AI That Runs Your Company Day to Day
AI works the back office across marketing, sales, customer service, support, finance, and operations. It runs on an AI Operating System so each automation compounds into the next. The customer experience stays human, the operations belong to AI.
95%
Of enterprises see no measurable return on GenAI
67%
Vendor-partnered builds succeed vs 33% internal
90 days
From first agent live to compounding wins
The Challenge
Why Most Back-Office AI Never Pays Off
Manual back-office load eats the week
Teams spend up to 60% of the week cleaning supplier files in Excel, PDF, and CSV. Half the SKUs are missing descriptions or attributes. The work is real, recurring, and it never gets ahead of itself.
AI bolted on as standalone chatbots
A bot shines in the demo and never wires into the actual workflow. Five teams run five stacks with no shared owner, so AI stays a pile of disconnected experiments instead of a system that runs the company.
Gains that reset each project
Correct a mistake today and the next project makes it again. With no learning loop and no named owner, every automation starts from zero. The capability evaporates the moment the project ends.
AI Plays, Organized by the Function They Run
Each play wires AI into a live function instead of standing beside it. We start with the one that hurts most, prove it against a KPI, then let it feed the next.
AI for Marketing
Campaign personalization off live data, not last-click attribution from six weeks ago. Unified customer profiles built from store visits, web behavior, marketplace orders, and support tickets, with segments rebuilt on the last 48 hours and brand tone respected.
AI for Sales
Lead scoring, proposal drafting, follow-ups, and pipeline hygiene run by agents that sit inside the CRM. The team spends its time in conversations that need a human, and the busywork around them runs on its own.
AI for Customer Service and Support
Deflection, agent assist, and routing wired into the live support stack. AI handles the repetitive load and surfaces the right answer, while every customer-visible reply passes through a human on a defined cadence.
AI for Finance and Back-Office
Reconciliation, anomaly detection, and cash-flow work that today lives in spreadsheets. Supplier files in any format get normalized against your master schema, and only true exceptions reach a person.
Retail Operations AI
The six retail plays where they fit: GEO discoverability, dynamic pricing within brand-floor rules, product enrichment at SKU scale, supplier listing, marketplace growth, and campaign personalization. The same enriched catalog the agents produce also makes the brand findable when AI answers.
Internal Ops Automation
RAG over internal documents, transcription, and process automation for the work that crosses every department. Centralized builds with named owners, so capability lands in production and stays there instead of being rebuilt by each team.
AI Use-Case Map
Where to Start: Adoption Against Impact
Map each operations use case by how ready your team is to adopt it and how much it moves the business. Start top-right, prove it, then work outward.
Start here: high adoption, high impact
Ready to run and moves a real number. These are the wedge plays that earn trust and fund the rest.
Tap to expand
Build toward: low adoption, high impact
Big payoff, but the team or data is not ready yet. Sequence these once the operating model is in place.
Tap to expand
Quick wins: high adoption, low impact
Easy to roll out and useful, just not the headline. Good for momentum and for building the default-to-AI habit.
Tap to expand
Hold: low adoption, low impact
Not worth the build effort yet. Park these so the team protects its time for the plays that compound.
Tap to expand
Click any quadrant to explore AI use-cases
The Approach
The AI Operating System for Back-Office Work
A three-layer operating model that turns one-off automation into compounding capability across every function. Process inventory and consolidated knowledge underneath, a skill layer with a learning loop in the middle, the assistant and workflow tooling on top.
Process inventory and knowledge
Before automating anything, inventory the processes across every department and pull org-wide knowledge out of the connectors and files it is scattered across. This is the foundation the top 5% never skip.
- ✓Full process inventory across every function
- ✓Org-wide knowledge consolidated into a usable form
- ✓Governance, GDPR, and EU AI Act mapping from the start
- ✓Humans stay 100% responsible for AI output
Skill registry with named owners
A maintained registry of reusable, process-specific skills. Each skill has a named owner from the relevant department who keeps its recurring workflow current, so know-how has an accountable home.
- ✓Reusable skills registered per process
- ✓A named Skill-Owner drawn from any function
- ✓Centralized build over expecting everyone to build their own
- ✓80/20 discipline: 20% of effort for 80% of the value
The learning loop
The loop captures process-specific know-how so a correction made once is reused everywhere. This is what turns each automation into a head start on the next instead of a fresh build.
- ✓Corrections persist and propagate across the org
- ✓Each function builds on the one before it
- ✓Adoption measured from Activity to Anchoring to Impact
- ✓Capability compounds instead of resetting
What Manual Operations Actually Cost
Manual back-office work does not hold steady. It compounds as catalogs, suppliers, and channels grow, and the team falls further behind every quarter.
Teams spend up to 60% of the week cleaning supplier Excels, PDFs, and CSVs, and half the SKUs are missing descriptions or attributes. The build-versus-buy split tells the same story: internal builds succeed about 33% of the time against 67% for vendor partnerships. The discipline that closes the gap is integration into the live workflow, governance, and a learning loop, which is exactly what a partnered build brings.
Supplier-file work, before and after
From 60% of the week to about 6% exception routing
Agents ingest any supplier format, normalize it against your master schema, flag conflicts, and route only true exceptions to a human. Pays back in roughly 3 months. Build-vs-buy: 33% internal success against 67% vendor-partnered.
Boost.space, The State of AI in E-commerce 2026; MIT NANDA, The GenAI Divide 2025.
From Process Inventory to Compounding Operations
A 90-day path that ships a live agent fast, then layers the operating model so each function builds on the last. Every cycle ends with measurable impact and a scale-or-stop decision.
Step 01
Process and Knowledge Inventory
Inventory the processes across functions, find where manual work consumes the most time, and consolidate the scattered knowledge that AI will run on. Pick the painful, measurable problem to automate first.
- ✓Process inventory and AI potential map
- ✓Knowledge consolidation plan
- ✓Prioritized use case with the KPI defined upfront
Step 02
First Agent Live
Stand up the first agent inside the real workflow, not as a standalone bot. Apply the 80/20 rule so 20% of the build delivers 80% of the value, with a human in the loop on every customer-visible output.
- ✓One production agent wired into the live workflow
- ✓Human approval cadence on customer-facing output
- ✓Baseline measurement against the defined KPI
Step 03
Scale and Second Function
Expand the first agent to the full scope and onboard a second function. Set up the skill registry with named owners and the learning loop so capability compounds instead of resetting.
- ✓Skill registry with named owners
- ✓Second function automated and integrated
- ✓Reporting tied to one business KPI
Step 04
Compound and Measure Impact
Bring a third function online, embed governance and the operating model, and instrument adoption from Activity to Anchoring to Impact. Close with a business review on time saved and net-new capability.
- ✓Adoption measurement framework live
- ✓Operating-model and governance handbook
- ✓Quarterly business review with scale-or-stop decision
Step 01
Process and Knowledge Inventory
Inventory the processes across functions, find where manual work consumes the most time, and consolidate the scattered knowledge that AI will run on. Pick the painful, measurable problem to automate first.
- ✓Process inventory and AI potential map
- ✓Knowledge consolidation plan
- ✓Prioritized use case with the KPI defined upfront
Step 02
First Agent Live
Stand up the first agent inside the real workflow, not as a standalone bot. Apply the 80/20 rule so 20% of the build delivers 80% of the value, with a human in the loop on every customer-visible output.
- ✓One production agent wired into the live workflow
- ✓Human approval cadence on customer-facing output
- ✓Baseline measurement against the defined KPI
Step 03
Scale and Second Function
Expand the first agent to the full scope and onboard a second function. Set up the skill registry with named owners and the learning loop so capability compounds instead of resetting.
- ✓Skill registry with named owners
- ✓Second function automated and integrated
- ✓Reporting tied to one business KPI
Step 04
Compound and Measure Impact
Bring a third function online, embed governance and the operating model, and instrument adoption from Activity to Anchoring to Impact. Close with a business review on time saved and net-new capability.
- ✓Adoption measurement framework live
- ✓Operating-model and governance handbook
- ✓Quarterly business review with scale-or-stop decision
Step 01
Process and Knowledge Inventory
Inventory the processes across functions, find where manual work consumes the most time, and consolidate the scattered knowledge that AI will run on. Pick the painful, measurable problem to automate first.
- ✓Process inventory and AI potential map
- ✓Knowledge consolidation plan
- ✓Prioritized use case with the KPI defined upfront
Step 02
First Agent Live
Stand up the first agent inside the real workflow, not as a standalone bot. Apply the 80/20 rule so 20% of the build delivers 80% of the value, with a human in the loop on every customer-visible output.
- ✓One production agent wired into the live workflow
- ✓Human approval cadence on customer-facing output
- ✓Baseline measurement against the defined KPI
Step 03
Scale and Second Function
Expand the first agent to the full scope and onboard a second function. Set up the skill registry with named owners and the learning loop so capability compounds instead of resetting.
- ✓Skill registry with named owners
- ✓Second function automated and integrated
- ✓Reporting tied to one business KPI
Step 04
Compound and Measure Impact
Bring a third function online, embed governance and the operating model, and instrument adoption from Activity to Anchoring to Impact. Close with a business review on time saved and net-new capability.
- ✓Adoption measurement framework live
- ✓Operating-model and governance handbook
- ✓Quarterly business review with scale-or-stop decision
Technologies we work with
Battle-tested tools across the modern cloud-native stack
Assistants and Workflow Automation
Retail Operations Plays
Operating Model and Governance
FAQ
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More of the AI catalog
AI services that work together across Engineering, In-Product, and Business Operations. Pick what fits your next move.
Let's Talk
Put AI to Work in Your Operations
Start with one painful, measurable process. We inventory it, ship the first agent inside your live workflow within weeks, and build the operating model so every function after it gets easier. Each 90-day cycle ends with measured impact and a clear decision to scale.
Based in Düsseldorf, Germany, working with clients across Europe