Technology & Automation
What this is
We build AI systems that do real work: knowledge assistants grounded in your own documents, document and form processing, classification and routing, summarisation, and agents that carry out multi-step tasks under supervision.
A convincing demo takes an afternoon. What takes engineering is grounding answers in your data, handling the cases where it is wrong, connecting it to your systems, and keeping quality and cost stable in production.
Grounding answers in current documents is cheaper, more accurate and far easier to keep up to date.
Automating the confident majority and routing the rest to a person is what makes deployment sensible.
A test set with an agreed measure of good is how you know whether it works, rather than whether it seems to.
Use cases
Staff searching manuals, policies and contracts for answers
High volumes of email or requests needing triage
Reports and summaries assembled by hand
Multi-step processes that follow predictable rules
A product that would be materially better with AI in it
What this is
Technology
Models
Orchestration
Delivery
Relevant work
How it runs
The task, its volume, its cost and its tolerance for error. If AI is the wrong tool, we say so.
A narrow build on real data with an agreed definition of a good result.
Retrieval, guardrails, human review steps, integration and monitoring.
Deployment, quality tracking and ongoing tuning as models and needs change.
What it should achieve
Repetitive knowledge work handled automatically
Consistent quality on high-volume tasks
Measured accuracy rather than assumed accuracy
Systems your team can operate and extend
Related capabilities
Questions
It usually needs to be repeated often, cost real time today, and tolerate occasional error with a review step. If it is rare, or must be perfect every time, conventional software is often the better answer.
Scopus assistant
Simulated — scripted, not a live model
