Tambot, an AI-Powered Market Analysis Tool That Stopped Drowning in Spreadsheets

Impact

Project Overview

A TAM report looks simple from the outside. Pick a market, find the numbers, write the analysis. In practice, it means pulling data from a dozen sources that don’t agree and manually assembling everything into a format a client can trust.

Tamlab, a US-based market research and consulting team, had built a strong reputation on the quality of its TAM work. The problem wasn’t the analysis; it was the hours it consumed. Every report meant the same cycle of manual data collection, source checking, assumption building, and formatting.

Tezeract built Tambot: a custom LLM-powered market analysis tool that lives inside Excel, automates data collection and report drafting, and gives analysts a structured, source-tagged first draft in minutes rather than hours.

What Changed

The result was a 70% reduction in manual research effort and a report turnaround that went from half a day to under fifteen minutes.

Customer Profile

Tamlab is a US market research and consulting team focused on market sizing and growth advice. Their work often starts with one question clients ask in different ways: how to calculate total addressable market for a new product, a new region, or a new segment.

A Research Process That Couldn't Scale

Primary Problem

Tamlab’s analysts knew how to size a market. The problem was that every total addressable market calculation required the same manual groundwork: find the relevant sources, check whether the numbers were current, reconcile definitions, pull competitor signals from wherever they happened to live, and then assemble everything into a client-ready format from scratch.

At 4–6 hours per report and 5–10 reports per week, Tamlab was burning 20–60 analyst-hours per week on largely repetitive work. The analysis itself, the judgment calls, the assumption-setting, and the strategic framing were a fraction of that time. The rest was data collection and formatting.

Secondary Challenges

The manual workflow created reliability problems that were harder to spot but just as damaging:

Conflicting source definitions

Different reports used different geographies, buyer segments, and time periods for the same market

Untracked assumptions

When data was thin or missing, analysts made judgment calls and moved on, with no mechanism to flag which parts of a report rested on weak inputs

Scattered competitive intelligence

Competitor signals were collected ad hoc and stored inconsistently

Inconsistent methodology between analysts

Without a standardized validation layer, report quality varied by who ran the workflow, which made it difficult to defend the methodology to clients

Business Impact

Tamlab’s value to clients lies in the quality and speed of its market intelligence. A process that couldn’t scale without degrading one or both of those things was a direct threat to the business. The question wasn’t whether to change the workflow; it was how to change it without disrupting the analytical rigor that made the work worth paying for.

Turn Your Market Research Into a 15-Minute Workflow

If your team is still spending hours pulling data, validating sources, and formatting reports, it’s time to rethink the process. Tambot shows how repetitive research can be automated without losing control over analysis.

Why Tezeract

Tamlab evaluated their options carefully before committing to a build. Adding analyst headcount would scale output but not efficiency, the same manual steps would simply be distributed across more people. Licensing a generic market intelligence platform meant forcing their specific workflow into a tool designed for a different use case.

What Tamlab needed was something that fit their existing Excel-first workflow, preserved analysts’ control over assumptions and outputs, and automated the time-consuming steps without adding analytical value.

Tezeract was selected based on a clear technical plan for multi-agent LLM design, Excel integration, and a delivery roadmap aligned with Tamlab’s release goals. The solution was shaped around real business use, not generic templates, with a focus on production-ready systems that support daily analyst work. This project also connects with our work in building scalable Agentic AI services and AI agent development services for teams that want to automate research, reporting, and decision support using structured AI workflows.

Tambot — LLM-powered market analysis tool

Tambot is a custom AI market analysis tool built as an Excel add-in with a Python/FastAPI backend and a multi-agent LLM architecture. It takes the inputs an analyst already has, industry, region, year, market notes, and returns a structured, source-tagged TAM report draft without the analyst leaving their spreadsheet.

How Navex Works

One agent finds relevant sources for AI-powered TAM analysis
One agent pulls numbers and context, then tags them to the right section.
One agent analyzes trends based on market numbers and CAGR, highlighting those that justify the numbers
One agent drafts the report so teams can run a clean total addressable market calculation without copy and paste

The Multi-Agent Architecture

Rather than routing every task through a single model, Tambot uses a coordinated agent workflow where each agent handles one job and hands off to the next:

Agent 1 - Source Discovery

Identifies relevant market data sources based on the industry, region, and year parameters.

Agent 2 - Data Extraction and Tagging

Pulls market size figures, growth rates, and trend signals from identified sources. Tags each data point to the report section it belongs to.

Agent 3 - Assumption Validation

Reviews the extracted data for gaps, mismatched definitions, and weak inputs. Flags sections where the underlying data is thin and marks assumptions that carry higher uncertainty.

Agent 4 - Report Drafting

This is what AI agents for business can AI-powered TAM analysis looks like when it’s built around the actual steps of a market research workflow.

Stop Repeating the Same Research Work Every Week

Market reports should not start from scratch every time. With a workflow like Tambot, your team can reuse structure, automate data collection, and focus only on insights that matter.

Phases wise Deployment

Tambot was delivered across a four-month window, structured so Tamlab could test each layer before the next one was built on top of it.

Workflow Mapping and Input Design

The team started by documenting Tamlab’s actual report process, including the manual steps, the source-checking habits, and the formatting conventions clients expected.

Key Milestone: Input schema defined. Report template agreed. Source quality criteria documented.

API and Excel Add-In

The FastAPI backend was built and connected to the Excel add-in. The input-to-output pipeline was established end to end, with basic data collection running before the agent architecture was layered on top.

Key Milestone: Excel add-in connected to backend. Basic pipeline returning structured output.

Multi-Agent Logic and First Draft Reports

The four-agent workflow was built and integrated. First real market inputs were processed and reviewed against Tamlab’s manual reports to calibrate output quality.

Key Milestone: Multi-agent workflow live. First draft reports reviewed and passing quality bar.

Hardening for Public Release

This phase focused on reliability, not new features. API performance under concurrent requests was optimized. Scraping logic was tightened to handle niche markets where data availability was thin. The platform was prepared for public use beyond Tamlab’s internal team.

Key Milestone: Platform stable under load. Public release criteria met. Tamlab team signed off.

Obstacles Countered and Resolved

Obstacles

API performance degraded when multiple fields were processed simultaneously for report generation.

Niche markets had thin or inconsistent data availability across sources

Extraction noise from web sources slowed the review process and reintroduced manual work

Balancing automation depth with analyst control over final outputs

Market definitions varied between sources, undermining TAM calculation consistency.

Resolution

Input validation and caching were tightened so the backend processed requests in a predictable sequence without bottlenecks

Scraper logic and source filtering were refined to prioritize high-signal data and flag reports where source coverage was limited

Filtering rules were tightened, and a structured “accept” checklist was developed with the Tamlab team, so review loops were fast and consistent

The architecture was designed so agents produce a draft, not a final report, analyst review and assumption adjustment remained in the workflow

The validation agent was trained to identify definition mismatches and surface them as flagged assumptions rather than silently accepting conflicting inputs

The Results

Tamlab started seeing results as soon as the first report runs were tested during delivery, then improved each week as more markets were added to the workflow.

Before Tambot, analysts building TAM reports spent days pulling data from scattered sources, formatting it manually, and still delivering estimates that were hard to defend in a boardroom.

Tambot cut that process down to minutes.

For Business Analysts

Generate structured TAM, SAM, and SOM reports without manual data aggregation

Pull market sizing data and competitive context from multiple sources in one run

Spend time interpreting results, not formatting spreadsheets

Deliver analysis faster without sacrificing depth or accuracy

For Leadership Teams

Access market intelligence on demand, without waiting on analyst turnaround time

Make investment and expansion decisions backed by structured, repeatable data

Reduce reliance on expensive third-party research reports for early-stage sizing

Get consistent output format every time, regardless of who runs the analysis

For Excel Power Users

Run AI-powered market analysis directly inside the tools they already use

No new platform to learn. The plugin fits into existing workflows

Export clean, presentation-ready reports without reformatting

Automate the repetitive parts of research without losing control of the output

Build an AI Agent That Works Inside Your Workflow

The real impact comes when AI fits into the tools your team already uses. Like Tambot inside Excel, we build solutions that work where your data already lives.

Building Tambot With Our Advanced AI Technology Stack

React js

Fast API

Python

OpenAI

ChatGPT

Claude

MS Excel

Selenium

Playwright

Perplexity

SERP API

GCP

Tools & Technologies

Description

Frontend Development

Application Backend

AI & Data Processing Server

Database

Cloud Infrastructure

Development Tools

Key Capabilities Built

Intelligent Crawling

Tamlab uses automated data collection to pull market signals from many sources, then sorts them by industry and region. This supports TAM report automation by reducing manual lookup work and keeping inputs consistent for each report run.

Forecasting

Tamlab applies multi-agent LLM workflows to review past market signals and project likely trends. This improves AI-powered TAM analysis by adding a clear forecast layer that teams can use while doing a total addressable market calculation.

Hosted Excel Plugin

Tamlab runs as an Excel plugin for market analysis that connects to a hosted backend. Users enter key details in Excel, then trigger Automated TAM report generation to produce a structured output without leaving their spreadsheet.

Benefits teams gain when Tamlab automates market sizing in Excel

Tamlab helps teams reduce manual research work and deliver TAM outputs faster, with more consistent inputs. It brings data collection, analysis, and report drafting into one Excel-first flow.

Sizing New Markets for Clients

With Tambot, the research layer is handled automatically. The consultant enters the market parameters, reviews the structured draft, adjusts the assumptions that require judgment, and delivers a polished report in a fraction of the time. This is what a market research AI agent looks like when it’s built around a consultant’s actual workflow.

Market Entry Evaluation

Tambot gives your business development team a structured first draft with source attribution fast enough to inform the conversation while it’s still happening. The AI-powered TAM analysis layer means the numbers come with context, not just figures.

Managing High Report Volume

The same workflow that produces one report produces ten, with consistent quality. For teams looking to understand how AI is reshaping data analysis and business intelligence, Tambot is a concrete example of what that looks like in practice.

Build Your Own Market Research Automation Tool With Tezeract

Tambot shows what happens when a TAM report automation tool is designed around the actual steps of a market research workflow, not a generic AI layer dropped on top of an existing process. The automation handles the repetitive work. The analyst handles the judgment. The client gets a better report, faster.

If your team is spending analyst hours on data collection that could be automated, Tezeract can map your current workflow and build a custom tool that fits it. Share your report format and we’ll put together a concrete build plan.

Frequently Asked Questions

How to calculate total addressable market for a B2B product?

Start by writing your market definition in one sentence. Pick a method: top-down (industry size then filter) or bottom-up (accounts × price). Use both when you can. List assumptions like geography, buyer type, and adoption rate. Then build a range, not one number. Keep every input tied to a source or a clear assumption. A repeatable template makes this faster and reduces error.

What is a total addressable market calculation, in plain terms?

It is a structured way to estimate the maximum revenue you could earn if you served all customers who fit your target market. It includes your market definition, data sources, assumptions, and the math. A strong model shows how you got the number and what would change it.

Why do TAM reports take so long to create?

Most time goes into manual data collection, cleaning, and rewriting the same sections. Teams jump across tools and sources, then redo work for each new market. Automation shortens this by collecting inputs, drafting sections, and keeping a standard format.

What is TAM report automation and what does it automate?

TAM report automation reduces manual steps like searching for market data, pulling competitor context, and drafting the report. A good setup also flags weak assumptions, keeps sources attached, and generates a first draft fast. Teams still review and decide. The tool removes repeated tasks.

What is Automated TAM report generation inside Excel?

It means you enter inputs in Excel, then trigger a workflow that collects data, runs the model, and produces a structured report output. This fits teams that already use spreadsheets for pricing, segments, and scenarios. It cuts copy and paste work and keeps the process consistent.

What is an LLM-powered market analysis tool used for?

It helps turn messy market text and data into structured outputs like a TAM report draft, a list of sources, and a set of assumptions. It can also summarize competitor moves and market signals. The best tools keep traceability so users can verify inputs.

How does AI-powered TAM analysis improve accuracy?

Accuracy improves when inputs are consistent and assumptions are checked. AI can flag missing data, spot mismatched definitions, and keep the same steps across reports. It does not remove the need for business judgment. It makes the model easier to audit and update.

What is automated competitive intelligence and why does it matter for TAM?

It is a system that gathers and summarizes competitor signals, like product changes, pricing pages, hiring trends, and positioning. For TAM, it helps you avoid sizing a market in a vacuum. It can also highlight constraints like geography, buyer type, and adoption barriers.

What is competitive intelligence automation in practice?

In practice it means automatic collection plus a repeatable summary format. It can include source links, change tracking, and short briefs by competitor. The goal is faster understanding, not more noise. It works best when it feeds the same report structure every time.

What should leaders ask before approving a TAM automation build?

Ask: What decisions will this support? What inputs must be traceable? What are the required sources? Who reviews outputs? What is the target time per report? What will be public-facing, if anything? Also ask how you will measure success, like hours saved, report consistency, and fewer missed deadlines.

Can an automated market research platform replace analysts?

It should not replace analysts. It should reduce the repeated work that slows them down. Analysts still define the market, review assumptions, and make calls on edge cases. Automation improves speed, consistency, and documentation.

How do you handle niche markets where exact data is missing?

Use bottom-up modeling, proxy data, and clear ranges. Write each assumption, explain why it is reasonable, and show sensitivity, like what happens if adoption is 1% vs 3%. A good workflow flags gaps and keeps the model easy to update when new data arrives.

What is the best way to validate TAM assumptions automatically?

Use checks like: source freshness, geography match, segment match, and unit consistency. Compare multiple sources and mark conflicts. Track which sections rely on weak assumptions. Automation can highlight risk areas so the reviewer focuses where it matters.

Why build an Excel plugin for market analysis instead of a web app?

Many teams already run segments, scenarios, and pricing in Excel. A plugin keeps the workflow in one place, reduces tool switching, and fits existing templates. A web app can still be useful for collaboration. The right choice depends on where your team already works.

What does “Automating TAM report creation with AI” look like in a rollout?

Start with one report format and a short list of target markets. Build a working draft flow, then add validation rules and source tracking. Run parallel tests against manual reports. Train users on inputs, review steps, and how to edit assumptions. Expand to more segments once the process is stable.