Custom ML models
Classification, forecasting and scoring models trained on your own data and measured against a baseline you can see.
Typical deliverable: versioned model, evaluation report and an inference API.
We design, train and integrate machine-learning and LLM systems that run inside your real workflows, with evaluation, monitoring and handover built in from the first week. Since 2004, KB Solutions has delivered software for 4,800+ clients across the USA, UK, Germany, Saudi Arabia and Malaysia.
Every engagement ends with something running in your stack, not a slide deck. Here is what that usually looks like.
Classification, forecasting and scoring models trained on your own data and measured against a baseline you can see.
GPT, Claude and open-source models connected to your documents and systems, with retrieval, guardrails and cited sources.
Assistants for customers and internal teams, plus pipelines that read PDFs, emails and forms into structured data.
Detection, classification and OCR for photos, video and scanned documents, in the cloud or on edge devices.
Agents and rules that triage, route, draft and summarise inside the tools your team already uses every day.
Demand, churn and risk forecasts, plus recommendation engines for stores, catalogues and content platforms.
You decide whether to continue at the end of each step, based on results on your own data.
We map the workflow, inspect the data you actually have, and agree on a measurable target with a clear go/no-go criterion.
A working model or LLM pipeline on real samples, with an evaluation set you help define, so the decision to continue rests on evidence.
APIs, batch jobs or UI integrated into your stack, with logging, evaluation dashboards, cost controls and safe fallbacks.
Documentation, sessions for your team, retraining schedules and optional ongoing support if you want us to keep it running.
The best AI projects start with a repetitive, well-defined task. These are the ones we are asked about most.
Ground replies in your help centre, policies and past tickets, cite the source, and hand off to a human whenever confidence is low.
Turn PDFs, scans and email attachments into structured records that flow into your ERP or accounting system, with a review step for exceptions.
Spot unusual patterns early, explain why they were flagged, and route them to the right team before they turn into incidents.
Suggest products and content from behaviour and catalogue data, with business rules for stock, margin and seasonality built in.
We pick the simplest stack that meets your accuracy, cost and hosting requirements, and we work inside your cloud accounts.
Tell us what you want AI to do and what data you have. We reply within one business day with an honest read on feasibility and a suggested first step.
A few lines are enough. We will ask the detailed questions on the call.
Wherever possible we build inside your own cloud accounts and environments, so training data and documents never leave your control. When a third-party model API is the right choice we say so up front, use business or enterprise tiers whose terms exclude training on your data, and can route sensitive fields through redaction or self-hosted open-source models instead. Access, retention and deletion rules are written into the scope before any data is shared.
Not always. LLM-based assistants, document extraction and many automation workflows work well with pre-trained models plus your documents and business rules. Custom predictive models do need historical data, and the discovery week exists precisely to check what you have, what quality it is in, and whether it is enough before you commit to a build.
We start with a fixed-scope discovery phase so you know the price of the first step before anything begins. At the end of discovery you receive a written quote for the prototype and, later, for the production build, each based on the actual data, integrations and hosting involved rather than a generic rate card. Running costs for model APIs and infrastructure are estimated separately and shown to you clearly.
Discovery typically takes about a week and a prototype on your data two to three weeks. Production timelines depend on integrations, security review and how much of your existing system needs to change; we give you a schedule with the quote and report progress against it every week.
Yes, and we prefer it. Your team knows the product and the data; we bring the ML and LLM engineering. We work in your repositories and ticketing tools, follow your code review process, and document decisions so your developers can own the system after handover.
Yes. Every build ships with monitoring, evaluation dashboards and runbooks. After handover you can choose a support arrangement covering model retraining, prompt and evaluation updates, dependency upgrades and cost reviews, or have your own team run it with us available on request.
One short form, one business day, and a clear recommendation on where to begin.