An AI strategy is only finished when it runs in production.
We run enterprise AI programmes from the first use-case shortlist to agents working inside your systems, and then we operate them. Every use case gets a written architecture and one success metric before the build starts. Every two weeks you see something working, not a status report.
The problem
Pilots that never reach the P&L
Enterprise AI usually starts as a set of proofs of concept, owned by different teams, built on different tools, with no agreed way to tell which one worked. Later, IT is asked to support something it never approved, and nobody can say what it changed.
- Use cases picked by enthusiasm, not by value, feasibility and risk.
- Pilots built outside your identity, security and change control.
- No baseline, so no result anyone can defend to the board.
- Your prompts, data and logic locked inside a vendor's platform.
What we deliver
Six parts of an AI programme that ends in running systems
A ranked use-case shortlist.
We walk your processes with the people who run them and rank candidate use cases by value, feasibility and risk, against the data and systems you actually have.
A written architecture per use case.
Paid discovery produces an architecture you could take to any vendor: the systems involved, the access it needs, the model choices and the failure modes.
One success metric, agreed up front.
Each use case has one measurable target before build starts, so the result after launch is a measurement, not an opinion.
Delivery in two-week increments.
Each increment ends in something your team can click and test, built inside your stack rather than in a sandbox.
Evaluation and controls from day one.
Changes are tested before they ship and real conversations are scored after. Permissions are scoped to the job, and every action is logged.
Operation, then hand-over.
An optional monthly retainer covers operation, monitoring and model changes. When your team is ready to take over, we set the same tooling up in your repository, and we can train them as well.
How it works
Build, launch, operate. You own the code.
- 01
Discover.
A paid discovery maps the process, ranks the use cases and produces the written architecture and the success metric for the first one.
- 02
Build.
Two-week increments, each ending in a working demo on your systems. Tests and documentation are written alongside the code, not at the end.
- 03
Launch and evaluate.
The agent goes live behind the controls agreed in discovery. Every real conversation or task is scored, and problems go into a backlog of fixes.
- 04
Operate or hand over.
We run it on a monthly retainer, or hand it to your team. Either way the source code sits in a repository you own, on your own cloud and API accounts.
From production
What we already run
We operate the systems we build. The same discovery, delivery and evaluation method is behind every page of our portfolio.
- Voice agents we built and operate in GWC Logistics' contact centre in Qatar
- 4Voice agents we built and operate in GWC Logistics' contact centre in Qatar
- Bilingual systems with true right-to-left interfaces
- EN · ARBilingual systems with true right-to-left interfaces
- Length of each delivery increment, each ending in a working demo
- 2 weeksLength of each delivery increment, each ending in a working demo
- Where the source code lives, on your own cloud and API accounts
- Your repoWhere the source code lives, on your own cloud and API accounts
Why us
We build what we recommend, then we run it.
Most AI programmes stop at a roadmap. Ours end with agents in production and a team that knows how to keep them running. We are a build-and-operate partner based in Prague, working across the GCC and Europe in English and Arabic.
Best for
- ›CIOs with a pilot portfolio and nothing in production. One programme, one architecture standard, one way to measure.
- ›COOs who need a number, not a demo. One success metric per use case, agreed before anything is built.
- ›Heads of CX facing volume they can't staff. Agents that answer and hand over, scored on every conversation.
- ›IT teams that will have to own the result. Code in your repository, deployed in your estate, documented as it is written.
FAQ
- How is this different from an AI strategy engagement?
- A strategy engagement ends with a document. Ours goes on to running software. The discovery phase does produce a written architecture and a ranked use-case list, but the build starts straight after, in two-week increments, and we can operate what we build.
- How do you choose the first use case?
- We rank candidates by value, feasibility and risk against the data and systems you already have. The first one is usually the one with a clear metric, a contained risk and an owner on your side, not the most ambitious one.
- Do we have to use a particular cloud or model?
- No. We are model-agnostic: the model provider is a configuration value, not an architectural commitment. We deploy on your own cloud and API accounts, including your Microsoft Azure estate if that is your standard.
- Who owns what you build?
- You do. Source code goes into a repository you own and runs on your accounts. There is no per-seat licence on what we build, and adding capability later is a change in your repository.
- How is it priced?
- Fixed scope at a fixed price, not open-ended hourly billing. A paid discovery comes first, then a build phase quoted against its architecture, then an optional monthly retainer for operation, monitoring and model changes.
- Can you train our teams as well?
- Yes. Through the AIEscalate Academy we run in-house programmes built on your own workflows, tools and data, so your people can extend the systems after hand-over.
Start with one use case that has a number attached.
Book a free consult. Bring the process you most want to change, and we will tell you whether AI should be part of the answer, what we would build first, and how we would measure it.