.
The same pattern runs across the A-Team — agents for reports, presentations, campaigns, each assembling the right specialists.