01 · COMMON TRAP
The Demo Trap
- Looks convincing in a demo, then struggles in day-to-day operations.
- Pilots run for months with no clear path to launch.
- Security, approvals, and integration show up too late.
Operational AI Services
Operational AI for workflows, software, and connected systems.
We ship AI into messy real-world operations.
Pragmatic over theatrical. Real
outcomes not demos.
Outcomes:
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Faster Decisions
Less Manual Work
Safe Automation
WHY AI PROJECTS STALL
Built for real operations
- We start with the workflow that is already slowing people down.
- We connect the system to the tools, data, and approvals it needs to work.
- Security and launch planning are built in from day one.
Services
Productized services.
Outcomes, not
technology.
Seven ways we engage — from a scoping sprint to production AI systems. Each one is shaped around a concrete outcome and an honest pilot plan.
01
Start Here
AI Opportunity Sprint
Find the one or two AI use cases worth building first.
In 3–6 weeks, we review your workflows, data, and bottlenecks, then give you a clear pilot plan with scope, effort, and expected impact.
02
Team Productivity
AI Assistants for Internal Teams
Help your team find answers and complete routine work faster.
We build secure AI assistants for support, operations, sales, and engineering using your documents, tickets, and internal systems.
03
Workflow Automation
AI Automation for Repetitive Work
Automate multi-step tasks without losing human control.
We design workflows where AI handles the repetitive steps, and your team steps in only for approvals, exceptions, or final review.
04
System Connections
AI Connected to Your Systems
Make AI useful inside the tools your business already runs on.
We connect AI to your CRM, ERP, warehouse, or custom software so it can find information and take action safely.
05
Forecasting & Decisions
Predictive Models for Better Planning
Improve forecasts, spot problems earlier, and route work more accurately.
We build prediction, scoring, and anomaly-detection models when they deliver better business results than a generic chatbot.
06
Smart Devices
AI for IoT and Connected Products
Turn device data into faster decisions and smarter products.
We add AI to connected devices and sensor-based systems for monitoring, prediction, and on-device intelligence.
07
Custom AI Solutions
If your use case does not fit a standard package, we design a solution around your workflow, systems, and constraints, starting with a clear pilot scope.
Industry Blueprints
01
Typical Pain
- Teams collect live data from meters, sensors, and connected devices but struggle to act on it quickly
- Users still jump between dashboards to understand consumption, asset status, and system behavior
- Expected energy usage is hard to forecast clearly enough for planning and customer conversations
How AI Fits
We connect meter streams, sensor data, and device events, then use AI assistants and forecasting models to explain system behavior, surface anomalies, and show expected energy usage ahead.
AI Outcomes
- Faster answers from live operational energy data
- Clearer visibility across connected assets and devices
- Better planning from energy usage forecasts and anomaly signals
02
Typical Pain
- Dispatch teams replan routes and schedules manually across too many systems
- Equipment issues are found after sensor drift or downtime has already started
- Quality checks still depend on paper forms, photos, or manual review
How AI Fits
We combine telematics, maintenance logs, and dispatch or warehouse data to recommend schedules, detect equipment anomalies, and surface the next best action for operators.
AI Outcomes
- Smarter dispatch decisions from live operational data
- Earlier warning signs before unplanned downtime
- Quicker and more consistent quality review
03
Typical Pain
- Tenant requests arrive through email, portals, and phone with no consistent triage
- HVAC and building issues are handled reactively after comfort complaints appear
- Property, tenant, and service data is scattered across BMS, CMMS, and ticketing tools
How AI Fits
We connect to your BMS and ticketing systems, analyze HVAC, occupancy, and service data, and use AI to triage requests, detect anomalies, and plan maintenance earlier.
AI Outcomes
- Faster tenant response with better request routing
- Earlier maintenance signals from building systems
- Better visibility across property operations
04
Typical Pain
- Device telemetry is collected but not translated into clear operator actions
- Firmware events and rollbacks are hard to monitor across deployed fleets
- Cloud-only decisions add latency where faster edge response is needed
How AI Fits
We ingest telemetry, firmware events, and sensor streams to run anomaly detection or edge inference, then push alerts and recommended actions into your operations console.
AI Outcomes
- Earlier warning signs from connected devices
- Faster response to device health issues
- Smarter real-time decisions closer to the device
05
Typical Pain
- Employees search across documents, tickets, ERP, and CRM systems to answer routine questions
- The same data is entered manually into multiple systems
- Approvals and compliance checks slow down routine work
How AI Fits
We index documents, tickets, ERP or CRM records, and approval flows so assistants can answer from trusted sources and trigger workflow steps with human approval where needed.
AI Outcomes
- Faster access to trusted internal answers
- Less manual admin across teams
- Shorter approval and review cycles
How We Work
From messy reality to production.
A simple 5-step path from first discovery to a live system your team can actually use.
Discuss a pilot →Typical Pilot
€20K-€40K · 6-9 weeks
Clear scope and
success metrics
01 / 05
Discover
Choose the workflow worth fixing first
SHIPS
Priority Assessment
02 / 05
Map
Understand the systems, data, and blockers around it
SHIPS
Delivery Plan
03 / 05
Design
Define what success, quality, and safety look like
SHIPS
Success Criteria
04 / 05
Pilot
Build and test the first working version
SHIPS
Working MVP
05 / 05
Scale
Launch it properly and monitor live performance
SHIPS
Production Launch
End of Journey · Beginning of Value
Selected Work
Challenge
Volts works with live data from meters, sensors, and connected devices. The challenge was to make that data easier to understand, demonstrate, and act on while also predicting future energy consumption.
What We Built
We developed an LLM assistant for the Volts system and a predictive energy model that forecasts future electricity consumption. The result is an AI layer that moves their users away from tedious UX to a single point of contact that provides context, detects problems and turns raw energy data into actionable context.
- Stefan B. (Team Lead)
AI WORKFLOW
DATA
CONTEXT
FORECAST
REASON
ACTION
Challenge
Gas price volatility makes daily trading decisions difficult. NeuralTrade needed a reliable forecasting engine that could process multiple market signals and give users a clearer view of the next-day price movement.
What We Built
We built a machine learning forecasting engine for next-day gas price movement inside the NeuralTrade platform. It combines market signals into a clearer daily outlook, helping users compare scenarios and make trading decisions with more context.
- Simeon Kuninski (CTO)
MARKET INTELLIGENCE WORKFLOW
DATA
SIGNALS
MODEL
FORECAST
DECISION
Built to Fit Your Systems
Works with the tools, data, and platforms you already use.
AI Workflows
How it works
ML & Statistics
Models, CV, statistics
Application Layer
Services & product apps
Data & Search
Storage & retrieval
Cloud & Deployment
Hosting
How We De-Risk AI Delivery
01
Same engineers from scope to rollout
The people who scope the pilot stay involved through build, integration, and production readiness.
02
Every pilot starts with a measurable test
Before build starts, we define the baseline, success metric, and review checkpoint.
03
Integration is part of the work
A pilot includes workflow touchpoints, interfaces, and system connections, not just a model demo.
04
Human approval where it matters
Higher-risk actions stay gated until the workflow is proven in live use.
05
Deployment around your constraints
We design around your infrastructure, security rules, data boundaries, and hardware environment from the start.
06
Clear scope before code
Every engagement starts with the use case, systems touched, deliverables, owners, and next-step decision.
Frequently Asked Questions
Do we need our data perfectly clean before starting?
No. We start with the data you already have and figure out what is good enough for a first pilot. Part of the work is cleaning, structuring, and connecting the missing pieces so you do not spend months preparing before learning anything.
How do we know if AI is worth doing for our business?
AI usually pays back fastest when these three things are true:
- You have a repetitive workflow with clear steps, approvals, or decisions.
- You have enough volume for the gain to matter, usually 500+ cases, requests, or tasks per month.
- Your data already exists in digital systems, documents, tickets, or sensor streams, or there is a practical way to start capturing it.
If the workflow is strong but the data layer is weak, we can help define what needs to be captured first, including sensors, devices, and hardware where needed, before recommending a larger pilot.
How long until we see something working?
Most engagements reach a working pilot in about 6 weeks and a production-ready release around week 9, depending on integrations and approvals. You should see something concrete early, not wait until the end for a reveal.
Will our data be used to train public models?
No. Your data stays inside the agreed environment and is not used to train public models. We can work with your infrastructure and security requirements from the start.
How much time do you need from our team?
Usually a small group from operations, product, or IT for discovery, feedback, and approvals. We do the heavy lifting, but we need quick access to the people who understand the workflow best.
What happens after deployment?
We hand over a documented system your team can run, with monitoring and clear ownership. If you want, we can continue with support, improvements, and model upkeep, but you are not locked into us.
How do you reduce wrong or made-up answers?
We keep the scope tight, connect the system to trusted sources, and add checks for higher-risk actions. For important workflows, people stay in the loop until accuracy is proven in real use.
Meet the AI Team
What are you excited about?
Let us know.
We'll
reach out
to you within 24 hours.
