Natural-language search
Engineers can ask questions across project documents.
TCE Document Intelligence System
We replaced manual PDF hunting with a source-cited AI search workflow for bid packages spanning thousands of pages. Engineers can move from a question to the exact supporting paragraph without giving up judgment.
Enterprise AI Document IntelligenceInfrastructure / Engineering
83%
estimated reduction in document search time
Based on engineer interviews and client estimates, not formal usage logs or a controlled pilot.
60 → 10
estimated minutes spent searching per engineer per working day
Includes time spent reading AI-generated answers. Based on engineer interviews and client estimates.
5k–50k
pages in large bid-document packages
Typical large New York State bid-document range from the source context.
The Starting Point
TCE engineers review dense bid-document packages for large infrastructure projects. Before this system, finding a requirement or checking context meant searching PDFs one at a time with Command-F, working across local files, or returning to printed pages and highlighted notes.
A Search Problem With a Trust Requirement
A plausible AI answer was not enough. Engineers needed to verify every response against the original documents, while TCE needed the system to respect the way projects, teams, and permissions already worked.
Thousands of technical pages
A single bid package can span 5–20 PDFs and roughly 5,000–50,000 pages.
Evidence over black-box output
Every answer needed citations, page references, exact paragraphs, and highlighted source text.
Enterprise access rules
Documents and projects had to remain scoped to the right admins, managers, users, teams, and roles.
Our Role
We worked across product strategy, AI workflow design, enterprise search, full-stack development, and the admin and permission model. The central product decision was to help engineers reach evidence faster, not automate the engineering judgment that follows.
Define the trusted workflow
We shaped the experience around the questions engineers ask and the evidence they need before acting.
Design the AI system
We connected question answering and summarization to exact source passages instead of unsupported responses.
Deliver the product around it
We built the user experience, document and project management, and enterprise permission controls as one system.
What Adamant Code Built
TCE needed a faster way for engineers to search huge bid-document packages. Adamant Code built an AI document intelligence system with citations, page references, exact paragraphs, and enterprise permission logic.
Source-cited answers
Answers include page references, exact paragraphs, and highlighted sources.
Enterprise controls
Admins, managers, users, teams, and project permissions are managed inside the system.
From Question to Verifiable Answer
The workflow keeps the speed of AI and the traceability of the original bid documents in the same interface.
1. Ask in natural language
An engineer asks a project question without translating it into keywords or opening every PDF.
2. Retrieve the supporting evidence
The system searches the project’s document set and builds an answer from relevant source material.
3. Inspect the exact source
Citations link the response to its page, paragraph, and highlighted passage for quick verification.
4. Apply engineering judgment
The engineer reviews the evidence in context and decides how it should inform the work.
The Product Beyond the Answer
The search experience sits inside a broader enterprise product. TCE can organize the source material, control access, and manage the people and projects that use it.
Document and project management
Teams can organize bid documents around the projects where engineers need them.
Source-cited search and summaries
Answers and summaries carry their supporting pages and paragraphs into the review workflow.
Team and role permissions
Admins can assign users, managers, teams, and document access at the appropriate level.
Azure-aligned deployment
The deployment approach was designed around TCE’s existing enterprise environment and sensitive data requirements.
The Outcome
The system is in production and still used by TCE. Based on engineer interviews and client estimates rather than formal usage logs or a controlled pilot, average document-search time fell from around 60 minutes to around 10 minutes per engineer per working day. That is an estimated 83% reduction while keeping the original evidence visible.
What This Proves
For expert teams, useful AI is not the system that sounds most certain. It is the system that helps people reach the right evidence faster, understand where an answer came from, and keep judgment in human hands.
Planning a source-cited AI search product?
If your team works across large, technical document sets, we can turn the search problem into a trusted product workflow, from source evidence and permissions to the final interface.
Product walkthrough
Inside the product
1 / 5
New Chat
Natural Language Search
AI Summarized Result
List of sources
Source Paragraph Highlight
01
Engineers start with a project-specific document library and ask a question in natural language.
Client video testimonial
Patrick Besser, Data Engineer, TC Electric shares their experience working with Adamant Code.
From 15-Minute Searches to Instant Answers
Patrick Besser, Data Engineer, TC Electric
What TCE Says
A TCE engineer on why answers tied to their source matter.
“The enterprise chatbot can answer that question and provide multiple sources in an instant with highlighted sources. So that's been a huge value for our company!”
Patrick Besser
Data Engineer
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