AI Solutions.
Assistants, automation and decision support built into the software and apps you already run. Practical AI, measured by the hours it saves and the decisions it improves.
What this covers
AI assistants and agents
Support agents, internal copilots and customer-facing assistants grounded in your own documents and data, with guardrails and a human handoff when they should not answer.
Document AI
Extraction, classification and summarisation for invoices, contracts, claims and records.
Workflow automation
Multi-step processes that used to need a person, now run with a person only where judgment is needed.
Forecasting and recommendations
Demand, churn, maintenance and next-best-action models trained on your history.
AI inside your existing software
Search, summaries, smart forms and natural-language reporting added to the systems and apps you already run, including the ones we did not build.
How we work
Workshops to find where AI actually pays: the repetitive, the document-heavy, the decisions made on gut today.
A short pilot on real data with a measurable target, before anyone commits to a build.
Integrated into your systems and workflows, with evaluation, logging and guardrails from day one.
Accuracy, cost and time saved, tracked after launch. Models and prompts improve on a cadence you set.
The stack
Model-agnostic on purpose. We pick per task, and we can run inside your cloud when the data must not leave.
Start with a four-week pilot, not a platform.
Most AI projects fail on the use case, not the model. A fixed-scope pilot on your own data tells you whether it pays before you fund the build. See how this played out for B2G Atlas, an AI platform that turns government RFPs into compliant proposal drafts.
Scope a pilotQuestions we get
Will our data be used to train someone else’s model?
No. We use enterprise API terms or run models inside your own cloud, and your data is not used for training by us or by the model provider. We put this in writing.
How do you stop the AI from making things up?
Answers are grounded in your documents with citations, evaluated against test sets before launch, and routed to a person when confidence is low. Where a wrong answer is expensive, the AI drafts and a human approves.
Do we need our own data scientists?
No. We bring the team. What we need from you is access to the data, a named owner for the workflow, and people willing to test it.
Can this work with regulated data?
Yes, with care. We build with HIPAA-aware practices, keep data in your region and your cloud where required, and sign an NDA before the first call if you need one. We have not been SOC 2 audited, and we say so rather than imply otherwise.
What does it cost to run?
Model usage is metered, so we design for cost from the start: smaller models where they are good enough, caching, and a monthly cost report alongside the accuracy numbers.
Tell us where the hours go.
We come back with the use case, the pilot plan, and what it would take.
Scope a pilot