AI & Automations
Generative AI & LLM Applications
Assistants and automations grounded in your own documents
Generative AI can draft, summarize and answer questions in seconds, but a general chatbot knows nothing about your policies, products or customers. We build assistants and workflows that answer from your own documents, show where each answer came from, and keep sensitive data where it belongs.
- Knowledge assistants (RAG)
- Document processing
- Support copilots
- Prompt and workflow design
- Evaluation and testing
- Secure deployment
Why it matters
The value is not in the model. It is in connecting it to your knowledge, putting a person in the loop where it matters, and knowing exactly where your data goes.
What this includes
Six parts of the work. Which of them you need depends on where you are starting from, and we agree that before anything is built.
- 01
Knowledge assistants (RAG)
Assistants that answer from your documents, wikis and tickets, with links back to the source of every answer.
- 02
Document processing
Extracting fields, summaries and classifications from contracts, invoices, forms and reports.
- 03
Support copilots
Draft replies, ticket summaries and routing suggestions, reviewed by an agent before anything is sent.
- 04
Prompt and workflow design
Structured prompts, tools and guardrails that make output consistent rather than a lucky first try.
- 05
Evaluation and testing
Test sets that measure accuracy and failure cases before launch, and catch regressions when anything changes.
- 06
Secure deployment
Private or business-tier model access, access controls, logging and data handling that fits your policies.
Not sure which of these you need?
Tell us where things stand for you today and we will suggest where to start.
How we deliver it
Four stages, agreed with you before the work starts, with a working result at the end of each rather than one reveal at the end.
Pick the use case
We choose one frequent, checkable task and measure how it is done today, so the improvement can be judged.
Prototype on your data
A working prototype connected to real documents, tested with the people who will use it.
Evaluate and harden
We build test sets, add guardrails and handle the edge cases the prototype exposed.
Launch and improve
A controlled rollout with usage logging and feedback, then refinement based on what people actually ask.
The stack
Technologies we work with
The tools we reach for most often in this work. We fit in with what you already run rather than asking you to change it.
3 groups
- 01 / 035
Models
- OpenAI
- Anthropic Claude
- Azure OpenAI
- Amazon Bedrock
- Open-weight models
- 02 / 034
Retrieval
- LangChain
- LlamaIndex
- pgvector
- Azure AI Search
- 03 / 033
Application
- Python
- FastAPI
- Next.js
What you receive
Yours to keep when the engagement ends, whether we carry on working together or not.
A working application
An assistant or workflow in use by a real team, connected to your own content.
An evaluation suite
Questions and expected answers that measure quality before every change goes live.
A data handling note
Where data flows, what is stored, what is logged and who can see it.
A usage policy draft
Plain guidance for staff on approved tools, permitted data and when output must be reviewed.
Common questions
The ones that come up in almost every first conversation about this work.
We use business or private deployments that do not train on your inputs, and we document exactly where data flows so your team can confirm it meets your policies.
Answers are grounded in your own documents with sources shown, tested against a set of known questions, and reviewed by a person wherever a mistake would matter.
Yes. Most assistants connect to document stores, ticketing systems or databases you already run, rather than asking you to move content somewhere new.
Let's talk about Generative AI & LLMs
A short call is usually enough to work out whether we can help and where it makes sense to start. No preparation needed.