We help organisations understand what is worth improving, build the useful solution and develop their people so the capability lasts.
Baltic Augment grew from the technology team inside Baltic Apprenticeships. Over several years that team built data and reporting capability, improved Salesforce and connected systems, automated repeated work and developed practical AI services for a live organisation.
The work was rarely neat. It involved legacy systems, imperfect data, people with very little spare time, and the reality that a technically good solution can still fail if nobody understands or adopts it.
Other SMEs face exactly the same problems. Baltic Augment exists to share what the team has learned, bring practical delivery capacity into an organisation, and connect the short-term improvement to the longer-term development of its people.
Technology changes faster than organisations can build capability. A consultancy can create momentum quickly, but you still need people who understand the work after the project ends.
Practical improvement now, and structured capability development over time. Most suppliers can only offer one of those, so the second one quietly becomes your problem.
Baltic Apprenticeships is our first delivery environment. We test ideas against real processes, real users and real consequences inside the group before treating them as reusable patterns. Not every internal experiment becomes a product — it means we have somewhere honest to learn what breaks.
We bring technical knowledge, delivery experience and a view of what is possible. You bring the domain knowledge: how the work really happens, which exceptions matter and what good looks like. Neither half is enough on its own.
These are not aspirations. If we break one of them on your engagement, you should say so.
A small team, which is deliberate. You will not meet a senior person at the pitch and then never see them again.
Harry leads Technology Operations at Baltic Apprenticeships across data, systems, IT, cyber and applied AI. His role here is to connect customer problems, commercial value and the wider capability an organisation needs. He leads early conversations and workshops, and makes sure the technical work stays tied to a measurable business outcome.
Jamie is Baltic's AI Engineer and the technical lead behind much of its applied AI, software and automation work. He designs and builds internal services, integrations and platforms, and turns ambiguous business problems into practical technical approaches. Here, his focus is architecture, technical delivery, quality and reusable capability.
Adam has joined as a Graduate AI Engineer. His early work is on real internal AI and automation projects, building depth in discovery, evaluation, engineering and delivery before taking on more customer-facing responsibility. He is part of a long-term investment in engineering capability, not a finished consultant we are about to bill you for.
Ben owns Baltic's entire IT and cyber security function. He joined ten years ago as an apprentice and now runs the thing end to end, which is the capability model working exactly as it is supposed to. He lives in Azure, and he is the reason we can be specific rather than vague about identity, access, Microsoft 365 and whether an environment can safely host what we are proposing to build.
Faith works across analysis, reporting and the information buried in documents, calls and feedback. She builds dashboards people actually use, and she understands the mathematics underneath them — which is what makes her good at saying when a number means what everyone thinks it means, and when it does not.
Natural photography of the actual team, ideally at work. Individual headshots can replace the initials above.
Depending on what the work needs, we can draw on colleagues across Baltic's Technology Operations function.
We have no external customer case study we are able to publish yet, and we are not going to dress up a discovery conversation as one. Here is what we have built inside our own organisation, with the assumptions shown so you can judge them.
A repeated sales task involved finding a suitable local candidate, checking travel distance, reading a CV, extracting the relevant points and writing them into a customer email. We connected candidate data, a public postcode source, AI-assisted CV analysis, Salesforce and Salesloft so that much of the preparation now happens automatically. The resulting email step became the strongest-performing step in the sequence.
Modelled on roughly 24 uses per consultant per day across 16 consultants, using internal salary assumptions. It describes capacity released, not an audited cash saving.
Coaches previously created training events, emails and individual calendar invitations by hand for every cohort. A connected workflow now takes the training dates the coach enters once and automates the downstream administration from there. It is a deliberately unglamorous example, and one of the best returns we have measured.
Based on eight hours of administration per cohort, across roughly 60 coaches and three cohorts a year. Again, capacity released rather than budget removed.
We built a central reporting and analysis capability where there had not been one. Retention analysis, early-risk measures, salary analysis, employer segmentation, caseload management and learner-risk reporting helped the business see where intervention was needed. Over the wider period, learner retention moved from the mid-60s to the high-70s.
Data was one contributor among several, and we do not claim it caused the improvement on its own. What it does prove is that metrics need ownership and an operational process behind them — a dashboard on its own changes nothing.
We redesigned parts of our prospecting data and workflow across ZoomInfo, Salesforce, Salesloft and Tableau. The clearest measured outcome was a substantial improvement in email deliverability.
The wider change also ran into adoption and operating-model problems, and was paused. We include it because that lesson — technically sound work failing on the people side — shaped how we handle handover now.
Drawn from the real workflow. Any dashboard or system screenshots must use synthetic or approved data.
We have also built an internal AI platform bringing approved models, company context, reusable services and connected tools into a more controlled environment, and explored how information inside calls, meetings and customer feedback can be analysed at a scale that would be unrealistic manually. Both have taught us a great deal about permissions, knowledge quality, model choice, evaluation and the gap between building a tool and changing behaviour.
Neither is something you can buy from us today, and we would rather say that plainly than put it on a product page.
Typically ten to a few hundred people, with enough operational volume that manual work and poor information carry a real cost.
No twelve-month programmes. No jargon. No selling you AI you do not need. Just a straight look at how the work actually happens, and a plan you can afford to act on.
You do not need a finished brief or a preferred technology. Tell us what is slow, repeated, difficult to trust or harder than it should be. We will arrange a short conversation and tell you whether we think there is a useful next step.