01
Faster onboarding
No need to build your own agent platform first. Initial use cases go live sooner, and the focus stays on business value rather than technology development.
02
Lower investment and technology risk
A lower initial commitment, usage-based cost models, and the freedom to replace individual agents in specific task areas without disturbing the rest.
03
Continuous optimisation
Improvement is part of the operating model rather than a build-and-forget project that ages from the day it ships.
04
Direct access to specialist expertise
AI agent architects, data and integration engineers, agent operations and monitoring, and AI governance — without hiring each of them.
05
Scalability as an agent factory
Extend to further processes, teams, countries or brands from one platform, instead of accumulating isolated pilots.
06
Always technologically current
Foundation models get replaced and new agent frameworks get integrated as part of the service, not as a migration project.
07
Clear business accountability and SLAs
Availability, performance and quality of results are committed to. An AI prototype becomes a professionally managed digital employee.
08
A real path from pilot to scale
The transition from proof of concept to production is designed in, rather than being the point where most projects stall.
What makes these projects succeed
Across our own deployments and the wider AI work our team has run, the same factors keep separating the projects that reach production from the ones that do not.
- Conceptual depth in the concept itself, rather than theoretical platitudes.
- No pyramid staffing. Senior people with relevant background and project experience, from strategy and concept through to implementation — not a thin layer of expertise over a large junior team.
- A seamless transition from concept to implementation, run as one continuous thread rather than a handover.
- Empathy and seniority to anchor the target picture in the organisation and with the committees that have to approve it, using proper change-management methods where needed.
- Benchmarks and prior experience used to structure the work, so recommendations come from practice and nobody reinvents the wheel.
- A pragmatic approach that prioritises a few questions that actually drive business outcomes, instead of boiling the ocean.
- Experienced project leads who have carried line responsibility in AI and data-driven marketing and sales, not only advisory roles.
- Referencing best practice, including the annual Marketing Tech Monitor.
Why this combination is unusual
- Building and implementing AI agents since 2016.
- Deep experience in data-driven customer interaction and MarTech projects, on both the business and IT sides.
- Independent — not tied to any other software vendor.
- Neutral analysis, with no incentive to sell a large implementation team afterwards.
- Strategy and roadmap work delivered with customers across a wide range of industries.
- Experience in both mid-sized companies and large corporations, at operational and board level.
Bring one bottleneck.
Leave with an agent.
Tell us about one real process in your operation. We come back with a concrete agent design and a cost model for running it.