LLM application development
Web and internal tools built around a language model, from the interface to the prompt and data layers.
AI & Machine Learning
Generative AI & LLM solutions
Large language models, put to work on real business tasks.
We build tools on GPT, Claude, Llama and other models that read, draft and summarise alongside your staff — scored on your own examples before anyone relies on them.
Generative AI that does a job, not a demo.
Large language models (LLMs) such as GPT, Claude, Gemini and Llama can read, write, summarise and reason over text. That makes them useful for work that used to need a person for every item: drafting replies, pulling fields out of contracts, summarising long reports, turning notes into structured records, or answering questions about your own documents.
The hard part is not calling the model — it is making the output reliable. We design the prompts, the data flow and the checks around the model, measure quality on your real examples, keep costs predictable, and choose between hosted APIs and self-hosted open models based on your privacy requirements.
Generative AI & LLM Solutions
What we offer
Document processing
Extracting structured data from invoices, contracts, forms and reports — with confidence checks and human review.
Content & drafting assistants
Tools that draft emails, descriptions, reports and replies in your tone, for a person to review and send.
Internal copilots
Assistants that help your team search, summarise and act inside the tools they already use.
Fine-tuning & model selection
Choosing the right model for cost, speed and privacy, and fine-tuning open models when prompting is not enough.
Evaluation & guardrails
Test sets, automatic quality scoring, content filters and fallbacks so output quality is measured, not assumed.
When it’s the right fit
- Your team spends hours reading, summarising or re-typing documents
- You want to add an AI feature to your product but need it to be dependable
- Customer or internal requests follow patterns a model could draft answers for
- Data privacy rules mean you cannot simply paste company data into a public chatbot
How we deliver
- 01Collect real examplesWe gather genuine inputs and the outputs your team would accept — the benchmark every version is scored against.
- 02Model & prompt designModels compared on quality, speed, cost and privacy, then prompts and output formats designed around the task.
- 03Guardrails & reviewValidation checks, content filters and a human approval step wherever a mistake would be expensive.
- 04Launch & cost trackingReleased to a first group of users, with quality scores and cost per request visible on a dashboard.
Technology
Tools we use for Generative AI & LLM Solutions
- OpenAI
- Anthropic Claude
- Llama
- Mistral
- LangChain
- LlamaIndex
- Python
- FastAPI
Frequently asked questions
Is our data safe when using an LLM?
We use API providers under business terms that exclude training on your data, or deploy open-source models on infrastructure you control when data must not leave your environment.
How do you stop the model from making things up?
We ground answers in your own data (see RAG), constrain the output format, add automatic checks and keep a human review step where mistakes are costly. Quality is measured on a test set before launch.
What does it cost to run an LLM application?
Running costs depend on usage and model choice. We estimate per-request cost during scoping and design with caching and smaller models where they are good enough, so the bill stays predictable.
Next step
Tell us what you want to build.
A free 30-minute consultation with an engineer — no obligation, reply within one business day.
Contact us