AI Integration
AI capabilities connected to the software, data and workflows an organisation already uses, for jobs such as extraction, classification, summarisation, search and drafting, with validation and human review built in.
A chat tool on its own cannot raise a ticket, file a document or update a record in another system. A model becomes useful once it is connected to the systems where the work happens, which raises the usual engineering questions: what it can see, what it can change and what happens when it is wrong.
The system around the model
The service focuses on the engineered system around the model: data access, permissions, instructions, validation, human review where it is warranted, downstream actions, monitoring and operating behaviour. The model reads, classifies, retrieves or drafts. Surrounding software applies rules, checks the result and writes to the systems of record.
Controls should be proportionate to the consequences of error, the sensitivity of the data, the downstream actions, user expectations and the operating context. Logging, evaluation and human review are design decisions for the job, not a universal implementation promise.
Typical starting points
Documents processed by hand
Applications, invoices or forms arrive as PDFs and email, and staff type the details into a system.
Requests sorted manually
Someone reads every incoming message or ticket to decide where it goes and how urgent it is.
A pilot that cannot go further
A prototype works on sample data but has no answer for permissions, failure or review, so it cannot be used on real records.
What we do
Assess the use case
We establish whether a model is the right tool for the job, or whether conventional software or automation would do it better.
Connect the model to the organisation's systems
Hosted or self-operated models are integrated with APIs, retrieval over internal knowledge and structured outputs.
Set the boundaries
Identity, permissions and the actions a model-driven workflow is allowed to take are defined and enforced in software.
Evaluate and plan for failure
The system is tested on representative data, review steps are defined, and fallback behaviour is designed for a model or provider outage.
What you end up with
Model output inside the workflow
Extraction, classification, search or drafting that lands in the system where the work happens, after validation.
Controls matched to the risk
Review steps, logging and limits on downstream actions chosen for the consequences of a wrong answer.
An evaluation on representative examples
A record of how the system performed on real material, so decisions about wider use rest on evidence.
How a typical engagement works
01Use case
Confirm the use case
We define the job, the data it needs and what a good result looks like.
02Controls
Design access and controls
Data access, permissions, logging and review steps are designed around the cost of an error.
03Build and evaluate
The integration is built and tested against representative examples from the organisation's own records.
04Run and monitor
Run, monitor and hand over
Monitoring, fallback behaviour and documentation are put in place before the system is relied on.
05Review
Review on real use
Quality and controls are reviewed on real use, and the evaluation is repeated when the model, provider or data changes.
Frequently Asked Questions
Start with a conversation
Book a free 15 minute call. Describe the problem, the systems involved and what you already know, and we will tell you plainly whether we can help and what a sensible next step looks like. There is no obligation.