A Large Language Model integrated directly into your application, workflow or customer portal. Classify, summarise, search or map free-text input to structured fields, without your data leaving to a training set.
A Large Language Model — Claude, GPT-4o, Gemini, Llama — is, in itself, simply an API call. The value only comes once such a model is genuinely part of your working process: it reads your documents, classifies your incoming emails, searches your knowledge base, or maps customer input to the right fields in your CRM. That is where we come in: the integration, the context, and the monitoring around it.
We build LLM integrations as part of a broader software project or as a standalone capability within an existing application. An AI agent is a specific form within that — a multi-step system that can call tools. An LLM integration is broader and often simpler: a single well-prompted call, an embedding search with RAG, or a fine-tuned classifier running in production.
Depending on what the model needs to do, how sensitive the data is and how much custom work surrounds the model. We advise which fits during the first conversation, and always check first whether a lighter option will do.
A defined feature in your application where a single well-prompted LLM call does the work: classifying an incoming email, summarising a long document, automatically assigning a sentiment label to a review, or turning free-text customer input into structured fields in your CRM. Cloud LLM from Anthropic, OpenAI or Google. Includes monitoring of token usage and quality.
The LLM receives context drawn from your own data. We build a retrieval pipeline over your documents, knowledge base or product catalogue: embeddings, vector store, semantic search, and a chat or Q&A layer that answers only from the sources it finds. Source citations as standard, hallucinations kept in check. Suited to internal knowledge bases, customer support and product search features.
An open-source model — Llama, Mistral, or a specialised variant — running on your own cloud or on-premises. Full data control, no calls to external APIs, no uncertainty about the training set. We deploy it via vLLM, Ollama or a managed inference layer, with the associated monitoring, scaling and authorisation. More upfront work, but for compliance-sensitive sectors often the only route.
A production-ready LLM integration, plus everything needed to manage and steer it yourself on cost, quality and privacy.
Four patterns where we guide clients — if you recognise one, we'd be happy to talk further. Not every use case calls for an LLM; sometimes a classic rule engine or a specialised model is cheaper and more reliable.
Hundreds of invoices, contracts, emails or requests a day are currently read, sorted and forwarded by hand. An LLM can classify, summarise and extract the relevant fields — leaving your team time for the cases that genuinely need attention.
Employees or customers search through a growing pile of documents and fail to find what they need. Semantic search with RAG lifts search results from keyword level to meaning level, with source citations so you know where an answer comes from.
Lead forms, support requests, product specifications — users write in free text, while your CRM or ERP expects structured data. An LLM integration maps free text to the right fields, with validation and a human check where needed.
Do you work with medical, legal or financial data where GDPR and sector regulations are strict? Then a private LLM on your own infrastructure is the right route — more expensive to set up, but without the privacy trade-offs of an external API.
The applications we build most often. The common thread: a well-defined task, a measurable quality threshold and a logging layer that shows what the model did.
Automatically label incoming invoices, contracts, emails or requests and extract the relevant fields — amount, date, party, type, priority. For a high volume of incoming post, this demonstrably reduces manual work and delivers consistent data in your back office.
Condense long documents into a few paragraphs, or produce domain-specific translations that understand legal or technical jargon. Far more reliable than generic translation APIs once you give the model context about your field.
Automatically label customer reviews, NPS comments or support requests by sentiment, topic and urgency. Produces a dashboard that lets teams spot trends before complaints escalate.
A search function that understands what a user means, not just which words appear. Combines keyword search with embeddings and returns answers with source citations. A building block for both internal knowledge bases and product search features.
A customer types what they are looking for in free text, and the LLM maps it to the right fields in your CRM, ERP or order system. Includes a human check for low confidence scores so errors don't reach production.
For e-commerce catalogues with thousands of SKUs: consistent, SEO-friendly descriptions based on specifications. Tone of voice is safeguarded via a prompt library; people stay in the loop for sampling and final editing.
An LLM that generates queries or code snippets within your application, for example a reporting tool in which non-technical users can ask questions in plain language, which the model converts into safe SQL.
Stripping personal data from logs, support ticket archives or training data before they are used further. An LLM recognises patterns that regex misses: names, addresses and policy numbers in free text.
No vendor lock-in to a single model provider. For each use case we choose what fits best in terms of quality, price, latency and compliance, and we make sure that switching to a newer model later does not mean a full rebuild.
Anthropic Claude (Opus, Sonnet, Haiku) for nuanced language work and long context. OpenAI GPT-4o and o1 for broad use and complex reasoning. Google Gemini via Vertex AI for those already on Google Cloud. Azure OpenAI for those running a Microsoft stack. Cohere for specific embedding and classification work.
For scenarios where data must not leave the premises. We run Llama 3 (70B and smaller), Mistral variants and specialised fine-tunes via vLLM on GPU instances, or via Ollama for smaller models. This includes the operational layer: scaling, monitoring, authorisation and cost control.
Postgres with pgvector, Qdrant or Pinecone as the vector store, depending on scale and your existing stack. Evaluation frameworks to detect regressions when models are updated. Anthropic's prompt caching and OpenAI's cached prompts are used where sensible; a caching strategy can significantly reduce token costs in production.
A conversation in which we establish which task the model needs to perform, what data is involved, how sensitive that data is and what quality is acceptable. This is also where the first answer to the cloud-versus-private question takes shape.
We test several models (Claude Sonnet, GPT-4o, Gemini or a Llama variant) on your own examples. At the end you receive a comparison on quality, latency and cost, plus advice on which model suits which flow best.
A working build every two weeks. We build the integration into your stack, set up an evaluation suite, implement caching and cost monitoring, and configure logging so that we can prove which prompt version produced which answer. This includes any integrations with your existing systems.
A phased rollout, initially to a small group of users so that unwanted behaviour is caught before it scales. Followed by ongoing maintenance: prompts evolve, models are replaced, and your eval set grows along with them.
What clients typically want to know before we start, including the most frequently asked question: what does a private LLM cost?
A free, no-obligation half-hour introductory call. We listen to your use case, ask questions about data, compliance and quality, and give you direction you can act on, even if the eventual advice is not to deploy an LLM at all. For larger projects, we can combine this conversation with an exploration of enterprise AI implementation.
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