TCE · Enterprise AI document search · About 3 months
Document search engineers can verify
Document search for a New York infrastructure company, where every answer points to the exact page and paragraph it came from.
~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 searching per engineer per working day
Includes time spent reading AI answers. Engineer interviews and client estimates.
Live
in production and still used by TCE
Status in October 2026. No active-user count is published.
1The answer, with its citations
2The source, passage highlighted
The challenge
A search problem with a trust requirement.
A plausible AI answer was not enough. Engineers had to verify every response against the original documents, and the system had to respect how TCE’s projects, teams and permissions already worked.
Scale
Thousands of technical pages per bid package.
Evidence
Evidence over black-box output: citations, page references, exact paragraphs and highlighted source text on every answer.
Access
Enterprise access rules for admins, managers, users, teams and projects.
My role
I ran this end to end, from the first conversation through discovery with TCE’s bid-review engineers, scope, solution design and leading the build with the Adamant Code team, to launch. The central product decision was mine to make early: help engineers reach the evidence faster, and leave the engineering judgment that follows with them.
- First call
- Discovery
- Scope & proposal
- Solution design
- Build lead
- Launch
- Iteration
The AI brings engineers to the evidence. The decision that follows stays with them.
The workflow
From question to verifiable answer.
- Ask in natural language, without translating the question into keywords or opening every PDF.
- The system retrieves the relevant passages from the project’s document set and builds an answer from them.
- Citations link the answer to its page, paragraph and highlighted passage.
- The engineer reviews the evidence in context and decides how it informs the work.
The product beyond the AI
More than an answer box.
Document and project management
Packages organised by project, so each search runs against the right set of documents.
Source-cited search and summaries
Answers and summaries with citations, page references and the exact source paragraph highlighted.
Team and role permissions
Admin, document, project, team and role permissions that follow how TCE already works.
A deployment around TCE’s environment
An approach designed around TCE’s Azure environment and sensitive enterprise data.
The outcome
In production, still used.
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, an estimated 83% reduction, with the original evidence still in view.
~83%
Estimated reduction. Engineer interviews and client estimates, not usage logs or a controlled pilot.
60 → 10
Estimated minutes per engineer per working day, including time spent reading AI answers.
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 BesserData & Systems Engineer, TC Electric
