Service
Custom AI tools for business
We build AI-powered software fitted to your specific business — not generic chatbots or off-the-shelf tools that cover 80% of what you need and leave the rest to workarounds.
Service
We build AI-powered software fitted to your specific business — not generic chatbots or off-the-shelf tools that cover 80% of what you need and leave the rest to workarounds.
What is it
A custom AI tool is software that uses an AI model to perform a specific, defined task in your business — answering customer questions, reading and extracting data from documents, summarising information, routing requests, or augmenting a human workflow. Unlike general-purpose AI assistants, a custom tool is scoped to a narrow problem, trained on your context, connected to your systems, and built to produce reliable output rather than impressive-looking output. The difference is the difference between a tool that works in production and a demo that works in a presentation.
What we build
An AI assistant that answers customer questions using your actual documentation, product information, and knowledge base — not general internet knowledge. Handles the questions your team answers ten times a day, instantly and consistently, and routes complex or sensitive cases to a human. Reduces support volume without reducing support quality.
Tools that read incoming documents — contracts, invoices, applications, intake forms, reports — and extract structured data, flag issues, or route them automatically. Replaces hours of manual document review with a process that runs in seconds and produces consistent, auditable output.
AI tools your team uses to work faster: searching across policy documents and internal knowledge bases, summarising meeting notes, drafting standard responses, answering product questions, or generating first drafts of structured content. The tool knows your organisation's context — not just general knowledge.
Tools that take unstructured input — emails, forms, call transcripts, survey responses — and return structured, usable data: categories, entities, sentiment, decisions, flags. Feeds clean data into downstream systems without manual processing.
How we build it
The most important step — and the one most AI projects skip. We work with you to define the specific problem the AI needs to solve, the inputs it will receive, what a good output looks like, and what should happen when the AI is uncertain. A well-scoped task is the difference between a reliable tool and an unpredictable one.
We also identify where human review is needed — not every decision should be fully automated, and we will tell you honestly when it should not be.
We connect AI models — accessed via API, not trained from scratch — to your data, systems, and workflow. We own the surrounding software: the interface, the integrations, the prompt engineering, the error handling, and the fallback logic. The AI model is one component; the application that makes it reliable and useful is the work.
We use models from leading providers and design the architecture so the model can be swapped if a better or cheaper alternative becomes available — without rewriting the application.
AI tools are only as good as their performance on real input. We test against actual examples from your business — the edge cases, the ambiguous inputs, the things users actually send — before anything goes live.
We tune the system until the output is reliable enough to trust in production, and we define clear metrics for what "reliable enough" means before we start.
Why Hemlex
There is a significant difference between an AI tool that works in a controlled demonstration and one that works reliably in production, day after day, on real input from real users. We have seen both — and we build for production. That means robust error handling, clear fallbacks, monitoring, and honest conversations about what the AI can and cannot do reliably.
Every AI tool we build uses API-based models — we do not build on proprietary platforms that control your data and your costs. The model can be changed if a better option becomes available; the application we build around it is yours. We also avoid unnecessary third-party dependencies: data goes where it needs to go, and no further.
Not every problem is a good fit for AI. Some tasks are better automated with traditional code; some require human judgement that no model reliably replicates. We will tell you if AI is the wrong tool for the job — before you pay for a build, not after. When AI is the right tool, we scope it narrowly enough to do its job well.
We build AI tools using API-based models accessed over encrypted connections. The application layer — the interface, integrations, prompt logic, and error handling — is built in TypeScript and hosted on infrastructure you control. We do not use AI application platforms that introduce additional vendor dependencies; the tool is yours, the code is yours, and the model relationship is direct.
Frequently asked questions
General AI tools are broad by design — they know a little about everything and can turn their hand to most tasks, but they do not know your business, your documents, or your workflows. A custom tool is narrow by design — it knows a lot about a specific domain and is built to do one thing reliably. It is also integrated into your systems, so it can act on what it knows: update a record, route a request, generate a document in your format. General tools are good for ad-hoc work; custom tools are good for repeatable processes where consistency and reliability matter.
Not always. Some tools — support chat, document processing, internal search — work well with existing documentation and a set of real examples, without any model training. Others benefit from more data, particularly where the task involves nuanced classification or domain-specific language. We assess what you have at the start of the project and tell you honestly whether it is enough to build something reliable — and what "reliable" means for your specific use case. We do not start building until we are confident the data is sufficient.
We use API-based models from leading providers — currently including models from Anthropic and OpenAI, depending on the task. We do not build or fine-tune proprietary models; we build the application software around existing models, which is faster, cheaper, and more maintainable. The model choice is driven by what performs best on the specific task. Because we design the architecture to be model-agnostic, the model can be swapped as the landscape evolves — which it does, quickly.
It depends on the architecture, and we discuss this explicitly before we start. For most tools, input data is sent to an AI model API to generate a response — this is how API-based AI works. We minimise what is sent, avoid sending sensitive data that is not necessary for the task, and can design architectures that reduce or eliminate third-party data exposure where that is a requirement. We are based in Estonia, operate under EU law, and can sign data processing agreements as part of any engagement.
We design for failure from the start. Every tool we build has a defined fallback: a confidence threshold below which it defers to a human, a clear "I do not know" response when uncertain, or a routing rule that escalates the case. We never ship a tool that fails silently or produces wrong output with false confidence. We also build monitoring so you can see where the tool is performing well and where it is not — so problems are caught and corrected, not ignored.
In some narrow, repetitive tasks — yes, and that is often a good outcome. More often, the right framing is that an AI tool makes an employee significantly faster at the tasks that used to take most of their time, freeing them for work that requires judgement, relationships, or creativity. We scope every project honestly: we will tell you what the AI can realistically do, what the expected time saving is, and whether the return on investment justifies the build. We do not oversell what AI can do, because tools that underperform expectations damage trust in the technology and in us.
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