Production AI, end to end.
Design. Build. Run. The same team for every step.
We built our company on AI. The same people decide what to build, build it, and run it. No strategy deck handed off to an execution team. No half-built pilots. We do the same for companies that can move quickly, whether they are 80 people or 8,000.
Three ways to plug us
into your business.
Each model exists for a different stage of your AI program. We will tell you which fits, or that you do not need us yet.
Agents as a Service
We design, build, and run production AI agents in your workflows. You consume the output.
An inbox triage agent handling 12,000 emails a month at a professional services firm. Updated weekly.
AI-Organization as a Service
We stand up the full AI capability. Infrastructure, governance, first use cases, the team to run it. You exit with an AI function you can operate without us.
A wealth manager replacing a fragmented vendor stack with an internal AI team and a sequenced roadmap of 15 use cases.
External AI-Officer
Senior AI leadership, fractional. Two to four days a month. For companies that need the judgment but are not ready to hire the role.
Vendor selection, board reporting, and the call on whether to build, buy, or wait.
Most AI roadmaps are a list.
Ours is a dependency graph.
The standard AI consulting deliverable is a prioritized list of use cases. It is wrong. Use cases share infrastructure: clean data pipelines, evals, retrieval, observability. Built in the wrong order, you rebuild foundations five times. Built in the right order, the sixth use case takes a week.
We sort everything into three buckets. Infrastructure Enablers. Quick Wins. Complex Dependencies. The order matters more than the list.
What we have shipped.
An AI invoice editor that the people doing the work actually use.
A 12-month sequenced AI program for a €200M+ AuM firm.
From 25+ AI ideas to three sequenced production agents.
We also build software.
Some started as client work. Some as side projects. All run in production. Each has its own site.
[Invoice Agent]
[Inbox Agent]
Talk to us before
you commit to a vendor.
In thirty minutes we will tell you whether your AI roadmap is sequenced correctly. Often that saves three months of misspent work.
Most AI roadmaps are a list. Ours is a dependency graph.
The order you build things in determines whether AI compounds or stays a set of expensive demos.
The list is the problem.
Walk into any company that has commissioned an AI strategy and you will find the same artifact. A grid of 20 to 50 use cases, scored on value and complexity, color coded by quarter. It looks rigorous. It is wrong.
The list assumes each use case is independent. In production, nothing is. The third use case shares 60 percent of its infrastructure with the first one. The seventh one cannot exist without a data pipeline that the fourth one quietly built. If you pick from the list in the wrong order, you rebuild the same foundation five times and call the result "iteration."
There is a better question to ask. Not what should we build first? but what builds the conditions for everything else to compound?
Three buckets. The order matters.
Three tiers. Each one has a different job.
Infrastructure Enablers
The foundations that unlock everything else. Data pipelines, retrieval layers, evals, observability, governance, identity. Boring. Underfunded. Almost never on the roadmap that arrives from the strategy phase. They get built early, or they get rebuilt repeatedly.
Quick Wins
Small, visible, trust-building. Run in parallel with the infrastructure work. Their job is not operational savings. It is proof that the team can ship and that the foundation pays back.
Complex Dependencies
The high-value use cases that show up on every AI strategy slide. Customer-facing agents. Cross-functional copilots. Underwriting assistants. They only become tractable once the first two tiers exist. Attempted before, they fail expensively and get blamed on "the technology."
Four phases. Specific timelines.
No assessment-as-a-deliverable. No 90-day strategy phase that ends with a slide pack. Every phase ends in a build decision.
Diagnostic
We map your current state, score use cases against the framework, and deliver a 12-month sequenced roadmap. The diagnostic ends with a build decision, not a deck.
First production agent
One Quick Win shipped to production while Infrastructure work starts in parallel. The first agent matters more for what it proves than what it saves.
Foundation
Data, evals, observability, governance. More agents shipped on top of the foundation. By the end, the cost of the next use case drops by an order of magnitude.
Compounding
Your team owns the platform. We add velocity and ship the harder use cases. The engagement ends. If we did our job, you do not need us after twelve months.
Built with your team, not for it.
We work inside your tools. Your repo, your wiki, your environment. We document obsessively. The handover is the deliverable, not an afterthought.
This is the part most AI vendors avoid. Their incentive is to stay. Ours is to leave on time, with your team running what we built, so you call us back for the next thing instead of being stuck with the last one.
What we do not do.
We do not sell strategy decks as the deliverable.
We do not take percentage-of-savings commercials. They anchor the wrong incentive.
We do not lock you into our infrastructure. Everything is portable.
We do not ship use cases we would not run ourselves.
We do not work with companies that need six months to approve a six-week project.
We do not sell per-seat pricing. Volume-based or nothing.
Book the diagnostic call.
Thirty minutes. We will tell you what we would sequence first and why. If we are not the right team, we will tell you that too.
Three ways to work with us.
Pick by where you are in your AI program, not by what you can afford. We will tell you if you are choosing wrong.
Agents as a Service.
We design, build, and operate production AI agents inside your workflows. You consume the output. We absorb the build, the updates, and the operating overhead.
AI-Organization as a Service.
We design and stand up the AI capability. Infrastructure, governance, the first use cases, the team to run it. The full Foundational program from week one to handover.
External AI-Officer.
Senior AI leadership, fractional. Two to four days a month. The role of a Head of AI, before you are ready to hire one.
Not sure which one fits?
A 30-minute call will usually tell both sides. We are a fit for companies where the person we are talking to can say yes within two or three conversations. If your AI program runs through a six-month procurement cycle, we are not the right team.
What we have shipped.
Specific outcomes, real timelines. Names withheld where required. Numbers are real.
Patent attorney assistants spent hours every week chasing the same invoicing exceptions.
One of Europe's larger IP law firms. Like most professional services firms at scale, the bottleneck was not the lawyers. It was the patent attorney assistants who handled billing across thousands of active matters. Every invoice needed to be checked against fee schedules, jurisdictional rules, and the actual work logged. Most of it was mechanical. None of it was easy to automate with off-the-shelf tools because the systems were legacy and the rules were specific.
We built a three-phase AI invoice editor. Phase one mapped the process by shadowing assistants, not interviewing them. Phase two delivered an editor that drafts invoices from raw activity data and routes exceptions to the right reviewer. Phase three layered an intelligence module that gets sharper with every correction the assistants make.
It runs on the firm's existing Oracle infrastructure. No cloud lift. No data leaving the EU. Humans keep the final call on every invoice that goes out the door.
25 candidate use cases. No foundation. Three competing vendor pitches.
A German wealth manager with 3,000 portfolios under management. Like most firms in the segment, no shortage of AI ambition. A Microsoft Fabric data platform partially built. A Big 4 firm proposing a "transformation." A US digital consulting partner with a deck. What they did not have was a sequencing answer.
We ran a structured deep dive across lead generation, onboarding, portfolio management, and compliance. Scored every candidate use case against the Foundational framework. Identified which infrastructure had to come first. Rebuilt the roadmap around dependencies rather than the original wish list.
The result was a 12-month sequenced program, three named pilots, and a clear position on what the firm should build internally versus buy externally. Pilot one is live within 8 weeks of contract signature.
A €200M+ business with AI scattered across every function and no operating model holding it together.
One of Europe's leading personalized-goods ecommerce companies. Multiple teams experimenting with AI. None sharing infrastructure. None sharing evals. The CEO knew the next eighteen months were defining and wanted a partner who would not need eighteen months to start.
We ran a structured workshop in Munich, mapped every active and proposed AI initiative against the Foundational framework, and produced a prioritized MVP scope within six weeks. The first three production agents are now in delivery, sequenced so each one builds the infrastructure the next one needs.
Procurement automation
15 percent procurement spend reduction at a Mittelstand packaging producer through agent-assisted purchasing workflows.
Knowledge management chatbot
An engineering knowledge bot covering decades of technical documentation. 300+ hours saved per day across a 350-person organization.
Use case prioritization & rollout
From 12 candidate use cases to three prioritized rollouts that delivered a 35 percent efficiency gain on the targeted processes.
Continuous agent operations
47 production agent updates in 18 months. A live demonstration that AI agents are a continuous practice, not a launch event.
Things we have built.
Some started as client work. Others started as itches. They live here. Each has its own site.
[Invoice Agent]
Built for one of Europe's larger IP law firms. Generalized for any service business with complex, high-volume invoicing. Drafts invoices from raw activity data. Routes exceptions to the right reviewer. Learns from every correction. Runs on legacy systems. Ships with audit-grade controls.
[Inbox Agent]
Built for high-volume professional inboxes where context matters more than keywords. Triages incoming mail by case. Drafts replies grounded in case context. Escalates edge cases to humans. Integrates with the case management systems law firms, agencies, and consultancies use.
Want something like this?
That is what the consulting side is for. The same team that builds these, builds for clients. The only difference is the contract.
Operators who ship AI in production.
A small team. No partners-of-partners. Everyone you meet on the sales call works on your account.
Who's building this.
Mitul Jain
Cristian Martin
Ehsaan Qadir
Burkard Schemmel
Hassen
Alex Hagemann
How we got here.
We started in DACH as Rodenberg Partners, working with Mittelstand operators and scaleups who wanted AI to actually ship. The pattern repeated. A €300M family business and a €60M Series C had the same problem. They could decide quickly. They just did not know what to build first or what to build it on.
That pattern is not regional. Kognitiv is what we look like now. Same people. Same answer to the same question. Fewer geographic assumptions.
What we believe.
Ready to talk?
Thirty minutes. We will tell you what we would sequence first, what the first agent should be, and whether we are the right team for what you need.