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The AI projects we have seen stall did not fail because of the model.
They stalled because nobody had clearly established which decisions could be automated with the data actually available. So that is where we start — one clearly defined decision, tested on your own data, in your own environment, with an output your board and your regulator can read.
Ten years of practice, and where these products came from
Onshore Outsourcing has been building with artificial intelligence and machine learning in client delivery since 2016 — a decade, and nine years before this initiative existed. It began as hands-on work inside real systems, not as a practice line added when the category became fashionable.
What that decade actually was
- AI and machine-learning research. Internal capability built around AI, machine learning and emerging automation.
- Conversational AI for contact centers. Real-time transcription, sentiment analysis, natural-language processing and agent assist.
- Digital customer experience. Omnichannel support, self-service and digital engagement.
- Knowledge automation. AI-driven knowledge recommendation and workflow assistance for the people doing the work.
- An innovation and product program. Internal prototypes, and the product culture that came out of them.
That is where the products on this site come from. LightHouse, DUSTy AI, the voice work and the data-readiness assessment are not additions to an AI wave. They are what a decade of building this kind of system turned into. And the contact-center line in particular is the direct ancestor of what we would deploy here — transcription, sentiment, agent assist and knowledge recommendation are the same problems, in French.
Ten years is not a credential on its own. What it bought is the argument at the top of this page. We have watched enough programs stall to know they rarely fail on the model — they fail because nothing remembers why the last decision was taken. That is a conclusion from a decade of delivery, not a position taken from the outside.
This is the parent company’s record — Onshore Technology Services. Onshore Africa is its initiative, and this is the method it brings to this market.
Viability before build
Before anything is built, the question is whether it can be. What data exists, what state it is in, which decisions are actually candidates for automation.
The output is a decision named in your own measure: clearance time at the terminal, first-contact resolution on the desk, the fraud rate on a payment flow, uptime across a fleet. The data behind it is identified, and the gaps are stated plainly.
Agentic automation
Agents that carry out multi-step work inside your systems, with the boundary of their authority written down: what they may decide, and what they must escalate.
Onshore Outsourcing built OrderPerfect.ai’s ordering platform on generative AI — natural-language ordering across phone, email and text. A retrieval step makes the model answer from the client’s own catalog rather than inventing. Telephony and point-of-sale are integrated, and latency is tuned for real conversation. Onshore Outsourcing publishes the results in its case-study repository: a 30% increase in phone orders captured, a 23% increase in upsells, and a lower operational cost of taking orders. The engagement is Onshore Outsourcing’s, delivered by U.S.-based teams.
LightHouse — the Context Graph
Every decision is captured as it is taken — who took it, why, what was considered instead, and what happened as a result. Nobody has to write any of it down. Every knowledge program we have watched fail, failed at that same point, because it depended on busy people documenting things afterwards.