AI agents
Agents that handle support, sales and operations tasks across your tools.
- CRM
Services
AI development and LLM integration
We add AI where it earns its keep: answering questions from your own documents, extracting data from invoices and forms, classifying and routing requests, and building assistants into your product.
Every engagement starts with a small, measurable pilot on your real data, and moves to production only when the pilot proves its value.
AI development
What we build
Knowledge assistants (RAG)
Assistants that answer from your policies, manuals or product data, and show the source for each answer.
Document AI
Structured data extracted from invoices, purchase orders and forms, with human review for edge cases.
Classification and routing
Emails, tickets and leads sorted by intent, urgency or department automatically.
AI features in your product
Search that understands meaning, summaries, drafting assistants and recommendations.
Custom ML and computer vision
Models for prediction, detection and inspection when off-the-shelf models are not enough.
Data pipelines
The plumbing that gets clean, current data to your AI and analytics.
Your data stays yours
Configured for business use
Model provider settings chosen so your data is not used to train public models.
Least access
Each AI feature can reach only the data it needs.
Self-hosting options
Open-source models in your own cloud when data must not leave it.
Visible behaviour
Logs and evaluations so you can see what the AI did and why.
Where AI pays off, and where it doesn't
AI is worth it when a task needs judgement over unstructured input (language, documents, images) and happens often enough to matter. It is not worth it when a clear rule would do the job. We will tell you which is which before you spend money.
Process
How we work
- Step 1
Use-case workshop
Pick the one or two problems where AI can deliver measurable value.
- Step 2
Data review
What data exists, its quality, and what access is needed.
- Step 3
Pilot
A working prototype on your real data.
- Step 4
Evaluation
Measured against agreed criteria: accuracy, time saved, cost per request.
- Step 5
Production
Hardened, integrated, secured and monitored.
Technology
- Python
- OpenAI and other LLM APIs
- Open-source LLMs
- Vector search
- PyTorch
- TensorFlow
- OpenCV
Use cases
- An internal assistant answering HR and policy questions across branches
- Invoice capture feeding an accounting system
- Support tickets classified and routed before a person sees them
- Product search that understands everyday language
FAQ
Frequently asked questions
Is our data used to train AI models?
We configure model providers so business data is not used for training, and we can use self-hosted open-source models where data must stay in your environment.
Which AI model will you use?
The one that fits the task, cost and privacy needs. We often test two or three during the pilot and choose on measured results rather than brand.
How accurate will it be?
We agree target accuracy up front, measure it on your real examples during the pilot, and design human review for the cases the model is unsure about.
What does an AI pilot cost?
Pilots are scoped and fixed-price, so you know the cost before committing to a full build. We quote after the use-case workshop.
Can AI run on our own servers?
Yes, using open-source models deployed in your cloud or data centre. There are trade-offs in capability and cost, which we explain before you decide.
Planning an AI project?
Tell us what you're building. An engineer, not a salesperson, will reply within one business day with questions, an approach and next steps.