Service
AI that takes hold, not a pilot that ends.
Most AI projects don't fail because of the technology. They fail because nobody owns the process, the data isn't ready and nobody was trained to carry on. Organisational enablement is the work that makes it hold after we leave.
Strategy, ownership and training.
We start from the question of where AI genuinely fits in the organisation, and where it doesn't. From there we build a plan that starts with one measurable process, define who owns each process inside the organisation, and train the teams to work with what was built, judge its output and keep improving it.
- AI strategy and a process map ranked by return and feasibility
- Usage, security and permissions policy
- Practical training for teams and managers, not theory
- Building internal ownership, so you can carry on without us
Because a mandate from the top produces compliance, not use.
Organisations that mandated AI adoption got performative compliance, shallow usage and abandonment the moment the pressure came off. What works is starting with one process where the team sees a result, keeping judgment with the people who know the business, and measuring how often the output was checked and improved, not how many people opened the tool.
- Small winsEvery stage produces a visible result and buys the next one.
- Human judgmentAI multiplies whoever can judge its output. So the people come first.
- Internal ownershipWhen the engagement ends, someone on your side owns it.
Five steps, and each one earns the next.
- Diagnose
We go inside one department and pick the process that pays for itself fastest. And when there isn't one, we say so.
- Data, permissions & integration
We map what data exists across the ERP, the CRM and whatever comes back from the markets, at what quality, and who is allowed to reach it. That decides whether this is an eight-week build or an eight-month one, and we tell you before we start.
- Implement
We build the solution into the existing systems and run it with the real team, on the volume they handle every day.
- Prove
One metric, agreed before we start, measured against the day-one baseline.
- Expand, embed & monitor
Only once the number is in. The next process, with training and internal ownership.
- Permissions mapped before implementation
- No training on your data, and that is in writing
- Identifying information masked before anything reaches a model
- Data residency to your requirement, including GDPR
- A full log of what was sent, what came back and who approved it
Related
- Process automationOrder entry, CRM updates, quotes and reports that someone copies by hand. We take one process that costs you hours every week and rebuild it inside the systems you already run.
- Internal assistantsThe answer already exists somewhere: in a procedure, a spec sheet, a customer file or a veteran's head. An internal assistant connects that knowledge to your teams, answers in plain language and shows where the answer came from.
- A 56% lift in lead conversion at a medical equipment company.Case study. A company with a large distributor business and an early direct-to-customer channel, which wanted to reach the next stage without breaking what already worked. A project Shahar Peer led before founding Pele.
- Who are you in this scene?We got the casting of The Matrix wrong, and the mistake explains why most companies use AI badly.
- The project isn't the AI.What happened at Cox, and why 75% of the work went where nobody budgets.
Tell us the process that costs you the most hours.
Twenty minutes with both of us. You'll leave knowing whether AI belongs in that process, including when the answer is no.