THE INTELLIGENT PAUSE BEFORE TRANSFORMATION
We help small and mid-sized enterprises streamline operations, improve customer experiences, and grow with secure, cost-efficient AI solutions.
Not a vendor bolted onto your business, but the layer where friction becomes function — where an idea stops waiting and starts running.
The intelligent pause before transformation.
Valuable teams are often trapped between expensive development, disconnected tools, manual processes, and uncertainty about how to introduce AI safely. BufferInc turns these obstacles into a practical transformation roadmap.
Specialist development can cost approximately €80–€120 per hour, making experimentation difficult. (indicative range)
Repetitive approvals, data entry, and document handling slow down otherwise capable teams.
Employees lose time copying information between tools and responding to predictable requests.
Decision-makers need clarity about value, risk, data protection, and implementation.
Many SMEs cannot justify building a full internal AI engineering team.
We begin with the bottleneck, map the data and workflow, then build the smallest reliable system that creates meaningful operational value.
What it does
Provides contextual, personalized answers across common customer questions and can escalate complex cases to a human.
Outcome
Routine questions resolve immediately, and your team keeps the cases that genuinely need a person.
Typical integration points
What it does
Supports 24/7 appointment booking, common inquiries, lead qualification, and structured call summaries.
Outcome
Enquiries are captured and qualified around the clock instead of ending in voicemail.
Typical integration points
What it does
Produces relevant outreach based on a prospect's industry, company website, and approved sales positioning.
Outcome
Sales teams send outreach that reflects the prospect, at a pace a person alone cannot sustain.
Typical integration points
What it does
Helps draft emails, offers, proposals, summaries, and internal reports while preserving human review.
Outcome
First drafts arrive in minutes and people spend their attention on judgement, not formatting.
Typical integration points
What it does
Provides permission-aware answers grounded in approved internal PDFs, emails, knowledge bases, and wikis.
Outcome
Institutional knowledge becomes searchable in plain language, without widening who can see what.
Typical integration points
What it does
Monitors equipment and operational signals to identify anomalies and possible failures earlier.
Outcome
Maintenance can be planned around early signals instead of reacting to an unplanned stop.
Typical integration points
Three representative shapes of work. Each one moves the same way: a problem you can name, a system that addresses it, an outcome your team can act on.
Illustrative use case only — not medical advice. Any real diagnostic system requires clinical validation, regulatory review, privacy controls, and human oversight.
Outcomes described qualitatively. No performance figures are claimed.
These are not five separate initiatives. They are one operating system: automate the repeatable, sharpen the decisions, and return the difference to your people.
Automate repeatable work across support, administration, and data processing.
Use predictive insight for forecasting, prioritization, and risk assessment.
Deliver faster, more consistent, and more relevant interactions at scale.
Discover patterns in customer and market data that can guide new offers and growth.
Give employees more time for judgment, relationships, and creative problem-solving.
Five stages, each with a defined output. You always know what is being built, what it costs, and how success is measured.
01
Identify the bottleneck, users, data, and success criteria.
02
Map the workflow, safeguards, user experience, and technical architecture.
03
Deliver a focused pilot with measurable acceptance criteria.
04
Connect approved systems, train users, and document operations.
05
Monitor quality, cost, adoption, and new opportunities.
These are the service principles we design against. They are commitments about how systems are built and operated — not a substitute for your own legal review.
Architected for GDPR-conscious deployment, with infrastructure and data-handling choices documented for each engagement.
Data flows, lawful basis, and retention are mapped as part of the design phase rather than retrofitted after launch.
Deployment on German or EU infrastructure, including AWS Frankfurt where appropriate for the workload.
Transport security and encrypted storage as a default posture across services and backups.
Systems inherit your existing permission model. An assistant never widens who can see what.
Confidentiality agreements and DPAs are put in place where the engagement requires them.
Retention controls, audit logging, and a defined human review point for consequential outputs.
An operational goal we build toward. This is not a current certification and is not presented as one.
Three commercial models. Which one fits depends on whether you are proving a use case, running one, or continuously improving several.
From €15,000
Defined use case, discovery, implementation, testing, and launch.
Most common
Maintenance from €1,000/month
after the initial project
Monitoring, improvements, support, and agreed operational maintenance.
Custom pricing
Ongoing evaluation, knowledge updates, workflow improvements, and model/cost optimization.
Final pricing depends on integrations, data readiness, security requirements, and workflow complexity. Every engagement begins with a clearly documented scope.
Discuss Your Use CaseTell us where work is slowing down. We will help you identify the clearest path toward an intelligent system.
Buffering before breakthrough.