Straight answers.

FLOCORE is an AI Operations Layer for construction. It connects your schedules, PMIS, field activity, and live site signals into one system that:

• Measures lost time, drift, and delay-at-risk continuously.
• Runs operational simulations on top of your project's digital twin.
• Sends signals and recommendations directly to the Control Center, Field App, and Copilot.

We are not just a “dashboard with AI”, but an operational brain that sits on top of the tools you already own.

PMIS, BIM viewers, and BI primarily store data (contracts, drawings, logs, schedules) and display reports/charts, generally after the fact. FLOCORE focuses on daily operations:

• Reading changes in schedules, daily logs, issues, and field signals.
• Calculating lost time and delay-at-risk per zone/activity.
• Runs what-if simulations for delays, supply disruptions, or weather.
• Sending alerts and insights to people who can act – not just weekly reports.

In short: BI answers "what happened?". FLOCORE answers "what will be disrupted if you don't act now?".

We deliberately call it an operational digital twin, not just a 3D model. This means:

• FLOCORE connects BIM / 2D plans / zone mapping with operational data: schedule, progress, issues, safety, logistics, and equipment utilization.
• This twin is updated by real-time data: activity detected from CCTV, drones, and sensors, plus Field App reports.
• Every area on site has a state: working, idle, blocked, unsafe, congested, rework, etc.

The digital twin in FLOCORE is not a “pretty picture”, but an operational canvas used by the machine to calculate risk and run simulations.

No. We have a clear design principle: AI calculates, humans decide.

FLOCORE calculates lost time, delay-at-risk, and simulation results based on data. It presents alternative scenarios and potential intervention points.

However:

• Decisions to add shifts, re-sequence, or accept delays remain with project leadership.
• All recommendations can be explicitly explained (data source, assumptions, and consequences).

AI in FLOCORE is an analytic and decision support tool, not a replacement for site managers or planners.

FLOCORE Copilot is an AI agent embedded throughout the FLOCORE ecosystem:

• Connected to Control Center, Field App, Schedule, Issues, and Digital Twin.
• Understands project concepts: zones, activities, milestones, risk bands, lost time.
• Answers questions like: “Which zone has the most lost time this week?” or “If L4 steel deck delivery is delayed 1 day, what's the impact on the critical path?”

It is designed for voice-first usage in the field. Integration into HandyTalky / radio-systems is a development path architecturally prepared: Copilot can be the "brain" behind communication devices crews already use.

Yes. FLOCORE is designed to utilize various types of inputs, including:

• CCTV & Time-lapse cameras – analyzed with computer vision to detect activity, area density, idle time, and unsafe patterns.
• Drone imagery (periodic) – used for global site updates (earthwork progress, staging area, stockpiles).
• PMIS & Scheduling data – baseline, updates, and activity re-sequencing.
• Logistics & Equipment – material arrival data, crane & heavy equipment status, utilization.
• Weather & environment – for work sensitive to external conditions (pours, lifting, façade).

All converted into operational states per zone/activity used for risk detection and simulation.

Broadly, FLOCORE does three things:

1. Deviation Detection: Compares baseline schedule with actuals (progress, camera activity, field reports). Flags delayed, idle, or blocked activities.
2. Propagation & Impact Analysis: Uses schedule dependencies and WBS structure to see how one disruption flows to other activities and milestones.
3. Simulation & Scenario Testing: Runs “what-if” scenarios: “if this activity slips 8 hours, what is the effect on the next gate?”. Produces delay-at-risk figures (days at risk) and recommends intervention points.

This is not an oracle that is certainly correct, but a machine that consistently calculates the consequences of every deviation.

Traditional planning is usually done before or after major events, relying on manual input. FLOCORE operates in between formal planning cycles:

• It runs every day, even multiple times a day as new data comes in.
• It connects schedule with actual field conditions (cameras, logs, logistics), not just formal reports.
• Allows project teams to run lightweight simulations without editing the master schedule every time.

Result: Planners keep the master schedule, FLOCORE provides a deviation radar and scenario engine that lives between formal planning cycles.

We do not assume field data will ever be “clean”. So FLOCORE:

• Uses multi-source fusion – does not make decisions based on a single input (e.g., only CCTV or only manual logs).
• Flags areas/activities with low confidence and displays uncertainty, rather than forcing a definite result.
• Provides workflows in Field App & Copilot for quick clarification (“is this activity really blocked?”).

For management, this means you see not just “numbers”, but also how confident the system is in those numbers.

We designed the Field App with three principles:

• Replace, don’t add. Replaces WhatsApp photos, manual notes, and long forms with Voice-to-report and Photo-to-issue.
• Immediate local benefit. Site managers and safety officers get daily summaries for toolbox talks and documentation proof for NCRs without extra work.
• Built for harsh reality. Offline-first, minimalist UI, suitable for use with gloves & in noisy environments.

If a specific flow proves unrealistic during pilot, we treat it as a product design error, not field personnel error.

For sites with unstable connections, we:

• Use an offline-first pattern in the Field App – inputs can still be made; synchronization happens when connection appears.
• Ensure the analytic engine in Control Center doesn't “break” just because of a few hours without updates – risks appear with a last timestamp, so users know data freshness.

Thus, field operations don't stop because of internet, and management has an honest understanding of when data was last updated.

Transparency can change dynamics, but our position is:

• FLOCORE is made to reduce the blame game, not heat it up.
• The system gives context: delays are not just “Person A is slow”, but could be due to weather, supply, or resource conflict.
• Data helps discussion become more objective: “these are the facts”, “these are mitigation options”.

We encourage using FLOCORE as a risk & decision support tool, not a tool for hunting individual faults.

FLOCORE is layering, not replacement:

• Connects existing systems: PMIS, scheduling tools, drives, sensors.
• Does not force mass migration or massive data structure changes.
• Pilots are generally limited to 1–2 projects, so integration risk is manageable.

Technically: We use standard APIs and connectors. Integration can be adapted for cloud, hybrid, or on-prem according to company policy.

In principle:

• Data remains yours – FLOCORE only processes and presents.
• Data encryption in transit and at rest.
• Role-based access control – field crew doesn't see things only relevant for executives, and vice versa.
• Audit logs for sensitive activities (login, config changes, critical data access).

For enterprise needs, we provide detailed security & platform documents, and deployment can be discussed to align with internal standards (ISO/SOC, etc).

We are aware of this concern, so our architecture design actually reduces lock-in:

• Data can be exported, both as raw events and aggregate metrics.
• Key metrics (lost time, delay-at-risk, risk bands) are documented so they can be used in other tools if needed.
• Integration to PMIS, BIM, and data warehouse uses open standards.

We want companies to stay because FLOCORE helps operations, not because it's hard to leave.

From the start of the pilot, we agree on cross-functional KPIs:

• Time-to-signal: Average time from issue occurrence to signal appearing at management level.
• Delay-at-risk detected: Potential delay days identified early.
• Admin hours saved: Repetitive work hours (reporting, recapping, data hunting) reduced at field and PM level.
• Improved documentation coverage: Especially for safety, issues, and rework.

Then, we help your team convert that into a financial model (overhead saved, potential fines avoided, increased cashflow predictability). Pilot results are presented as a before/after report for the CFO or board.

You don't need to wait for “perfect”. FLOCORE can start from:

• Existing schedules & PMIS.
• Photos, daily logs, and manual documentation currently scattered.

Initial phase focuses on reducing reporting friction and increasing basic visibility. Building a good data foundation for twin & simulation later. Over time, you can add cameras, drones, sensors, and deeper BIM integration. FLOCORE is designed to grow with maturity, not just for organizations that are already very advanced.

Four aspects make FLOCORE defensible:

1. Domain focus: We focus on construction & field operations, not horizontal platforms.
2. Operational twin & simulation engine: Not just a BIM view module or dashboard; it is a risk and time model living on top of your data.
3. Field-first integrations: Especially voice & Copilot that can live on HandyTalky / radio and other field devices, not just web.
4. Experience playbook: Risk patterns, threshold config guidelines, and mitigation best practices gathered from cross-project implementations.

Even if large vendors build similar features, FLOCORE remains relevant as a neutral and specialist layer that can work on top of various stacks.

Vendor risk must always be considered. We reduce exposure by:

• Architecture allowing data export and metric structure documentation.
• Integration that does not lock you into proprietary non-portable formats.
• Contracts that can include clauses regarding escrow or data access if needed for enterprise scale.

Thus, even if the worst happens, you can still take your data and history insights as a foundation for the next solution.

Because the problems FLOCORE tries to solve – lost time, invisible delays, and slow decisions – are already incurring costs today. We don't ask for massive transformation, just:

• Select 1–2 real projects.
• Agree on measurable KPIs.
• Run a 90-day pilot with controlled technical and political risk.

The main question the pilot answers: Can we see delay risk and lost time faster than we do now, using data we already have and signals we can install today? If yes, you've found a worthy ops layer. If no, you learned without breaking existing systems.