AI assistants for the construction industry

AI assistants for developers and construction companies: documents, estimates, procurement, site supervision and project summaries in one workspace.

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AI layer

An AI assistant must take action, not just reply with text

Core thesis of the AI block: a pilot with measurable impact, private data under control, agent actions logged, quality passes evals before scaling.

1-2 hof daily routine the assistant takes off an employee or manager

2-4 wksenough for a pilot on one process with an impact metric

40%of agentic AI projects Gartner expects to be canceled without clear value

Assistant ≠ chatbot

A chatbot answers; an assistant checks the regulations, queries systems, records the deviation and proposes the next step.

Control plane

Agent registry, owner, permissions, memory, evals, trace logs, kill-switch and budget at the enterprise-layer level.

Data

RAG returns an answer with a source citation; LLM Gateway obfuscates personal data before the model and restores it after the response.

Processcorporate memoryagentaction in the systemlogs and evals

Internal links

Agent governance layer RAG with sources LLM Gateway and personal data

Sources

Anthropic: Building effective agents Gartner agentic AI cancellations Chip Huyen: AI Engineering

Which assistants we deploy

The design documentation assistant detects discrepancies across design, working and as-built documentation and helps the team avoid losing decisions in correspondence. The estimate-and-contract assistant reconciles estimates, contracts, supplementary agreements, KS-2, KS-3 forms, limits and actual volumes.
The procurement assistant surfaces shortages, delivery delays and the impact of purchasing on the site schedule.
The construction control assistant gathers remarks, photos, reports and contractor statuses into a managed list of deviations.
The project manager assistant prepares a daily summary of deadlines, budget, risks and decisions that need to be made.

How it works within the developer's loop

We don't add a chat on top of chaos. First we connect the data sources: 1C, ERP, the common data environment, BIM, DWH, contractor reports, email, EDI and project repositories. Then we set up permissions, context, regulations and quality criteria. The AI assistant operates within the boundaries of the process: it prepares summaries, finds deviations, explains causes and hands disputed decisions over to a human. This approach supports loose coupling of systems: assistants help manage the site but do not turn 1C, BIM or the common data environment into a new monolith.

How we start the rollout

The first step is to choose one process with a measurable effect: closing out remarks, KS form verification, procurement, construction control or the project manager summary. In 2-4 weeks you can assemble a pilot loop: data sources, roles, a knowledge base, verification scenarios and metrics. The goal of the pilot is not a demo for the sake of a demo, but a solution that demonstrates reduced manual reconciliation, faster response to deviations and clear accountability across the site.

Contacts

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