AI Transform LLC
The AI Employee that takes a sales rep from site visit to client-ready proposal in under 20 minutes, so a 12-person team quotes like one three times its size.
Venkat Veswanath
CEO, AI Transform LLC
“Our reps were quoting the same job three different ways. We needed numbers our CFO could sign off on, not numbers that sounded right. Dojo Labs built it so every estimate runs through the same pricing engine, line item by line item, every rep, every time.”
Venkat Veswanath
CEO, AI Transform LLC
Measurable Outcomes
that drive ROI.
~85%
Prep Time Reduced Per Estimate
12
Sales Reps Onboarded
99%+
Pricing Match vs Manual Quotes
< 3 wks
Time to Value
By integrating our computation layer, AI Transform LLC transformed from a services-heavy model to a scalable, automated platform.
Client Overview
About AI Transform LLC
AI Transform LLC delivers AI-powered business solutions to mid-market clients across construction, contracting, and enterprise verticals. Led by CEO Venkat Veswanath, the company runs a 12-person distributed sales team handling everything from small renovation bids to multi-million-dollar project estimates.
Industry
Construction & Contracting
Client
Venkat Veswanath, CEO at AI Transform LLC
Team Size
12 sales agents (field + remote)
Engagement Type
Internal Sales Tooling, Build & Integration
The Problem
The Challenge
Before we came in, every estimate at AI Transform was a manual exercise built on tribal knowledge. A rep visiting a site would pull scattered pricing spreadsheets up on a phone, cross-check material costs and labor multipliers from memory, drive back to the office to format a proposal, and hope the next rep handling a similar job arrived at a similar number.
Pull scattered pricing spreadsheets up on a phone during site visits
Cross-check material costs and labor multipliers from memory
Drive back to the office to format a proposal, often hours later
Hope the next rep on a similar job arrived at a similar number
2 to 3 hours of prep per estimate cycle, with quotes drifting between reps
The Core Problem
A sales team only closes as fast as it can quote. Every estimate burned 2 to 3 hours of a rep's day on spreadsheet lookups and proposal formatting, capping how many jobs 12 people could chase, and the numbers still drifted from rep to rep. They did not need another chatbot. They needed a Employee that does the estimating work for every rep, on every job, with numbers the CFO could sign off on.
What We Built
Our Solution
We built an AI Employee, a three-tool estimation suite, that takes any rep from site visit to client-ready proposal in under 20 minutes, with every number traceable to the same pricing source. It does the prep that used to eat a rep's afternoon, so the team spends its time selling.
01. Site Estimation Engine
An interface where reps enter site details (project type, square footage, material categories, labor scope, location) and the system returns a structured estimate with low, mid, and high breakpoints, line-item justified. We architected this so the LLM parses intent and context while our computation layer does the math. These are separate systems on purpose.
Input: project type, square footage, material categories, labor scope, location
Processing: LLM parses intent, computation layer queries the internal pricing DB
Output: low, mid, high estimate breakpoints with line-item justification
02. Meeting Preparation Assistant
A brief generator that produces structured pre-meeting documents in under two minutes: project background, comparable past jobs from internal records, talking points calibrated to project type and budget, and risk flags for scope creep and pricing volatility.
Generates structured meeting briefs in under 2 minutes
Surfaces comparable historical projects from internal records
Produces talking points calibrated to project type and budget
Flags scope creep and pricing volatility risks
03. PDF Proposal Generator
A one-click pipeline from locked estimate to branded client-ready PDF. Line items, ranges, scope summary, and assumptions all populate from the same data the estimate was built on. Designed to plug into AI Transform's existing CRM stack in Phase 2.
Branded PDF output with line items, ranges, and assumptions
Auto-generated scope summary
Designed to plug into the existing CRM stack in Phase 2
Replaces 1 to 2 hours of manual document formatting per proposal
Tech Stack
Technologies Used
| Layer | Technology | Role |
|---|---|---|
| Backend API | Python / FastAPI | Service layer, routing, business logic |
| LLM (Reasoning) | Anthropic Claude | Natural language parsing, brief generation |
| LLM (Fallback) | OpenAI | Cross-validation and redundancy |
| Compute Layer | Deterministic Python Engine | Pricing math, range calculation, formula logic |
| Data Store | PostgreSQL | Internal pricing reference database |
| Cloud | AWS (EC2, S3, Lambda) | Hosting, storage, serverless triggers |
| PDF Generation | ReportLab / WeasyPrint | Branded proposal output |
| Auth | JWT + Role-Based Access | Agent-level permissions, admin controls |
Why We Built a Computation Layer
A construction estimate is real arithmetic: material quantities times unit prices, labor hours times rates, margin applied last. An LLM asked to do that produces a number shaped like the right answer. Sometimes it is. Often it is not. You will not know which until the client signs and the job runs over.
So we built a computation layer that intercepts every numerical request and routes it through a deterministic Python engine. It queries the same pricing database, applies the same formulas, and returns the same result every time.
Part of the engagement was building the TCO and ROI case for the computation layer itself, quantifying the cost of inconsistent quotes against the cost of building the system. The math worked.
The Transformation
Before & After Dojo Labs
Before
2 to 3 hours per estimate cycle
Manual spreadsheet lookups
Quotes that drifted between reps on similar jobs
Post-visit document formatting
Tribal knowledge dependency
After
Under 20 minutes from site visit to PDF
Pricing pulled from a single source of truth
Standardized output across all 12 reps
One-click branded proposals
Meeting briefs generated on demand
Roadmap
What's Next
We architected the system from day one to support deeper CRM integration. Phase 2 extends the suite directly into AI Transform's CRM stack:
Auto-generated proposals triggered by deal stage changes
Estimate history and rep performance tracking
Real-time pricing sync as reference data updates
Mobile-first interface for on-site use
This phase is an extension of what we built, not a rebuild.
Ready to build something like this?
Book a 30-minute call. We'll discuss where your AI handles numbers, identify hallucination risks, and map out your computation layer.
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