A knowledge base of past bids
Over 5,000 of the company's past bids, drawn from the documents and notes in its Zoho CRM and organized so they can be searched by scope.
A nationwide asphalt and concrete paving contractor priced every bid by hand from old bids and spreadsheets. True Cedar built an AI bidding agent inside the Zoho CRM the company already runs on. From a new job's scope, it returns a near-instant bid price and recommendation, grounded in comparable winning bids from the same area. It was delivered in under one week.
Client name withheld. Timelines reflect this specific project and are not a guarantee for other projects; every engagement is governed by its own written statement of work and agreement.
| Client | A nationwide asphalt and concrete paving contractor |
| Before | Estimators priced each bid manually from old bids and spreadsheets |
| What we built | An AI bidding agent integrated into Zoho CRM |
| Knowledge base | Over 5,000 of the company's past bids |
| Delivered | In under one week |
| Where it lives | A button on the opportunity record in Zoho |
Each new bid started the same way. An estimator opened old bids and spreadsheets, looked for jobs that resembled the new one, and priced it by hand.
The company's pricing history was valuable, but it sat in years of past bid documents rather than anywhere an estimator could simply ask it a question. Every bid meant digging that history out again.
Over 5,000 of the company's past bids, drawn from the documents and notes in its Zoho CRM and organized so they can be searched by scope.
Given a new job's scope, the agent finds comparable winning bids from the same area and returns a bid price and recommendation in seconds.
Estimators click Generate Draft Bid on the opportunity record, and the suggested price, confidence and full draft are written back to that record.
Every draft shows line items, unit costs, a target-margin price, a confidence score and flags for any gaps in the data.
When there is no comparable basis for a price, the agent flags the gap instead of guessing at a number.
New bids are added to the knowledge base daily, and a backtest checks the agent's pricing against past bids.
The company did not adopt a new system. The agent arrived inside the one its estimators already used every day, as a button on the record they were already working in.
Nothing goes out automatically. The agent produces a draft; an estimator reviews it and decides. A second calculation works in the other direction, starting from a target margin and returning the price that achieves it.
The Generate Draft Bid button was working against the company's live Zoho fields five days after work began. Refinements based on the estimators' feedback followed, and the agent has continued to improve since, including weighting comparable jobs by how close they are to the new one.
The agent runs on a Next.js application, a PostgreSQL database with vector search, and leading AI models, connected to Zoho CRM.
Connecting new capability to the systems you already run.
Agents that do real work inside your workflow.
Replacing work your team does by hand from old files.
Tell us how the work gets done today. We will tell you whether an agent like this fits, and what it would take.