AI assistants & RAG systems
Chatbots and copilots grounded in your documents, databases and systems. They answer with citations, admit when they don't know and respect who is allowed to see what.
- RAG
- Chatbots
- Vector search
- Citations
Automate / AI, ML & workflow automation
AI that knows
your business.
Assistants grounded in your own data, automations that run whole workflows and private models that keep sensitive information in-house, designed and delivered by senior AI and ML engineers.
- GroundedAnswers cited from your own data
- End to endWorkflows that act, not just answer
- PrivateLocal models, data stays in-house
01 / The gap
Your knowledge is everywhere.
Your answers aren't.
Policies sit in PDFs, decisions in email and data across a dozen systems. A useful AI layer connects them, answers with evidence and acts on the result, without exposing what should stay private.
Where AI initiatives stall
01
Generic chatbots
Confident answers with no sources, drawn from the internet rather than from how your business actually works.
02
Pilots that never ship
An impressive demo with no integrations, no evaluation and no owner once the workshop ends.
03
Data that can't leave
Compliance rules out public AI services, so the most valuable use cases never get started.
We design for all three from the first conversation.
02 / What we build
Three ways AI
earns its keep.
Knowledge assistants, workflow automation and private models. Used alone or together, each is built around your data, your systems and your rules.
Agentic workflow automation
Workflows and agents that read, decide and act across your tools, from intake to update, with human approval built in wherever judgement matters.
- AI agents
- n8n
- Integrations
- Human-in-the-loop
Local & private LLMs
Open-weight models running on your servers or private cloud, so prompts, documents and answers never leave your environment.
- On-premise
- Private cloud
- Open-weight models
- Air-gap ready
Also in scope
- Document extraction
- Classification & routing
- Summaries & reporting
- Voice & chat channels
- Evaluation & monitoring
- Fine-tuning
03 / Private by design
Your data stays.
The model comes to it.
Not every workload can touch a public AI service. We deploy open-weight models on your own servers or private cloud, and help you match the right model and setup to each use case.
Choose a deployment
Best for
Regulated and highly sensitive data, air-gapped sites, hospital systems and pharma R&D.
Sensitive data that needs elastic scale, pinned to the region your compliance requires.
Lower-sensitivity workflows that benefit from frontier reasoning, with redaction before anything leaves.
Data control
Speed to deploy
Model capability
What we handle
- GPU sizing
- Model selection
- vLLM serving
- Monitoring
- VPC setup
- Region pinning
- IAM & keys
- Autoscaling
- Zero-retention terms
- PII redaction
- AI gateway
- Usage logging
04 / Use cases
Where automation
pays off first.
We go deepest in healthcare, life sciences and education, where our team brings real domain experience and where the right automation frees skilled people for the work only they can do.
Pharma, biotech and hospitals
Built by people who have worked inside pharma and hospitals.
Our AI lead brings hands-on domain experience from pharmaceutical and hospital environments, so we understand safety reporting, quality systems and clinical workflow before the first line of code.
- Pharmacovigilance
- GxP & quality
- Clinical workflows
- HIPAA & GDPR aware
Adverse event intake
Reports arriving by email, web form and call notes are read, structured and coded, so safety teams start from a drafted case instead of an inbox.
- Inbox
- Extract & code
- Seriousness triage
- Draft case
Human checkpointA safety reviewer confirms every case
SOP and GxP knowledge assistant
Staff ask in plain language and get answers drawn from current SOPs, validation records and guidance, each with the source cited.
- Question
- Search in-force SOPs
- Cited answer
Human checkpointOnly approved, current documents are searched
Discharge summary drafting
Notes, results and medication changes are compiled into a structured draft, giving clinicians a summary to review rather than a blank page.
- EHR notes
- Summarise
- Structure
- Draft ready
Human checkpointA clinician reviews and signs every summary
Enquiry and referral triage
Enquiries and referrals are classified by urgency and specialty, answered where routine and routed to the right team with context attached.
- Enquiry
- Classify urgency
- Answer or route
- Team queue
Human checkpointClinical questions always reach staff
Universities, schools and training
Give educators time back without lowering the standard.
From the first enquiry to student support, the right automation takes routine work off staff while keeping academic judgement firmly with people.
- Admissions
- Teaching & assessment
- Student success
- FERPA & GDPR aware
Admissions and enquiry assistant
Applicants get accurate, instant answers on courses, fees, deadlines and entry requirements, and qualified leads reach admissions with full context.
- Web & WhatsApp
- Answer from prospectus
- Qualify
- CRM
Human checkpointEdge cases hand off to an advisor
Rubric-aligned feedback drafting
Submissions are assessed against your rubric to draft consistent, specific feedback that educators refine and release.
- Submission
- Rubric check
- Draft feedback
- Educator review
Human checkpointGrades are always set by educators
Course-grounded study assistant
A tutor that answers only from your course materials, lectures and readings, available around the clock inside your learning platform.
- Student question
- Search course content
- Cited answer
Human checkpointRestricted to approved course material
Early-alert student support
Attendance, LMS activity and submission patterns are monitored to flag students who need support before they fall behind.
- LMS & attendance
- Risk signals
- Alert tutor
- Outreach logged
Human checkpointTutors decide every intervention
Every organisation is different. These are starting points. Each engagement begins with your processes, your data and your constraints.
Discuss your use case05 / How we deliver
Pilot fast. Prove it.
Then scale.
Every engagement starts small, on your real data, with success measured against criteria agreed upfront. What works moves to production. What doesn't gets cut early.
The toolkit
n8nSelf-hostable orchestration across hundreds of apps
LangGraphAgents with controlled, auditable steps
Claude & GPTFrontier reasoning where the data allows
Llama, Mistral, QwenOpen-weight models for private deployment
pgvector & QdrantFast semantic search over your content
vLLM & OllamaEfficient model serving on your hardware
The guardrails
- Answers with sourcesEvery response cites the documents it drew on.
- Permission-aware retrievalPeople only ever see what their access allows.
- Humans approve what mattersSign-off steps for decisions with real consequences.
- Measured, not assumedTest sets for accuracy and safety before launch, monitoring after.
- Sensitive data handled properlyRedaction, audit logs and retention rules you control.
06 / Questions
Straight answers.
What operations, IT and compliance leaders usually ask before we start.
Can the AI work with our existing systems and documents?
Yes. We connect to where your knowledge already lives, such as SharePoint, Google Drive, Confluence, databases, CRMs, ERPs, EHRs and learning platforms, and index it in place with the same permissions your people already have.
How do you stop the AI making things up?
Answers are generated only from retrieved sources and cite them. When nothing relevant is found the assistant says so instead of guessing. We test every build against a question set drawn from your real work, and keep humans in the loop wherever a decision carries consequences.
Do we have to send our data to OpenAI or other providers?
No. We can run open-weight models entirely on your servers or in your private cloud, so prompts, documents and outputs never leave your environment. Where an external model is the right fit, we use enterprise agreements with zero data retention and redact sensitive fields first.
Is a local LLM as capable as ChatGPT or Claude?
For focused, well-grounded work such as answering questions from your documents, extracting data and classifying requests, modern open-weight models perform very well. For complex open-ended reasoning, frontier models still lead, so we often recommend a hybrid that keeps sensitive workloads local.
What infrastructure does a private LLM need?
It depends on the model size, the number of users and response-time targets. A single GPU server can support a department, while larger rollouts run on clustered GPUs or a private cloud. We size it during discovery so you invest in what the use case actually needs.
Is this suitable for regulated healthcare and pharma environments?
Yes. We design around HIPAA, GDPR and GxP expectations with private deployment, access controls, audit trails, validation documentation and human sign-off on clinical or safety decisions, working alongside your quality and compliance teams.
How quickly can we see a working pilot?
A focused pilot on your real data typically runs in weeks, not quarters. We agree the success criteria before we start, so the decision to scale is based on measured results rather than a demo.
07 / Start automating
Taking on new pilots Have a process
worth automating?
Tell us the workflow, the data it touches and where it slows you down. We'll come back with a pilot plan, the right deployment model and a realistic view of the return.
Senior AI & ML engineersThe people who scope it are the people who build it.
Private by defaultYour data stays where your policies say it must.
Measured outcomesSuccess criteria agreed before any build starts.