CONXAI

No-code agentic AI platform for construction document and jobsite-data automation

Cloud-based agentic AI platform for the construction industry. Its DocNostic module extracts and cross-references information from specs, RFPs, submittals, and drawings to automate document review; its SiteLens module analyzes jobsite photos and video to generate automated progress, labor, and equipment-utilization reports.

€7.7M total disclosed (€2.7M pre-seed, January 2022, led by Earlybird UNI-X fund and Pi Labs with participation from noa/A-O PropTech and Argonautic Ventures; €5M Series A, April 2026, with Earlybird, Pi Labs, noa, and Zacua Ventures) raisedGrowth Stage

Founded 2020 · Named case-study customers: KAJIMA Corporation (major Japanese general contractor; 70% reduction in document-processing time via DocNostic, 10+ use cases configured across Japanese sites), Hensel Phelps (top U.S. general contractor; DocNostic used for document review and scope validation), and Hilti (global construction-tools manufacturer; SiteLens used to automate firestop-selection, 50% workflow-time reduction). Total number of customers not publicly disclosed. A third-party revenue estimate ($2.9M ARR for 2024, per Getlatka, which also describes the company as "bootstrapped") is inconsistent with CONXAI's disclosed VC funding and is not independently verified, so it is not used here as a maturity signal.

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WHY IT EXISTS

Why This Tool Exists

The Problem

Construction teams generate large volumes of unstructured jobsite and document data - daily photos and video, specifications, RFPs, submittals, drawings, and sensor readings - but CONXAI's own marketing states that roughly 90% of project data goes unused and 30% is lost once a project closes out. Manually cross-checking submittals against specs, reviewing bid/tender documents, or scanning site photos for progress and issues consumes significant skilled-staff time, and the resulting knowledge is rarely captured for reuse on future projects.

The Solution

CONXAI provides a no-code agentic AI platform that ingests unstructured construction data and turns it into structured, searchable knowledge and automated workflows. Its DocNostic module extracts and cross-references information across specs, drawings, RFPs, and submittals to automate document review (KAJIMA Corporation reports a 70% reduction in document-processing time). Its SiteLens module analyzes jobsite photos and video with a fine-tuned computer-vision model to generate automated progress, labor, and equipment-utilization reports and flag issues (Hilti reports a 50% reduction in a firestop-selection workflow using SiteLens).

USAGE

How You Use It

Delivery Method

SaaSAPI

Integrations

Not publicly specified - the platform page lists "API interoperability" as one of five architecture pillars and a published AWS case study confirms production AI inference runs on Amazon EKS, but no named third-party software integrations (e.g., Procore, Autodesk Construction Cloud) were found on official CONXAI sources

Disciplines

Construction

Workflows

Data & Automation

Project Phases

Bidding/Procurement, Construction Administration

Project Types

Commercial, Infrastructure, Industrial

TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input

What it needs

Required:Construction documents (specs, drawings, RFPs, submittals) for DocNostic, Jobsite photos and video for SiteLens

Optional:Sensor readings, CAD files, Project schedule/budget data (used for SiteLens metrics such as Cascade/Burndown and Line of Balance analysis)

Formats:Not publicly specified - official sources describe accepted content types (documents, images, video, CAD) but do not list specific file formats or extensions

Output

What you get

Format:Structured reports and dashboards, with extracted data also accessible via API

Fields:Extracted and cross-referenced document data (spec/RFP/submittal comparisons, discrepancy flags), Site condition, labor-deployment, and equipment-utilization reports, Lean-construction metrics (Cascade/Burndown analysis, Line of Balance), Tagged, searchable visual assets (photos/video), Critical-issue notifications

Algorithm

How it works

Model:Proprietary "Neuro-Agentic Reasoning Architecture" combining a construction-domain ontology, multi-modal AI, and an agentic-graph workflow layer. For jobsite imagery (SiteLens), CONXAI has published via an AWS case study that it uses the OneFormer segmentation model, fine-tuned on a proprietary dataset of 50,000+ self-labeled construction-site images, recognizing 40+ construction-specific object classes.

Accuracy:No independently verified precision, recall, or accuracy benchmark has been published. Case studies report business-outcome metrics rather than model accuracy: KAJIMA cites a 70% reduction in document-processing time (50% during its proof-of-concept); Hilti cites a 50% reduction in a firestop-selection workflow via SiteLens.

Privacy

How your data is protected

Retention:Per CONXAI's general privacy policy, collected customer data is deleted after order completion or termination of the business relationship; contact-form data is retained until deletion is requested. The platform page states customer project data is kept under strict multi-tenant partitioning and "retained exclusively for that customer," but no specific retention period (days/years) for SiteLens/DocNostic project data is publicly disclosed.

Training:Not publicly specified whether customer project data is used to train or fine-tune CONXAI's models.

Compliance:ISO 27001 (claimed on platform page), SOC 2 Type II (claimed on platform page; a Sprinto-powered Trust Center lists security-policy categories but requires an access request to view underlying audit reports/certificates), GDPR compliant (per privacy policy and platform page)

API

Integration

Endpoint:Contact vendor for API access

Method:Not publicly documented (platform page lists "API interoperability" as one of five architecture pillars; no public API reference or documentation found)

USE CASES

Use Cases

·Automating bid/tender document review by extracting and cross-referencing data from specs, RFPs, and submittals (DocNostic)
·Flagging discrepancies and missing information across large, multi-format construction document sets, including non-digital/scanned formats (e.g., KAJIMA use case)
·Jobsite photo and video analysis for automated progress, labor, and equipment-utilization reporting (SiteLens)
·Automating complex technical product-selection workflows from jobsite photos (e.g., Hilti firestop selection)
·Preserving searchable institutional knowledge from site imagery and documents that would otherwise be lost after project closeout

PRICING

Pricing

Free

Not publicly specified - no self-serve free tier or trial advertised; site describes a "low cost of entry" and per-use-case modular deployment

Pro

Not publicly specified - no self-serve pricing tier; deployment and pricing are use-case/module-based via sales engagement

Enterprise

Custom pricing via demo/sales contact ("Book a Demo"); scales from a single use case to enterprise-wide deployment

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Research Sources & Data QualityLast verified: 2026-07-17

Data Sources (14)

Verified Data (10)

problem, solution, deliveryMethod, yearFounded, founders, headquarters, fundingTotal, compliance-claims (ISO 27001/SOC2/GDPR), algorithm-details (SiteLens computer-vision model via AWS case study), named-customer-case-studies

Not Found (7)

api-documentation, pricing-tiers, third-party-software-integrations, input-file-formats, aggregate-customer-count, data-training-usage-policy, specific-data-retention-period

Our Commitment: We only include verified data from official sources. If information isn't publicly available, we mark it as "Not publicly specified" rather than guessing.

ALTERNATIVES

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